CN101458738A - Disease serious degree ordering method, system thereof and recording medium - Google Patents
Disease serious degree ordering method, system thereof and recording medium Download PDFInfo
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Abstract
The invention discloses an illness severity sorting method, which utilizes a plurality of physiological parameters and an illness history database to judge an illness represented by a plurality of physiological parameters, thereby evaluating the severity and development tread, and determining a suggested treatment order when there are a plurality of illnesses occur. The method provides a train step and an execute step, wherein the train step comprises: attaining illness history data from the illness history database and establishing an optimum mathematical model for the illness; the execute step comprises: normalizing physiological parameters to obtain normalized physiological parameters, adding the normalized physiological parameters into the optimum mathematical model of the illness to calculate the energy value of the illness, and according to the energy value and the selected probability value of the illness, determining a suggested treatment order. The invention further discloses an illness severity storing system.
Description
Technical field
The present invention relates to a kind of severity sort method and system thereof, and relate in particular to a kind of disease serious degree ordering method and system thereof.
Background technology
We are in one and make rapid progress and develop the information age rapidly, in a lot of fields, computing machine has been used for assisting to judge the generation of certain situation or incident with different forms, the basis of these judgements is according to situation previous or known in the association area or incident mostly, especially when being used in the medical field that relates to many physiological parameters.
Arriving in response to aging society, the medical monitoring industry is shaped gradually, the main growth kinetic energy in this market come from can not be subjected to the time place restriction so that high-quality medical treatment and health service to be provided, the monitoring function of one of them is a physiological status of utilizing monitoring instrument monitoring sufferer, after obtaining physiological parameter by monitoring instrument, utilize and set up good rule base in advance, when reaching the caution value of setting in the rule base, triggers physiologic parameter value caution, so that when sufferer is fallen ill, can do the processing of the very first time.
Yet, its shortcoming is: can't present the illness that a plurality of physiologic parameter values of polymerization are presented behind, therefore need to judge according to the relevant speciality experience voluntarily by medical personnel, possessing the medical personnel that enrich professional experiences can judge immediately, but medical personnel elementary or that experience is less possibly can't make real-time and suitable reaction and help sufferer, and are subject in reaction time and personnel's the scheduling.In addition, when physiological parameter changes, can't judge that illness trend and severity change.And, when a plurality of illnesss occur simultaneously, can't advise the priority that medical personnel handle.
Therefore, need a disease serious degree ordering method to improve the problems referred to above.
Summary of the invention
Technical matters to be solved by this invention is to provide a kind of disease serious degree ordering method and system and recording medium, the method is utilized physiological parameter and the illness of illness historical data base to judge that a plurality of physiological parameter of polymerization is presented, the severity and the development trend of assessment illness, when several illnesss occur simultaneously, judge the suggestion processing sequence.
For achieving the above object, a kind of disease serious degree ordering method is proposed.According to a preferred embodiment of the present invention, this disease serious degree ordering method provides training stage and execute phase.Training stage comprises the following steps: to obtain the illness historical data from the illness historical data base, and utilizes the illness historical data to set up the suitableeest mathematical model.Execute phase comprises the following steps: to carry out physiological parameter normalization operation to obtain normalized physiological parameter, the suitableeest mathematical model of normalized physiological parameter substitution calculating the illness energy value, and is calculated the illness preferred value according to the probit value that is selected of illness energy value and illness.
The illness historical data base is collected and is come from medical personnel to the diagnosis and treatment record of various illnesss, relevant case, relevant information and each medical field expertise.When the training stage, the step of suitable mathematical model comprises to utilize the illness historical data to set up: select training mode, utilize training mode to produce and adjust the mathematical model of illness, when the mathematical model of illness meets expected results, carry out the reliability analysis, when the reliability analysis is higher than setting value, set up the suitableeest mathematical model of illness.When the mathematical model of illness does not meet expected results, reselect a kind of new training mode.When the reliability analysis is lower than setting value, reselect a new training mode.Training mode can be the technology of statistical method, mathematical method, artificial intelligence approach or other tool Training Capability.Training mode among the present invention is to use the regression analysis in the statistical method to analyze relation between physiological parameter and disease serious degree.
