CN117577327A - Physical examination information recommendation system based on big data - Google Patents
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Abstract
The invention relates to a physical examination information recommendation system based on big data, in particular to the technical field of medical treatment, which comprises an information acquisition module, a first analysis module, an adjustment module, a second analysis module, an optimization module, a recommendation module and a feedback module, wherein the information acquisition module is used for acquiring historical physical examination project data, blood sugar data and recommended physical examination projects of a current user, the first analysis module is used for carrying out first abnormal analysis on the current user, the adjustment module is used for adjusting the process of the first abnormal analysis on the current user, the second analysis module is used for carrying out second abnormal analysis on the current user, the optimization module is used for optimizing the process of the second abnormal analysis on the current user, the recommendation module is used for recommending physical examination projects and physical examination time for the current user, and the recommendation process of next physical examination time is corrected according to the number of physical examination data index abnormality of the current physical examination of the current user.
Description
Technical Field
The invention relates to the technical field of medical treatment, in particular to a physical examination information recommendation system based on big data.
Background
Along with the development of technology, big data technology is widely applied in various fields. In the field of medical health, most of physical examination information recommendation systems in the current market still stay in the traditional manual recommendation stage, and the advantages of big data cannot be fully utilized, so that the recommended physical examination items and results lack pertinence and accuracy.
Chinese patent publication No.: CN111341446a discloses a personalized physical examination package recommending method, which specifically comprises the following steps: constructing a key value pair, a problem library, a common disease library, a minimum set of physical examination packages, a maximum set of physical examination packages and a physical examination person classification mechanism of common diseases and risk factors; classifying the physical examination persons according to a physical examination person classification mechanism, acquiring personal health information and physical examination budget of the physical examination persons, and forming physical examination packages for the physical examination persons according to the physical examination budget; quantifying the personal health information into disease risk factors of physical examination persons according to key value pairs of common disease and risk factors; predicting the abnormal probability of the individual related physical examination items according to the disease risk factors of the physical examination persons and the common disease list of each problem in the problem library, and calculating the actual cost of the individual related physical examination items; adding physical examination items to the minimum set of physical examination packages according to physical examination item adding rules to form physical examination packages and recommending physical examination persons; therefore, when the physical examination package is recommended to the user, the method only considers the historical physical examination information of the user and the living habit of the patient, and when the physical examination information is recommended to the diabetic, the problems of low accuracy and low efficiency of the physical examination information recommendation exist.
Disclosure of Invention
Therefore, the invention provides a physical examination information recommendation system based on big data, which is used for solving the problems of low accuracy and low efficiency of physical examination information recommendation in the prior art.
In order to achieve the above object, the present invention provides a physical examination information recommendation system based on big data, the system comprising,
the information acquisition module is used for acquiring historical physical examination project data, blood sugar data and recommended physical examination projects of the current user;
the first analysis module is used for carrying out abnormal blood sugar early warning according to the blood sugar value of the current user acquired in the monitoring period and carrying out first abnormal analysis on the current user according to the blood sugar early warning result;
the adjusting module is used for carrying out primary adjustment on the process of the first abnormality analysis of the current user according to the acquired detection time of the current user in the monitoring period and carrying out secondary adjustment on the process of the first abnormality analysis of the current user according to the number of times that the current user is not detected in the monitoring period;
the second analysis module is used for carrying out abnormality analysis on the historical physical examination data of the current user according to the acquired historical physical examination data of the current user and carrying out second abnormality analysis on the user according to the abnormal historical physical examination data of the current user;
The optimization module is used for performing primary optimization on the process of second abnormality analysis of the current user according to all user history physical examination data corresponding to the current user abnormality history physical examination data, and performing secondary optimization on the process of second abnormality analysis of the current user according to the physical examination times of the current user in the management period;
the recommending module is used for recommending physical examination items and physical examination time of the current user according to the first exception analysis result and the second exception analysis result of the current user;
and the feedback module is used for correcting the recommending process of the next physical examination time according to the abnormal number of physical examination data indexes of the current physical examination of the current user.
Further, the first analysis module is provided with an early warning analysis unit, and the early warning unit is used for comparing the blood glucose value a0 of the current user acquired in the monitoring period with a preset blood glucose value a1, performing abnormal analysis of blood glucose according to the comparison result, and performing abnormal early warning of blood glucose according to the analysis result, wherein:
when a0 is less than or equal to a1, the early warning unit judges that the blood glucose level of the current user is normal, and does not perform abnormal early warning of blood glucose;
when a0 is more than a1, the early warning unit judges that the blood sugar value of the current user is abnormal and carries out early warning of abnormal blood sugar.
Further, the first analysis module is further provided with a first abnormality analysis unit, and the first abnormality analysis unit is configured to compare the blood glucose abnormality pre-warning frequency b0 with a preset pre-warning frequency b1 in a monitoring period, and perform a first abnormality analysis on a current user according to a comparison result, where:
when b0 is less than or equal to b1, the first abnormality analysis unit judges that the blood glucose early warning frequency of the current user is normal;
when b0 is larger than b1, the first abnormality analysis unit judges that the blood sugar early warning frequency of the current user is abnormal.
Further, the adjustment module is provided with a first adjustment unit, the first adjustment unit is configured to compare a detection time t0 of a current user in a monitoring period with a preset detection time t1, perform abnormality analysis on the detection time according to a comparison result, and perform primary adjustment on a process of first abnormality analysis of the current user according to an analysis result, where:
when t0 is less than or equal to t1, the first adjusting unit judges that the current user detection time is normal;
when t0 is greater than t1, if t0-t1 is less than or equal to t2, the first adjusting unit determines that the current user detection time is normal, if t0-t1 is greater than t2, the first adjusting unit determines that the current user detection time is abnormal, adjusts the process of first abnormality analysis of the current user once, sets the adjusted preset early warning frequency as b1', and sets b1' =b1× {1+sin [ c0× (pi/2)/c 1] };
Wherein c0 is the number of abnormal time detected by the current user in the monitoring period, and c1 is the number of days in the monitoring period.
Further, the adjustment module is provided with a second adjustment unit, and the second adjustment unit is configured to compare the number of days d0, which is not detected by the current user, with the preset number of days d1 in the monitoring period, and perform secondary adjustment on the process of the first anomaly analysis of the current user according to the comparison result, where:
when d0 is less than or equal to d1, the second adjusting unit judges that the number of days undetected by the current user is normal, and secondary adjustment is not performed;
when d0 > d1, the second adjusting unit determines that the current user is abnormal in the number of days of detection, and performs secondary adjustment on the process of the first abnormality analysis of the current user, and sets the adjusted preset early warning frequency as b1", and sets b1" =b1' × [1-0.8× (d 0-d 1)/(d0+d1) ].