When the execute phase, physiological parameter normalization operation is to carry out codomain conversion and normalization according to physiological event intensity evolution curve, convert physiological parameter to same standard of comparison, physiological event intensity evolution curve is set up according to the event detecting method that is evolved to the basis with incident intensity or other tool method of describing the evolution of incident intensity.
Obtain the step of illness historical data and utilize the illness historical data to set up that the step of suitable mathematical model can realize with illness model analysis module.Wherein when medical personnel had feedback as a result to the suitableeest mathematical model, illness model analysis module was carried out the oneself and is adjusted mechanism, and it is suitably to adjust the suitableeest mathematical model according to feedback as a result that this oneself adjusts mechanism.Carrying out physiological parameter normalization operation can realize with physiological parameter normalization module to obtain normalized physiological parameter.The suitableeest mathematical model of normalized physiological parameter substitution can be realized with the disease serious degree evaluation module with the energy value that calculates illness.The probit value that is selected according to illness energy value and illness calculates preferred value and can realize with illness precedence evaluation module.
And, for achieving the above object, the invention provides a kind of disease serious degree ordering system, it is characterized in that, comprise: an illness model analysis module, utilize a training mode and the historical data of the illness that obtains from an illness historical data base to set up the suitableeest mathematical model of this illness; One physiology parameter normalization module is carried out physiology parameter normalization operation to obtain a plurality of normalized physiological parameters with a plurality of physiological parameters;
One disease serious degree evaluation module calculating an energy value of this illness, and is analyzed the development trend of this illness with this suitableeest mathematical models of those normalized these illnesss of physiological parameter substitution; And an illness precedence evaluation module, be selected probit value to calculate a preferred value of this illness according to one of this energy value of this illness and this illness, when a plurality of illnesss occur simultaneously, judge a suggestion processing sequence with this preferred value of this illness.
And, for achieving the above object, the present invention also provides a kind of recording medium, the computer program code that its logger computer can read, this computer program code makes a computing machine carry out the disease serious degree ordering, comprise: a training stage is provided, and this training stage comprises: the historical data that (a) obtains this illness from this illness historical data base; And the historical data of (b) utilizing this illness is set up the suitableeest mathematical model of this illness; One execute phase was provided, and this execute phase comprises: (c) carry out physiology parameter normalization operation to obtain a plurality of normalized physiological parameters; (d) with this suitableeest mathematical models of those normalized these illnesss of physiological parameter substitution to calculate an energy value of this illness; And (e) be selected the preferred value that probit value calculates this illness according to one of this energy value of this illness and this illness.
Use the present invention and can judge the illness that a plurality of physiological parameter of polymerization is presented, the severity and the development trend of assessment illness when several illnesss occur simultaneously, are judged the suggestion processing sequence.
Describe the present invention below in conjunction with the drawings and specific embodiments, but not as a limitation of the invention.
Description of drawings
For above and other objects of the present invention, feature, advantage and embodiment can be become apparent, appended graphic being described in detail as follows:
Fig. 1 illustrates the disease serious degree ordering method process flow diagram according to a preferred embodiment of the present invention.
Fig. 2 is according to the foundation of a preferred embodiment of the present invention process flow diagram of suitable mathematical model.
Fig. 3 carries out physiological parameter normalization schematic flow sheet according to a preferred embodiment of the present invention.
Fig. 4 illustrates the suitableeest mathematical model synoptic diagram according to a preferred embodiment of the present invention.
Fig. 5 illustrates the general architecture synoptic diagram according to the main functional modules of a preferred embodiment of the present invention.