Further, the second analysis module compares the current user history physical examination data Ff with the standard physical examination data in the acquired management period, and analyzes the abnormality of the current user history physical examination data according to the comparison result, wherein:
when F R f＜Ff 1 Or F R f＞Ff 2 When the data second analysis module judges that the historical physical examination data of the current user is abnormal, if Fi/(FT×r) is less than or equal to Ffi0, the second analysis module judges that the physical examination item is a normal physical examination item, and if Fi/(FT×r) is more than Ffi0, the second analysis module judges that the physical examination item is an abnormal physical examination item;
When Ff 1 Or is less than or equal to F R f≤Ff 2 When the historical physical examination data of the current user is normal, the second analysis module judges that the historical physical examination data of the current user is normal;
wherein F is R F is historical physical examination data of F indexes of F items of the R current user, R is more than 0 and less than or equal to R, R is the number of historical physical examination of the current user, F is the category of physical examination items, F is more than 0 and less than FT, and FT is the number of physical examination indexes of the F items; ff (Ff) 1 Minimum standard physical examination data of F index of F item, ff 2 And the Fi is the abnormal historical physical examination data quantity of the current user in the F item, wherein the abnormal historical physical examination data quantity is the highest standard physical examination data of the F index of the F item.
Further, the optimization module is provided with a first optimization unit, and the first optimization unit is configured to perform the first optimization on all the user history physical examination data F corresponding to the abnormal history physical examination data R f' carrying out mean value calculation, comparing the calculation result with standard physical examination data, and carrying out primary optimization on the process of carrying out second abnormal analysis on the current user according to the comparison result, wherein:
when Ff Are all ”＜Ff 1 Or Ff Are all ”＞Ff 2 When the first optimizing unit performs a second abnormality analysis on the current userLine one-time optimization, if Ff Are all ”＜Ff 1 The first optimizing unit sets a preset abnormality ratio to Ffi0', sets Ffi0' = Ffi0× [1-0.23× (Ff) 1 -Ff Are all ”)/(Ff 1 +Ff Are all ”)]If Ff Are all ”＞Ff 2 The first optimizing unit sets a preset abnormality ratio to Ffi0", sets Ffi0" = Ffi0× [1-0.23× (Ff) Are all ”-Ff 2 )/(Ff 2 +Ff Are all ”)]；
When Ff 1 Or is less than or equal to Ff Are all ”≤Ff 2 When the first optimizing unit does not optimize;
wherein Ff Are all "= (Ff 1" +ff2"+, +ffm")/M, ff1 "is the 1 st user history physical examination data of the F-th index of the F-item, ff2" is the 2 nd user history physical examination data of the F-th index of the F-item, ffm "is the M-th user history physical examination data of the F-th index of the F-item, 0 < m.ltoreq.m, and M is the number of the F-th index user history physical examination data of the F-item.
Further, the optimization module is further provided with a second optimization unit, and the second optimization unit is configured to compare the physical examination times n0 of the current user with the preset physical examination times n1 in the management period, and perform secondary optimization on the process of performing the second abnormal analysis on the current user according to the comparison result, where:
when n0 < n1, the second optimizing unit determines that the physical examination times of the current user are abnormal, and performs a second optimization on the process of performing a second abnormality analysis on the current user, sets the optimized preset abnormality ratio to Ffi0", and sets Ffi" = Ffi0' × {1+0.75×arctan [ (n 1-n 0)/(n0+n 1) × (pi/4) ];
When n0 is more than or equal to n1, the second optimizing unit judges that the physical examination times of the current user are normal and does not optimize.
Further, the recommending module compares the abnormal physical examination item k0 of the current user with the number of each preset abnormal item in the management period, and recommends physical examination items and physical examination time of the current user according to the comparison result and the result of the first abnormal analysis of the current user, wherein:
when k is less than or equal to k1, the recommendation module judges that the number of abnormal physical examination items of the current user is small, if the blood sugar early warning times of the current user are normal, the recommendation module does not recommend physical examination information to the current user, if the blood sugar early warning times of the current user are abnormal, the recommendation module sets the recommended physical examination time as U1, sets U1=u0, and takes the abnormal physical examination items as recommended physical examination items, and the recommendation module pushes the recommended physical examination time U1 and the recommended physical examination items to the current user;
when k1 is smaller than k0 and smaller than k2, the recommendation module judges that the number of abnormal physical examination items of the current user is normal, if the blood sugar early warning times of the current user are normal, the recommendation module sets the recommended physical examination time as U2, sets U2 = U0, and uses the abnormal physical examination items as recommended physical examination items, the recommendation module pushes the recommended physical examination time U2 and the recommended physical examination items to the current user, if the blood sugar early warning times of the current user are abnormal, the recommendation module sets the recommended physical examination time as U3, sets U3 = U0 x [1- (b 0-b 1)/(b 0+ b 1) ], and uses the abnormal physical examination items as recommended physical examination items, and the recommendation module pushes the recommended physical examination time U3 and the recommended physical examination items to the current user;
When k0 is more than or equal to k2, the recommendation module judges that the number of abnormal physical examination items of the current user is large, if the blood sugar early warning times of the current user are normal, the recommendation module sets the recommended physical examination time as U4, sets U4 = U0 x [1-0.8 x (k 0-k 2)/(k 0+ k 2) ], and uses the abnormal physical examination items as recommended physical examination items, the recommendation module pushes the recommended physical examination time U4 and the recommended physical examination items to the current user, if the blood sugar early warning times of the current user are abnormal, the recommendation module sets the recommended physical examination time as U5, sets U5 = U0 x [1- (b 0-b 1)/(b0+b1) ]x [1-0.8 x (k 0-k 2)/(k0+k2) ], and uses the recommended physical examination items and the recommendation module pushes the recommended physical examination time U5 and the recommended physical examination items to the current user;
wherein k1 is the minimum preset abnormal item number, k2 is the maximum preset abnormal item number, and u0 is the preset physical examination time.