Wherein, Reference numeral:
110: obtain illness historical data 350: the physiological event intensity evolution after the normalization
120: set up the suitableeest mathematical model curve
160: start disease serious degree ordering 410: slope
170: carry out physiological parameter normalization 510: the illness historical data base
180: calculate illness energy value 520: illness model analysis module
190: calculate preferred value 530: physiological parameter normalization module
210: select training mode 540: the disease serious degree evaluation module
220: produce and adjust mathematical model 550: illness precedence evaluation module
230: whether mathematical model meets expected results 561: the illness historical data
240: whether reliability is higher than setting value 562: the suitableeest mathematical model
250: set up the suitableeest mathematical model 563: physiological parameter
310: the physiological event intensity before the normalization drills 564: regular physiological parameter
Advance curve 565: energy value
330: the physiological event intensity 566 after the codomain conversion: probit value
Evolution curve 567: preferred value
568: feedback as a result
Embodiment
Please refer to Fig. 1, Fig. 1 illustrates the disease serious degree ordering method process flow diagram according to a preferred embodiment of the present invention.This disease serious degree ordering method provides training stage and execute phase.Training stage comprises step 110, obtains the illness historical data from the illness historical data base, and step 120, utilizes the illness historical data to set up the suitableeest mathematical model.Execute phase comprises step 160, start the disease serious degree ordering, step 170, carry out physiological parameter normalization operation to obtain normalized physiological parameter, step 180, calculating the illness energy value, and step 190 is calculated preferred value according to the probit value that is selected of illness energy value and illness with the suitableeest mathematical model of normalized physiological parameter substitution.
Please be simultaneously with reference to Fig. 1 and Fig. 5, Fig. 5 illustrates the general architecture synoptic diagram according to the main functional modules of a preferred embodiment of the present invention.Step 110 obtains the illness historical data from the illness historical data base, and step 120, and utilizing the illness historical data to set up the suitableeest mathematical model is to realize with illness model analysis module 520.Step 160 starts the disease serious degree ordering, and step 170, and carrying out physiological parameter normalization operation is to realize with physiological parameter normalization module 530 to obtain normalized physiological parameter.Step 180 is to realize with disease serious degree evaluation module 540 the suitableeest mathematical model of normalized physiological parameter substitution to calculate the illness energy value.Step 190, to calculate preferred value be to realize with illness precedence evaluation module 550 according to the probit value that is selected of illness energy value and illness.
Illness model analysis module 520 at first obtains illness historical data 561 from illness historical data base 510, illness historical data base 510 has been collected and has been come from medical personnel's past in a large number to the diagnosis and treatment record of various illnesss, relevant case, relevant information and each medical field expertise, for example the content of diagnosis and treatment records can comprise systolic pressure as patient be lower than 80 or diastolic pressure be lower than 50, and respiration rate was greater than 30 o'clock, be diagnosed as doubtful acidosic illness, severity is a moderate, at that time the medical measure taked of medical personnel.Illness model analysis module 520 is utilized the suitableeest mathematical model 562 of associated conditions historical data 561 foundation that obtains from illness historical data base 510, this the suitableeest mathematical model 562 can present the relation between a plurality of physiological parameters and illness, and physiological parameter is numerical value such as patient's respiration rate, systolic pressure, diastolic pressure.Therefore, illness historical data 561 is integrated and inquired into to illness model analysis module 520 to find one to be fit to present the mathematical model that concerns between a plurality of physiological parameters and illness, and this mathematical model is set up becomes the suitableeest mathematical model 562.Wherein when medical personnel when using the suitableeest mathematical model 562 that feedback 568 is as a result arranged, illness model analysis module 520 can be carried out the oneself and be adjusted mechanism, it is suitably to adjust the suitableeest mathematical model 562 according to feedback as a result 568 that this oneself adjusts mechanism.When a plurality of physiological parameters occur simultaneously, can utilize the suitableeest mathematical model 562 to present the illness that a plurality of physiological parameter of polymerization is presented.
Physiological parameter normalization module 530 is carried out physiological parameter normalization operation to obtain regular physiological parameter 564 at physiological parameter 563.Disease serious degree evaluation module 540 is with the energy value 565 of the regular the suitableeest mathematical model 562 of physiological parameter 564 substitutions to calculate illness, assess the severity of illness with the notion of this energy value 565, in addition, can further utilize the suitableeest mathematical model 562 to analyze the future development trend of illness.Illness precedence evaluation module 550 utilizes the probit value 566 that is selected of energy value 565 and illness to calculate preferred value 567, when a plurality of illnesss occur simultaneously, can utilize the preferred value 567 of each illness to judge the suggestion processing sequence.Being selected probit value 566 and can being registered to earlier in the illness historical data base 510 before of each illness, via inquiry illness historical data base 510 learn illness be selected probit value 566 after, a kind of computing method of preferred value 567 be the energy value 565 with illness multiply by being selected probit value 566 of illness and the product that obtains as preferred value 567, judge the suggestion processing sequence with the preferred value 567 of each illness again, for example when a plurality of illnesss occur simultaneously, judge that the suggestion processing sequence of the illness that preferred value is the highest is 1.