Further, the feedback module compares the number s0 of abnormal physical examination data indexes of the current physical examination of the current user with each preset abnormal coefficient, and corrects the recommending process of the next physical examination time according to the comparison result, wherein:
when s0 is less than or equal to s1, the feedback module judges that the number of physical examination data index anomalies of the current physical examination of the current user is small, corrects the recommended process of the next physical examination time, sets the corrected preset physical examination time as u0', and sets u0' =u0× [1+0.2× (s 0-s 1)/(s0+s1) ];
When s1 is less than s0 and less than s2, the feedback module judges that the number of physical examination data index anomalies currently detected by the user is normal, and correction is not carried out;
when s0 is more than or equal to s1, the feedback module judges that the number of physical examination data indexes of the current physical examination of the user is more than or equal to the number of physical examination data indexes of the current physical examination of the user, corrects the recommended process of the next physical examination time, sets the corrected preset physical examination time as u0 ', and sets u 0' = u0× [1- (s 0-s 1)/(s 0+ s 1) ];
wherein s1 is the minimum preset anomaly coefficient, and s2 is the preset maximum anomaly coefficient.
Compared with the prior art, the invention has the advantages that the early warning unit improves the accuracy of the blood glucose abnormality analysis by setting the preset blood glucose value so as to improve the accuracy of the blood glucose abnormality early warning, thereby improving the accuracy of the first abnormality analysis on the current user, and finally improving the accuracy and the recommending efficiency of the physical examination information recommendation on the current user by setting the preset early warning times, thereby improving the accuracy of the first abnormality analysis on the current user, and finally improving the accuracy and the recommending efficiency of the physical examination information recommendation on the current user, the first adjusting unit improves the accuracy of the first abnormality analysis on the current user by setting the preset time difference so as to improve the accuracy of the physical examination information recommendation on the current user, and finally improves the accuracy and the recommending efficiency of the physical examination information recommendation on the current user by setting the preset days, and the second adjusting unit can improve the accuracy of the physical examination information recommendation on the current user by setting the preset time difference so as to improve the accuracy of the current abnormality analysis on the current user, the accuracy and the recommendation efficiency of the physical examination information recommendation of the current user are improved, the second optimization unit is used for improving the accuracy of the second exception analysis of the current user by setting the preset physical examination times, so that the exception analysis of the current user can be carried out from multiple dimensions, the accuracy and the recommendation efficiency of the physical examination information recommendation of the current user are improved, the recommendation module is used for improving the accuracy and the recommendation efficiency of the physical examination information recommendation of the current user by setting the preset exception items, and the feedback module is used for improving the accuracy of the next physical examination time recommendation by setting the preset exception coefficients, so that the accuracy and the recommendation efficiency of the physical examination information recommendation of the current user are improved.
Drawings
Fig. 1 is a schematic structural diagram of a physical examination information recommendation system based on big data in the present embodiment;
FIG. 2 is a schematic structural diagram of a first analysis module according to the present embodiment;
FIG. 3 is a schematic diagram of the adjusting module according to the present embodiment;
fig. 4 is a schematic structural diagram of an optimization module according to this embodiment.
Detailed Description
In order that the objects and advantages of the invention will become more apparent, the invention will be further described with reference to the following examples; it should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
It should be noted that, in the description of the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those skilled in the art according to the specific circumstances.
Referring to fig. 1, a schematic structural diagram of a physical examination information recommendation system based on big data according to the present embodiment is shown, the system includes,
the information acquisition module is used for acquiring historical physical examination project data, blood sugar data and recommended physical examination projects of the current user; the current user history physical examination item data comprises blood sugar detection data, urine sugar detection data, blood fat detection data, blood pressure detection data, liver function detection data, kidney function detection data and glycosylated hemoglobin detection data; the current user blood sugar data comprises the blood sugar value of the current user, the detection time of the current user and the times of undetected current user; the recommended physical examination items comprise fundus examination, electrocardiogram examination, diabetic foot examination, chest radiography, B ultrasonic and the like; the blood sugar detection data comprises a fasting blood sugar index, a random blood sugar index and a glycosylated hemoglobin index, the blood pressure detection data comprises a systolic pressure index and a diastolic pressure index, the urine sugar detection data comprises a urine sugar quantitative index, a urine sugar excretion rate index and a fasting urine sugar index, the blood fat detection data comprises a low-density lipoprotein cholesterol index, a high-density lipoprotein cholesterol index, a very low-density lipoprotein cholesterol index and a triglyceride index, the liver function detection data comprises a glutamic pyruvic transaminase index, a glutamic oxaloacetic transaminase index, a total protein index, an albumin index, a total bilirubin index, a direct bilirubin index and an indirect bilirubin index, the kidney function detection data comprises a blood creatinine index, a urea nitrogen index, a uric acid index, a urine protein index, a urine red blood cell and a urine leucocyte index, and the glycosylated hemoglobin index; in the embodiment, the setting of the current user history physical examination data and the hospital history physical examination data is not particularly limited, and the person skilled in the art can freely set the setting only by meeting the setting requirement of the current user history physical examination data and the hospital history physical examination data; in the embodiment, the method for acquiring the historical physical examination item data of the current user, the blood sugar data of the current user and the recommended physical examination item is not particularly limited, and can be freely set by a person skilled in the art, and only the acquisition requirements of the historical physical examination item data of the current user, the blood sugar data of the current user and the recommended physical examination item are met, wherein the historical physical examination item data of the current user, the blood sugar data of the current user and the recommended physical examination item can be acquired through interaction;
The first analysis module is used for carrying out abnormal blood sugar early warning according to the blood sugar value of the current user acquired in the monitoring period, carrying out first abnormal analysis on the current user according to the blood sugar early warning result, and connecting with the information acquisition module; in this embodiment, the setting of the monitoring period is not specifically limited, and a person skilled in the art can freely set the monitoring period only by meeting the setting requirement of the monitoring period, wherein the monitoring period can be set to 20 days, 30 days, 40 days, and the like;
the adjustment module is used for carrying out primary adjustment on the process of the first abnormality analysis of the current user according to the acquired detection time of the current user in the monitoring period, and carrying out secondary adjustment on the process of the first abnormality analysis of the current user according to the number of times of undetected current user in the monitoring period, and is connected with the first analysis module;
the second analysis module is used for carrying out abnormality analysis on the historical physical examination data of the current user according to the acquired historical physical examination data of the current user, carrying out second abnormality analysis on the user according to the abnormal historical physical examination data of the current user, and connecting with the adjustment module;
the optimization module is used for optimizing the process of the second abnormality analysis of the current user once according to all user history physical examination data corresponding to the current user abnormality history physical examination data, and also used for optimizing the process of the second abnormality analysis of the current user twice according to the physical examination times of the current user in a management period, the optimization module is connected with the second analysis module, the setting of the management period is not particularly limited in the embodiment, the management period can be freely set by a person skilled in the art, only the setting requirement of the management period is met, and the setting time of the management period is worth noting that the setting time of the management period is longer than the setting time of the monitoring period, wherein the management period can be set to be 1 year, 2 years, 3 years and the like;
The recommendation module is used for recommending physical examination items and physical examination time of the current user according to the first exception analysis result and the second exception analysis result of the current user, and is connected with the optimization module;
the feedback module is used for correcting the recommending process of the next physical examination time according to the abnormal number of physical examination data indexes of the current physical examination of the user, and is connected with the recommending module; in this embodiment, the method for acquiring the number of abnormal physical examination data indexes of the current physical examination is not specifically limited, and a person skilled in the art can freely set the method only by meeting the acquisition requirement of the number of abnormal physical examination data indexes of the current physical examination, wherein the number of abnormal physical examination data indexes of the current physical examination can be acquired through interaction.