Please refer to Fig. 2, Fig. 2 is according to the foundation of a preferred embodiment of the present invention process flow diagram of suitable mathematical model.Setting up, the step of suitable mathematical model comprises: step 210, select a kind of training mode, step 220 is utilized training mode to produce and is adjusted the mathematical model of illness, step 230, differentiate this mathematical model and whether meet expected result, step 220, whether the reliability of differentiating this mathematical model is higher than setting value, and step 250, when reliability is higher than setting value, set up the suitableeest mathematical model of illness.Wherein, when the mathematical model of illness meets expected results, just can carry out the reliability analysis, when the mathematical model of illness does not meet expected results, can get back to step 210, reselect a kind of new training mode.When the reliability analysis is lower than setting value, can get back to step 210, reselect a kind of new training mode.Training mode can be the technology of statistical method, mathematical method, artificial intelligence approach or other tool Training Capability, the regression analysis of lifting in the statistical method is the example explanation, use the relation between regression analysis analysis physiological parameter and disease serious degree, when a kind of illness can be by three physiological parameter X
1, X
2, and X
3During judgement, with disease serious degree letter formula HP (CE)=α X
1+ β X
2+ γ X
3Expression if severity is represented with 1 to 3 to heavy by light, uses a large amount of historical datas of this illness can obtain physiological parameter X
1, X
2, and X
3Related coefficient α, β and γ, at this moment, the life cycle fluctuations of this disease serious degree just can be described with this disease serious degree letter formula HP that tries to achieve, then, when this disease serious degree letter formula HP meets expected results, property that efficiency confirmed by the reliability analysis when reliability is higher than setting value, just is established as this disease serious degree letter formula HP the suitableeest mathematical model of illness.
Please refer to Fig. 3, Fig. 3 is for carrying out physiological parameter normalization schematic flow sheet according to a preferred embodiment of the present invention.Physiological parameter normalization operation is to carry out codomain conversion and normalization according to physiological event intensity evolution curve, convert physiological parameter to same standard of comparison, physiological event intensity evolution curve is set up according to the event detecting method that is evolved to the basis with incident intensity or other tool method of describing the evolution of incident intensity.The event detecting method that is evolved to the basis with incident intensity can be converted into the physiological event original value physiological event intensity level, the physiological event original value is the numerical value that records from medical monitoring instrument, be converted into the physiological event intensity level by physiological event intensity evolution curve, can be so as to filtering the order of severity and a development trend of true and false physiological event, detection physiological event up to process and judgement physiological event.When filtering true and false physiological event, have only physiological parameter when physiological event to reach or during greater than the caution value set according to the trigger point rule, just need to send Event triggered and notify.
Please be simultaneously with reference to Fig. 1 and Fig. 4, Fig. 4 illustrates the suitableeest mathematical model synoptic diagram according to a preferred embodiment of the present invention.For example the suitableeest mathematical model when an illness is disease serious degree letter formula HP (CE)=X1+1.5X2+X3, and numerical value 1 is defined as slight serious, it is serious that numerical value 2 is defined as moderate, numerical value 3 is defined as severe when serious, among this disease serious degree letter formula HP that regular physiological parameter X1, X2 that will be obtained by step 170 and X3 substitution are obtained by step 120 to calculate this illness at that time energy value, assess the severity of illness with this energy value, and, can further utilize the slope 410 of this point to analyze the future development trend of illness.
By the invention described above preferred embodiment as can be known, use the illness that this disease serious degree ordering method can judge that a plurality of physiological parameter of polymerization is presented, the severity and the development trend of assessment illness, when a plurality of illnesss occur simultaneously, judge the suggestion processing priority, assist medical operation personage to take suitable countermeasure.