Fig. 2 is a schematic structural diagram of a first analysis module according to the present embodiment, where the first analysis module includes,
the early warning unit is used for carrying out abnormal blood sugar early warning according to the blood sugar value of the current user acquired in the monitoring period;
the first abnormality analysis unit is used for carrying out first abnormality analysis on the current user according to the blood sugar early warning result in the monitoring period, and is connected with the early warning unit.
Fig. 3 is a schematic structural diagram of an adjustment module according to the present embodiment, where the adjustment module includes,
the first adjusting unit is used for adjusting the first abnormality analysis process of the current user for one time according to the detection time of the current user in the acquired monitoring period;
the second adjusting unit is used for carrying out secondary adjustment on the first abnormality analysis process of the current user according to the times of undetected current user in the monitoring period, and is connected with the first adjusting unit.
Referring to fig. 4, a schematic structural diagram of an optimization module according to the present embodiment is shown, where the optimization module includes,
the acquisition unit is used for acquiring all user history physical examination data corresponding to the current user abnormal history physical examination data; in this embodiment, the method for acquiring all the user history physical examination data corresponding to the current user abnormal history physical examination data is not specifically limited, and a person skilled in the art can freely set the method only by meeting the acquisition requirement of all the user history physical examination data corresponding to the current user abnormal history physical examination data, wherein all the user history physical examination data corresponding to the current user abnormal history physical examination data can be acquired through the physical examination platform background data importing;
The first optimizing unit is used for optimizing the process of second abnormality analysis of the current user once according to all user history physical examination data corresponding to the current user abnormality history physical examination data, and is connected with the acquisition unit;
the second optimizing unit is used for carrying out secondary optimization on the second abnormality analysis process of the current user according to the physical examination times of the current user in the management period, and is connected with the first optimizing unit.
Specifically, the embodiment is applied to physical examination information recommendation of diabetics, and performs primary exception analysis on blood glucose detection data of a user, performs secondary analysis on the user according to historical physical examination data of the user, and performs physical examination information recommendation on the user by combining the results of the two exception analyses.
Specifically, the early warning unit compares the blood glucose value a0 of the current user acquired in the monitoring period with a preset blood glucose value a1, performs abnormal analysis of blood glucose according to the comparison result, and performs abnormal early warning of blood glucose according to the analysis result, wherein:
When a0 is less than or equal to a1, the early warning unit judges that the blood glucose level of the current user is normal, and does not perform abnormal early warning of blood glucose;
when a0 is more than a1, the early warning unit judges that the blood sugar value of the current user is abnormal and carries out early warning of abnormal blood sugar.
Specifically, the early warning unit improves the accuracy of blood glucose abnormality analysis by setting a preset blood glucose value, so that the accuracy of blood glucose abnormality early warning is improved, the accuracy of first abnormality analysis on the current user is improved, and finally the accuracy and the recommendation efficiency of physical examination information recommendation on the current user are improved; in this embodiment, the preset blood glucose level is not specifically limited, and a person skilled in the art can freely set the preset blood glucose level only by meeting the setting requirement of the preset blood glucose level, wherein the optimal value of the preset blood glucose level is 6.1mmol/L.
Specifically, the first abnormality analysis unit compares the blood glucose abnormality pre-warning number b0 in the monitoring period with a preset pre-warning number b1, and performs a first abnormality analysis on the current user according to the comparison result, wherein:
when b0 is less than or equal to b1, the first abnormality analysis unit judges that the blood glucose early warning frequency of the current user is normal;
when b0 is larger than b1, the first abnormality analysis unit judges that the blood sugar early warning frequency of the current user is abnormal.
Specifically, the first abnormality analysis unit improves the accuracy of blood glucose abnormality pre-warning by setting preset pre-warning times, so that the accuracy of first abnormality analysis on the current user is improved, and finally the accuracy and the recommendation efficiency of physical examination information recommendation on the current user are improved; in this embodiment, the value of the preset early warning times is not specifically limited, and a person skilled in the art can freely set the value of the preset early warning times only by meeting the setting requirement of the preset early warning times, wherein if the monitoring period is 30 days, the optimal value of the preset early warning times is 3.
Specifically, the first adjusting unit compares the detection time t0 of the current user with the preset detection time t1 in the monitoring period, performs abnormality analysis on the detection time according to the comparison result, and adjusts the process of the first abnormality analysis of the current user once according to the analysis result, wherein:
when t0 is less than or equal to t1, the first adjusting unit judges that the current user detection time is normal;
when t0 is greater than t1, if t0-t1 is less than or equal to t2, the first adjusting unit determines that the current user detection time is normal, if t0-t1 is greater than t2, the first adjusting unit determines that the current user detection time is abnormal, adjusts the process of first abnormality analysis of the current user once, sets the adjusted preset early warning frequency as b1', and sets b1' =b1× {1+sin [ c0× (pi/2)/c 1] };
Wherein c0 is the number of abnormal time detected by the current user in the monitoring period, c1 is the number of days in the monitoring period, and t2 is the preset time difference.
Specifically, the first adjusting unit improves the accuracy of early warning of abnormal blood sugar by setting a preset time difference, so that the accuracy of first abnormal analysis on the current user is improved, and finally the accuracy and the recommending efficiency of physical examination information recommendation on the current user are improved; in this embodiment, the values of the preset time difference and the preset detection time are not specifically limited, and can be freely set by a person skilled in the art, and only the requirement of the preset time difference and the preset detection time is met, wherein the optimal value of t1 is 8 points, and the optimal value of t2 is 2 hours.
Specifically, the second adjusting unit compares the number of undetected days d0 of the current user with the preset number of days d1 in the monitoring period, and performs secondary adjustment on the process of the first anomaly analysis of the current user according to the comparison result, wherein:
when d0 is less than or equal to d1, the second adjusting unit judges that the number of days undetected by the current user is normal, and secondary adjustment is not performed;
when d0 > d1, the second adjusting unit determines that the current user is abnormal in the number of days of detection, and performs secondary adjustment on the process of the first abnormality analysis of the current user, and sets the adjusted preset early warning frequency as b1", and sets b1" =b1' × [1-0.8× (d 0-d 1)/(d0+d1) ].
Specifically, the second adjusting unit improves the accuracy of the blood glucose abnormality pre-warning by setting preset days, so that the accuracy of the first abnormality analysis of the current user is improved, and finally the accuracy and the recommending efficiency of the physical examination information of the current user are improved; in this embodiment, the value of the preset number of days is not specifically limited, and a person skilled in the art can freely set the value of the preset number of days only by meeting the value requirement of the preset number of days, wherein when the monitoring period is 30 days, the optimal value of d1 is 3.
Specifically, the second analysis module compares the current user history physical examination data Ff with the standard physical examination data in the acquired management period, and analyzes the abnormality of the current user history physical examination data according to the comparison result, wherein:
when F R f＜Ff 1 Or F R f＞Ff 2 When the data second analysis module judges that the historical physical examination data of the current user is abnormal, if Fi/(FT×r) is less than or equal to Ffi0, the second analysis module judges that the physical examination item is a normal physical examination item, and if Fi/(FT×r) is more than Ffi0, the second analysis module judges that the physical examination item is an abnormal physical examination item;
when Ff 1 Or is less than or equal to F R f≤Ff 2 When the historical physical examination data of the current user is normal, the second analysis module judges that the historical physical examination data of the current user is normal;
Wherein F is R F is historical physical examination data of F indexes of F items of the R current user, R is more than 0 and less than or equal to R, R is the number of historical physical examination of the current user, F is the category of physical examination items, F is more than 0 and less than FT, and FT is the number of physical examination indexes of the F items; ff (Ff) 1 Minimum standard physical examination data of F index of F item, ff 2 And (5) the highest standard physical examination data of the F index of the F item, fi is the abnormal historical physical examination data quantity of the current user in the F item, and Ffi0 is the preset abnormal proportion.
Specifically, the second analysis module sets a preset abnormality proportion to improve the accuracy of performing second abnormality analysis on the current user, so that the current user can be subjected to abnormality analysis from multiple dimensions, and the accuracy and recommendation efficiency of recommending physical examination information of the current user are improved; in this embodiment, the value of the preset abnormal ratio is not specifically limited, and a person skilled in the art can freely set the value of the preset abnormal ratio only by meeting the setting requirement of the preset abnormal ratio, wherein the optimal value of the preset abnormal ratio is 0.25; it should be noted that, in this embodiment, the setting of the standard physical examination data is not specifically limited, and a person skilled in the art can freely set the standard physical examination data only by meeting the setting requirement of the standard physical examination data, where the standard physical examination data can be set through interaction.
Specifically, the first optimizing unit performs the first optimization on all the user history physical examination data F corresponding to the current user abnormal history physical examination data R f' carrying out mean value calculation, comparing the calculation result with standard physical examination data, and carrying out primary optimization on the process of carrying out second abnormal analysis on the current user according to the comparison result, wherein: and is combined with
When Ff Are all ”＜Ff 1 Or Ff Are all ”＞Ff 2 When the first optimizing unit optimizes the process of the second abnormality analysis of the current user once, if Ff Are all ”＜Ff 1 The first optimizing unit sets a preset abnormality ratio to Ffi0', sets Ffi0' = Ffi0× [1-0.23× (Ff) 1 -Ff Are all ”)/(Ff 1 +Ff Are all ”)]If Ff Are all ”＞Ff 2 The first optimizing unit sets a preset abnormality ratio to Ffi0", sets Ffi0" = Ffi0× [1-0.23× (Ff) Are all ”-Ff 2 )/(Ff 2 +Ff Are all ”)]；
When Ff 1 Or is less than or equal to Ff Are all ”≤Ff 2 When the first optimizing unit does not optimize;
wherein Ff Are all "= (Ff 1" +ff2"+, +ffm")/M, ff1 "is the 1 st user history physical examination data of the F-th index of the F-item, ff2" is the 2 nd user history physical examination data of the F-th index of the F-item, ffm "is the M-th user history physical examination data of the F-th index of the F-item, 0 <M is less than or equal to M, and M is the number of F index user historical physical examination data of the F item.
Specifically, the first optimizing unit analyzes the historical physical examination data of all users corresponding to the current user abnormal historical physical examination data to improve the accuracy of second abnormal analysis on the current user, so that the current user can be subjected to abnormal analysis from multiple dimensions, and the accuracy and the recommendation efficiency of physical examination information recommendation on the current user are improved.
Specifically, the second optimizing unit compares the physical examination times n0 of the current user with the preset physical examination times n1 in the management period, and performs secondary optimization on the process of performing second abnormality analysis on the current user according to the comparison result, wherein:
when n0 < n1, the second optimizing unit determines that the physical examination times of the current user are abnormal, and performs a second optimization on the process of performing a second abnormality analysis on the current user, sets the optimized preset abnormality ratio to Ffi0", and sets Ffi" = Ffi0' × {1+0.75×arctan [ (n 1-n 0)/(n0+n 1) × (pi/4) ];
when n0 is more than or equal to n1, the second optimizing unit judges that the physical examination times of the current user are normal and does not optimize.