Certainly; the present invention also can have other various embodiments; under the situation that does not deviate from spirit of the present invention and essence thereof; those of ordinary skill in the art work as can make various corresponding changes and distortion according to the present invention, but these corresponding changes and distortion all should belong to the protection domain of the appended claim of the present invention.
Claims (22)
1. a disease serious degree ordering method utilizes a plurality of physiological parameters and the illness of an illness historical data base to judge that those physiological parameters are presented, and assesses the severity of this illness, it is characterized in that this method comprises:
One training stage was provided, and wherein this training stage comprises:
(a) obtain the historical data of this illness from this illness historical data base; And
(b) utilize the historical data of this illness to set up the suitableeest mathematical model of this illness;
One execute phase was provided, and wherein this execute phase comprises:
(c) carry out physiology parameter normalization operation to obtain a plurality of normalized physiological parameters;
(d) with this suitableeest mathematical models of those normalized these illnesss of physiological parameter substitution to calculate an energy value of this illness; And
(e) be selected the preferred value that probit value calculates this illness according to one of this energy value of this illness and this illness.
2. disease serious degree ordering method according to claim 1 is characterized in that, this illness historical data base is collected and come from medical personnel to the diagnosis and treatment record of various illnesss, relevant case, relevant information and each medical field expertise.
3. disease serious degree ordering method according to claim 1 is characterized in that, this preferred value of this illness multiply by this illness with this energy value of this illness this be selected probit value and a product.
4. disease serious degree ordering method according to claim 1 is characterized in that, step (d) also comprises the development trend of analyzing this illness.
5. disease serious degree ordering method according to claim 1 is characterized in that, when a plurality of illnesss occurred simultaneously, step (e) also comprises with this preferred value of this illness judged a suggestion processing sequence.
6. disease serious degree ordering method according to claim 1 is characterized in that, the suitableeest mathematical model that step (b) utilizes the historical data of this illness to set up this illness comprises:
Select a training mode;
Utilize this training mode to produce and adjust a mathematical model of this illness;
When this mathematical model of this illness meets an expected results, carry out a reliability analysis; And
When this reliability analysis is higher than a setting value, set up this suitableeest mathematical model of this illness.
7. disease serious degree ordering method according to claim 6 is characterized in that, also comprises when this mathematical model of this illness does not meet this expected results, reselects a new training mode.
8. disease serious degree ordering method according to claim 6 is characterized in that, also comprises when this reliability analysis is lower than this setting value, reselects a new training mode.
9. disease serious degree ordering method according to claim 6 is characterized in that, this training mode is the technology of a statistics method, a mathematical method, an artificial intelligence approach or other tool Training Capability.
10. disease serious degree ordering method according to claim 9 is characterized in that, this training mode is to use a regression analysis of this statistical method with the relation between the severity of analyzing those physiological parameters and this illness.
11. disease serious degree ordering method according to claim 1, it is characterized in that, this physiological parameter normalization operation in the step (c) is for carrying out codomain conversion and normalization according to director's part intensity evolution in all one's life curve, convert those physiological parameters to same standard of comparison, this physiological event intensity evolution curve is by being set up according to event detecting method or other tool method of describing the evolution of incident intensity that is evolved to the basis with incident intensity.
12. disease serious degree ordering method according to claim 1, it is characterized in that, step (b) also comprises when medical personnel have a feedback as a result to this suitableeest mathematical model of this illness, carry out an oneself and adjust mechanism, it is suitably to adjust this suitableeest mathematical model of this illness according to this feedback as a result that this oneself adjusts mechanism.
13. disease serious degree ordering method according to claim 1 is characterized in that, this energy value of this illness is in order to assess the severity of this illness.
14. a disease serious degree ordering system is characterized in that, comprises:
One illness model analysis module, utilize a training mode and the historical data of the illness that obtains from an illness historical data base to set up the suitableeest mathematical model of this illness;
One physiology parameter normalization module is carried out physiology parameter normalization operation to obtain a plurality of normalized physiological parameters with a plurality of physiological parameters;
One disease serious degree evaluation module calculating an energy value of this illness, and is analyzed the development trend of this illness with this suitableeest mathematical models of those normalized these illnesss of physiological parameter substitution; And
One illness precedence evaluation module is selected probit value to calculate a preferred value of this illness according to one of this energy value of this illness and this illness, when a plurality of illnesss occur simultaneously, judges a suggestion processing sequence with this preferred value of this illness.