Specifically, the second optimizing unit sets the preset physical examination times to improve the accuracy of performing second exception analysis on the current user, so that exception analysis can be performed on the current user from multiple dimensions, and the accuracy and recommendation efficiency of physical examination information recommendation on the current user are improved; in this embodiment, the value of the preset physical examination times is not specifically limited, and a person skilled in the art can freely set the value of the preset physical examination times only by meeting the setting requirement of the preset physical examination times, wherein when the management period is 2 years, the optimal value of the preset physical examination times is 8;
Specifically, the recommendation module compares the abnormal physical examination item k0 of the current user with the number of preset abnormal items in the management period, and recommends physical examination items and physical examination time of the current user according to the comparison result and the result of the first abnormal analysis of the current user, wherein:
when k is less than or equal to k1, the recommendation module judges that the number of abnormal physical examination items of the current user is small, if the blood sugar early warning times of the current user are normal, the recommendation module does not recommend physical examination information to the current user, if the blood sugar early warning times of the current user are abnormal, the recommendation module sets the recommended physical examination time as U1, sets U1=u0, and takes the abnormal physical examination items as recommended physical examination items, and the recommendation module pushes the recommended physical examination time U1 and the recommended physical examination items to the current user;
when k1 is smaller than k0 and smaller than k2, the recommendation module judges that the number of abnormal physical examination items of the current user is normal, if the blood sugar early warning times of the current user are normal, the recommendation module sets the recommended physical examination time as U2, sets U2 = U0, and uses the abnormal physical examination items as recommended physical examination items, the recommendation module pushes the recommended physical examination time U2 and the recommended physical examination items to the current user, if the blood sugar early warning times of the current user are abnormal, the recommendation module sets the recommended physical examination time as U3, sets U3 = U0 x [1- (b 0-b 1)/(b 0+ b 1) ], and uses the abnormal physical examination items as recommended physical examination items, and the recommendation module pushes the recommended physical examination time U3 and the recommended physical examination items to the current user;
When k0 is more than or equal to k2, the recommendation module judges that the number of abnormal physical examination items of the current user is large, if the blood sugar early warning times of the current user are normal, the recommendation module sets the recommended physical examination time as U4, sets U4 = U0 x [1-0.8 x (k 0-k 2)/(k 0+ k 2) ], and uses the abnormal physical examination items as recommended physical examination items, the recommendation module pushes the recommended physical examination time U4 and the recommended physical examination items to the current user, if the blood sugar early warning times of the current user are abnormal, the recommendation module sets the recommended physical examination time as U5, sets U5 = U0 x [1- (b 0-b 1)/(b0+b1) ]x [1-0.8 x (k 0-k 2)/(k0+k2) ], and uses the recommended physical examination items and the recommendation module pushes the recommended physical examination time U5 and the recommended physical examination items to the current user;
wherein k1 is the minimum preset abnormal item number, k2 is the maximum preset abnormal item number, and u0 is the preset physical examination time.
Specifically, the recommending module sets the number of preset abnormal items to improve accuracy and recommending efficiency of recommending physical examination information of the current user; it can be understood that, in this embodiment, the preset physical examination time is recommended physical examination after u0 days after the pushing is finished; in this embodiment, the setting of the number of preset abnormal items and the preset physical examination time is not specifically limited, and a person skilled in the art can freely set the setting of the number of preset abnormal items and the preset physical examination time only needs to be satisfied, wherein the optimal value of k1 is 2, the optimal value of k2 is 4, and the optimal value of u0 is 30 days.
Specifically, the feedback module compares the number s0 of abnormal physical examination data indexes of the current physical examination of the current user with each preset abnormal coefficient, and corrects the recommending process of the next physical examination time according to the comparison result, wherein:
when s0 is less than or equal to s1, the feedback module judges that the number of physical examination data index anomalies of the current physical examination of the current user is small, corrects the recommended process of the next physical examination time, sets the corrected preset physical examination time as u0', and sets u0' =u0× [1+0.2× (s 0-s 1)/(s0+s1) ];
when s1 is less than s0 and less than s2, the feedback module judges that the number of physical examination data index anomalies currently detected by the user is normal, and correction is not carried out;
when s0 is more than or equal to s1, the feedback module judges that the number of physical examination data indexes of the current physical examination of the user is more than or equal to the number of physical examination data indexes of the current physical examination of the user, corrects the recommended process of the next physical examination time, sets the corrected preset physical examination time as u0', and sets u0' = u0× [1- (s 0-s 1)/(s 0+ s 1) ];
wherein s1 is the minimum preset anomaly coefficient, and s2 is the preset maximum anomaly coefficient.
Specifically, the feedback module improves the accuracy of recommending the next physical examination time by setting a preset abnormal coefficient, so that the accuracy and the recommending efficiency of recommending physical examination information of the current user are improved; in this embodiment, the setting of the preset abnormal coefficient is not specifically limited, and a person skilled in the art can freely set the preset abnormal coefficient only by meeting the setting requirement of the preset abnormal coefficient, wherein the optimal value of s1 is 3, and the optimal value of s2 is 6.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.
Claims (10)
1. A physical examination information recommendation system based on big data is characterized by comprising,
the information acquisition module is used for acquiring historical physical examination project data, blood sugar data and recommended physical examination projects of the current user;
the first analysis module is used for carrying out abnormal blood sugar early warning according to the blood sugar value of the current user acquired in the monitoring period and carrying out first abnormal analysis on the current user according to the blood sugar early warning result;
the adjusting module is used for carrying out primary adjustment on the process of the first abnormality analysis of the current user according to the acquired detection time of the current user in the monitoring period and carrying out secondary adjustment on the process of the first abnormality analysis of the current user according to the number of times that the current user is not detected in the monitoring period;
The second analysis module is used for carrying out abnormality analysis on the historical physical examination data of the current user according to the acquired historical physical examination data of the current user and carrying out second abnormality analysis on the user according to the abnormal historical physical examination data of the current user;
the optimization module is used for performing primary optimization on the process of second abnormality analysis of the current user according to all user history physical examination data corresponding to the current user abnormality history physical examination data, and performing secondary optimization on the process of second abnormality analysis of the current user according to the physical examination times of the current user in the management period;
the recommending module is used for recommending physical examination items and physical examination time of the current user according to the first exception analysis result and the second exception analysis result of the current user;
and the feedback module is used for correcting the recommending process of the next physical examination time according to the abnormal number of physical examination data indexes of the current physical examination of the current user.
2. The physical examination information recommendation system based on big data according to claim 1, wherein the first analysis module is provided with an early warning analysis unit, the early warning unit is configured to compare a blood glucose value a0 of a current user acquired in a monitoring period with a preset blood glucose value a1, perform abnormal analysis of blood glucose according to a comparison result, and perform abnormal early warning of blood glucose according to an analysis result, wherein:
When a0 is less than or equal to a1, the early warning unit judges that the blood glucose level of the current user is normal, and does not perform abnormal early warning of blood glucose;
when a0 is more than a1, the early warning unit judges that the blood sugar value of the current user is abnormal and carries out early warning of abnormal blood sugar.
3. The physical examination information recommendation system based on big data according to claim 2, wherein the first analysis module is further provided with a first abnormality analysis unit, the first abnormality analysis unit is configured to compare a blood glucose abnormality pre-warning frequency b0 with a preset pre-warning frequency b1 in a monitoring period, and perform a first abnormality analysis on a current user according to a comparison result, wherein:
when b0 is less than or equal to b1, the first abnormality analysis unit judges that the blood glucose early warning frequency of the current user is normal;
when b0 is larger than b1, the first abnormality analysis unit judges that the blood sugar early warning frequency of the current user is abnormal.
4. The physical examination information recommendation system based on big data according to claim 3, wherein the adjustment module is provided with a first adjustment unit, the first adjustment unit is configured to compare a detection time t0 of a current user in a monitoring period with a preset detection time t1, perform an abnormality analysis on the detection time according to a comparison result, and perform a one-time adjustment on a process of a first abnormality analysis of the current user according to an analysis result, wherein:
When t0 is less than or equal to t1, the first adjusting unit judges that the current user detection time is normal;
when t0 is greater than t1, if t0-t1 is less than or equal to t2, the first adjusting unit determines that the current user detection time is normal, if t0-t1 is greater than t2, the first adjusting unit determines that the current user detection time is abnormal, adjusts the process of first abnormality analysis of the current user once, sets the adjusted preset early warning frequency as b1', and sets b1' =b1× {1+sin [ c0× (pi/2)/c 1] };
wherein c0 is the number of abnormal time detected by the current user in the monitoring period, and c1 is the number of days in the monitoring period.
5. The physical examination information recommendation system based on big data according to claim 4, wherein the adjustment module is provided with a second adjustment unit, the second adjustment unit is configured to compare a number d0 of undetected days of a current user with a preset number d1 of undetected days in a monitoring period, and perform secondary adjustment on a process of first anomaly analysis of the current user according to a comparison result, wherein:
when d0 is less than or equal to d1, the second adjusting unit judges that the number of days undetected by the current user is normal, and secondary adjustment is not performed;
when d0 > d1, the second adjusting unit determines that the current user is abnormal in the number of days of detection, and performs secondary adjustment on the process of the first abnormality analysis of the current user, and sets the adjusted preset early warning frequency as b1", and sets b1" =b1' × [1-0.8× (d 0-d 1)/(d0+d1) ].
6. The big data-based physical examination information recommendation system of claim 1, wherein the second analysis module compares the current user history physical examination data Ff with standard physical examination data in the acquired management period, and analyzes abnormality of the current user history physical examination data according to a comparison result, wherein:
when F R f＜Ff 1 Or F R f＞Ff 2 In the case, the data second analysis module determines the current userIf the history physical examination data is abnormal, if Fi/(FT×r) is less than or equal to Ffi0, the second analysis module judges that the physical examination item is a normal physical examination item, and if Fi/(FT×r) is more than Ffi0, the second analysis module judges that the physical examination item is an abnormal physical examination item;
when Ff 1 Or is less than or equal to F R f≤Ff 2 When the historical physical examination data of the current user is normal, the second analysis module judges that the historical physical examination data of the current user is normal;
wherein F is R F is historical physical examination data of F indexes of F items of the R current user, R is more than 0 and less than or equal to R, R is the number of historical physical examination of the current user, F is the category of physical examination items, F is more than 0 and less than FT, and FT is the number of physical examination indexes of the F items; ff (Ff) 1 Minimum standard physical examination data of F index of F item, ff 2 And the Fi is the abnormal historical physical examination data quantity of the current user in the F item, wherein the abnormal historical physical examination data quantity is the highest standard physical examination data of the F index of the F item.
7. The big data-based physical examination information recommendation system according to claim 1, wherein the optimization module is provided with a first optimization unit for optimizing all user history physical examination data F corresponding to the abnormal history physical examination data R f' carrying out mean value calculation, comparing the calculation result with standard physical examination data, and carrying out primary optimization on the process of carrying out second abnormal analysis on the current user according to the comparison result, wherein:
when Ff Are all ”＜Ff 1 Or Ff Are all ”＞Ff 2 When the first optimizing unit optimizes the process of the second abnormality analysis of the current user once, if Ff Are all ”＜Ff 1 The first optimizing unit sets a preset abnormality ratio to Ffi0', sets Ffi0' = Ffi0× [1-0.23× (Ff) 1 -Ff Are all ”)/(Ff 1 +Ff Are all ”)]If Ff Are all ”＞Ff 2 The first optimizing unit sets a preset abnormality ratio to Ffi0", sets Ffi0" = Ffi0× [1-0.23× (Ff) Are all ”-Ff 2 )/(Ff 2 +Ff Are all ”)]；
When Ff 1 Or is less than or equal to Ff Are all ”≤Ff 2 When the first optimizing unit does not optimize;
wherein Ff Are all "= (Ff 1" +ff2"+, +ffm")/M, ff1 "is the 1 st user history physical examination data of the F-th index of the F-item, ff2" is the 2 nd user history physical examination data of the F-th index of the F-item, ffm "is the M-th user history physical examination data of the F-th index of the F-item, 0 < m.ltoreq.m, and M is the number of the F-th index user history physical examination data of the F-item.
8. The big data-based physical examination information recommendation system according to claim 7, wherein the optimization module is further provided with a second optimization unit, the second optimization unit is configured to compare the number of physical examination n0 of the current user with the preset number of physical examination n1 in the management period, and perform secondary optimization on the process of performing the second abnormality analysis on the current user according to the comparison result, where:
when n0 < n1, the second optimizing unit determines that the physical examination times of the current user are abnormal, and performs a second optimization on the process of performing a second abnormality analysis on the current user, sets the optimized preset abnormality ratio to Ffi0", and sets Ffi" = Ffi0' × {1+0.75×arctan [ (n 1-n 0)/(n0+n 1) × (pi/4) ];
when n0 is more than or equal to n1, the second optimizing unit judges that the physical examination times of the current user are normal and does not optimize.