15. disease serious degree ordering system according to claim 14 is characterized in that, this illness historical data base is collected and is come from medical personnel to the diagnosis and treatment record of various illnesss, relevant case, relevant information and each medical field expertise.
16. disease serious degree ordering system according to claim 14 is characterized in that, this preferred value of this illness multiply by this illness with this energy value of this illness this be selected probit value and a product.
17. disease serious degree ordering system according to claim 14 is characterized in that, this training mode is the technology of a statistics method, a mathematical method, an artificial intelligence approach or other tool Training Capability.
18. disease serious degree ordering system according to claim 14 is characterized in that, this training mode is to use a regression analysis of this statistical method with the relation between the severity of analyzing those physiological parameters and this illness.
19. disease serious degree ordering system according to claim 14, it is characterized in that, this physiological parameter normalization operation system carries out codomain conversion and normalization according to director's part intensity evolution in all one's life curve, convert those physiological parameters to same standard of comparison, this physiological event intensity evolution curve is by being set up according to event detecting method or other tool method of describing the evolution of incident intensity that is evolved to the basis with incident intensity.
20. disease serious degree ordering system according to claim 14, it is characterized in that, when medical personnel have a feedback as a result to this suitableeest mathematical model of this illness, this illness model analysis module is carried out an oneself and is adjusted mechanism, and it is suitably to adjust this suitableeest mathematical model of this illness according to this feedback as a result that this oneself adjusts mechanism.
21. disease serious degree ordering system according to claim 14 is characterized in that, this energy value of this illness is in order to assess the severity of this illness.
22. the computer program code that a recording medium, its logger computer can read, this computer program code make a computing machine carry out the disease serious degree ordering, it is characterized in that comprise: a training stage is provided, and this training stage comprises:
(a) obtain the historical data of this illness from this illness historical data base; And
(b) utilize the historical data of this illness to set up the suitableeest mathematical model of this illness;
One execute phase was provided, and this execute phase comprises:
(c) carry out physiology parameter normalization operation to obtain a plurality of normalized physiological parameters;
(d) with this suitableeest mathematical models of those normalized these illnesss of physiological parameter substitution to calculate an energy value of this illness; And
(e) be selected the preferred value that probit value calculates this illness according to one of this energy value of this illness and this illness.
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| CNA2007101985180A CN101458738A (en) | 2007-12-11 | 2007-12-11 | Disease serious degree ordering method, system thereof and recording medium |
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| CNA2007101985180A CN101458738A (en) | 2007-12-11 | 2007-12-11 | Disease serious degree ordering method, system thereof and recording medium |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102360405A (en) * | 2011-08-25 | 2012-02-22 | Tcl集团股份有限公司 | Remote medical health monitoring data submitting and sorting method and system |
| CN103440421A (en) * | 2013-08-30 | 2013-12-11 | 上海普之康健康管理有限公司 | Medical data processing method and system |
| CN108573752A (en) * | 2018-02-09 | 2018-09-25 | 上海米因医疗器械科技有限公司 | A kind of method and system of the health and fitness information processing based on healthy big data |
-
2007
- 2007-12-11 CN CNA2007101985180A patent/CN101458738A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102360405A (en) * | 2011-08-25 | 2012-02-22 | Tcl集团股份有限公司 | Remote medical health monitoring data submitting and sorting method and system |
| CN103440421A (en) * | 2013-08-30 | 2013-12-11 | 上海普之康健康管理有限公司 | Medical data processing method and system |
| CN103440421B (en) * | 2013-08-30 | 2017-07-25 | 上海普之康健康管理有限公司 | medical data processing method and system |
| CN108573752A (en) * | 2018-02-09 | 2018-09-25 | 上海米因医疗器械科技有限公司 | A kind of method and system of the health and fitness information processing based on healthy big data |
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