9. The big data based physical examination information recommendation system of claim 3, wherein the recommendation module compares an abnormal physical examination item k0 of the current user with the number of each preset abnormal item in the management period, and recommends physical examination items and physical examination time of the current user according to a comparison result and a result of the first abnormal analysis of the current user, wherein:
When k is less than or equal to k1, the recommendation module judges that the number of abnormal physical examination items of the current user is small, if the blood sugar early warning times of the current user are normal, the recommendation module does not recommend physical examination information to the current user, if the blood sugar early warning times of the current user are abnormal, the recommendation module sets the recommended physical examination time as U1, sets U1=u0, and takes the abnormal physical examination items as recommended physical examination items, and the recommendation module pushes the recommended physical examination time U1 and the recommended physical examination items to the current user;
when k1 is smaller than k0 and smaller than k2, the recommendation module judges that the number of abnormal physical examination items of the current user is normal, if the blood sugar early warning times of the current user are normal, the recommendation module sets the recommended physical examination time as U2, sets U2 = U0, and uses the abnormal physical examination items as recommended physical examination items, the recommendation module pushes the recommended physical examination time U2 and the recommended physical examination items to the current user, if the blood sugar early warning times of the current user are abnormal, the recommendation module sets the recommended physical examination time as U3, sets U3 = U0 x [1- (b 0-b 1)/(b 0+ b 1) ], and uses the abnormal physical examination items as recommended physical examination items, and the recommendation module pushes the recommended physical examination time U3 and the recommended physical examination items to the current user;
When k0 is more than or equal to k2, the recommendation module judges that the number of abnormal physical examination items of the current user is large, if the blood sugar early warning times of the current user are normal, the recommendation module sets the recommended physical examination time as U4, sets U4 = U0 x [1-0.8 x (k 0-k 2)/(k 0+ k 2) ], and uses the abnormal physical examination items as recommended physical examination items, the recommendation module pushes the recommended physical examination time U4 and the recommended physical examination items to the current user, if the blood sugar early warning times of the current user are abnormal, the recommendation module sets the recommended physical examination time as U5, sets U5 = U0 x [1- (b 0-b 1)/(b0+b1) ]x [1-0.8 x (k 0-k 2)/(k0+k2) ], and uses the recommended physical examination items and the recommendation module pushes the recommended physical examination time U5 and the recommended physical examination items to the current user;
wherein k1 is the minimum preset abnormal item number, k2 is the maximum preset abnormal item number, and u0 is the preset physical examination time.
10. The big data-based physical examination information recommendation system according to claim 9, wherein the feedback module compares the number s0 of physical examination data index anomalies of the current physical examination of the current user with each preset anomaly coefficient, and corrects a recommendation process of the next physical examination time according to the comparison result, wherein:
When s0 is less than or equal to s1, the feedback module judges that the number of physical examination data index anomalies of the current physical examination of the current user is small, corrects the recommended process of the next physical examination time, sets the corrected preset physical examination time as u0', and sets u0' =u0× [1+0.2× (s 0-s 1)/(s0+s1) ];
when s1 is less than s0 and less than s2, the feedback module judges that the number of physical examination data index anomalies currently detected by the user is normal, and correction is not carried out;
when s0 is more than or equal to s1, the feedback module judges that the number of physical examination data indexes of the current physical examination of the user is more than or equal to the number of physical examination data indexes of the current physical examination of the user, corrects the recommended process of the next physical examination time, sets the corrected preset physical examination time as u0', and sets u0' = u0× [1- (s 0-s 1)/(s 0+ s 1) ];
wherein s1 is the minimum preset anomaly coefficient, and s2 is the preset maximum anomaly coefficient.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118280592A (en) * | 2024-05-29 | 2024-07-02 | 潍坊京达铭医疗科技有限公司 | Wisdom medical monitoring system |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107346376A (en) * | 2017-07-05 | 2017-11-14 | 刘浩 | A kind of method and system of health examination project intelligent recommendation |
| CN111341446A (en) * | 2020-02-11 | 2020-06-26 | 中山大学 | Personalized physical examination package recommendation method |
| CN112182412A (en) * | 2020-11-26 | 2021-01-05 | 南京吉拉福网络科技有限公司 | Method, computing device, and computer storage medium for recommending physical examination items |
| CN112908458A (en) * | 2021-03-29 | 2021-06-04 | 善诊(上海)信息技术有限公司 | Physical examination package generation system and method and computer readable storage medium |
| CN113782195A (en) * | 2021-01-15 | 2021-12-10 | 北京京东拓先科技有限公司 | Physical examination package customization method and device |
| US20220037002A1 (en) * | 2020-07-31 | 2022-02-03 | Boe Technology Group Co., Ltd. | Health managing method and storage medium |
| CN114664443A (en) * | 2022-03-18 | 2022-06-24 | 中南大学湘雅医院 | Intelligent physical examination package recommendation system |
| CN116504401A (en) * | 2023-03-21 | 2023-07-28 | 北京京东拓先科技有限公司 | Intelligent physical examination project recommendation method and device |
-
2023
- 2023-12-04 CN CN202311640718.2A patent/CN117577327B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107346376A (en) * | 2017-07-05 | 2017-11-14 | 刘浩 | A kind of method and system of health examination project intelligent recommendation |
| CN111341446A (en) * | 2020-02-11 | 2020-06-26 | 中山大学 | Personalized physical examination package recommendation method |
| US20220037002A1 (en) * | 2020-07-31 | 2022-02-03 | Boe Technology Group Co., Ltd. | Health managing method and storage medium |
| CN112182412A (en) * | 2020-11-26 | 2021-01-05 | 南京吉拉福网络科技有限公司 | Method, computing device, and computer storage medium for recommending physical examination items |
| CN113782195A (en) * | 2021-01-15 | 2021-12-10 | 北京京东拓先科技有限公司 | Physical examination package customization method and device |
| CN112908458A (en) * | 2021-03-29 | 2021-06-04 | 善诊(上海)信息技术有限公司 | Physical examination package generation system and method and computer readable storage medium |
| CN114664443A (en) * | 2022-03-18 | 2022-06-24 | 中南大学湘雅医院 | Intelligent physical examination package recommendation system |
| CN116504401A (en) * | 2023-03-21 | 2023-07-28 | 北京京东拓先科技有限公司 | Intelligent physical examination project recommendation method and device |
Non-Patent Citations (2)
| Title |
|---|
| 张婷婷 等: "一种基于改进轮询机制的体检项目智能推荐模型", 《绥化学院学报》, vol. 36, no. 06, 5 June 2016 (2016-06-05), pages 152 - 154 * |
| 张红玉;李莉;张光;马小静;姜慧;杨莎;董桂青;: "基于数据挖掘的体检套餐推荐系统设计及应用", 信息技术与信息化, no. 09, 25 September 2017 (2017-09-25), pages 84 - 86 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118280592A (en) * | 2024-05-29 | 2024-07-02 | 潍坊京达铭医疗科技有限公司 | Wisdom medical monitoring system |
| CN118280592B (en) * | 2024-05-29 | 2024-08-13 | 潍坊京达铭医疗科技有限公司 | Wisdom medical monitoring system |
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