CN110660485A - Method and device for acquiring influence of clinical index - Google Patents
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Abstract
The invention discloses a method and a device for acquiring the influence of clinical indexes, which comprises the following steps: establishing a sample set, wherein the sample set comprises a plurality of sample data, and the sample data comprises a prediction result and a plurality of clinical indexes; determining a clinical index to be analyzed from the plurality of clinical indexes; determining that the proportion corresponding to the clinical index to be analyzed in the sample data with the prediction result as the first result is the first proportion; determining that the proportion corresponding to the clinical index to be analyzed in the sample data with the prediction result as the second result is the second proportion; calculating an influence parameter of the clinical index to be analyzed according to the first proportion and the second proportion, and determining the influence of the clinical index to be analyzed on the prediction result according to the influence parameter; obtaining an influence parameter through statistical calculation; thereby analyzing the relevance and influence of the clinical indexes to be analyzed on the prediction result; thereby assisting the predictive model in interpreting the prediction.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for acquiring influence of clinical indexes.
Background
In medicine, the importance of clinical data is self evident. The majority of patients' situations may be directly or indirectly manifested by clinical data. In some cases, the combination of multiple clinical data may implicitly indicate certain conditions, or potential health risks, for the patient. For this case, it is difficult to find by manual data analysis.
The prediction model is established based on the current advanced technologies such as artificial intelligence, machine learning, big data analysis and the like, and clinical data are analyzed, so that diseases or potential risks can be discovered earlier than before, and treatment and rehabilitation are facilitated. Therefore, the application of the prediction model in the medical field has very important medical value.
However, a prediction model established based on some Machine learning algorithms, such as a prediction model of a Support Vector Machine algorithm (SVM) or a Naive Bayes algorithm (Naive Bayes), cannot provide an explanation for a prediction result while obtaining the prediction result, and cannot analyze influence and relevance of various input clinical indexes on the prediction result.
Disclosure of Invention
The invention provides a method and a device for acquiring the influence of clinical indexes.
In a first aspect, the present invention provides a method for obtaining influence of clinical indicators, comprising:
establishing a sample set, wherein the sample set comprises a plurality of sample data, and the sample data comprises a prediction result and a plurality of clinical indexes;
determining a clinical index to be analyzed from the plurality of clinical indexes;
determining that the proportion corresponding to the clinical index to be analyzed in the sample data with the prediction result as the first result is the first proportion; determining that the proportion corresponding to the clinical index to be analyzed in the sample data with the prediction result as the second result is the second proportion;
and calculating the influence parameter of the clinical index to be analyzed according to the first proportion and the second proportion, and determining the influence of the clinical index to be analyzed on the prediction result according to the influence parameter.
Preferably, the determining a clinical index to be analyzed from the plurality of clinical indexes comprises:
determining a specific numerical value of the clinical index as the clinical index to be analyzed.
Preferably, the calculating the influence parameter of the clinical index to be analyzed through the first proportion and the second proportion includes:
and dividing the quotient of the first proportion and the second proportion to be used as the influence parameter.
Preferably, the determining the influence of the clinical index to be analyzed on the prediction result according to the influence parameter comprises:
and when the influence parameter is larger than a preset critical value, the clinical index to be analyzed is considered to be positively correlated with the prediction result.
Preferably, the determining the influence of the clinical index to be analyzed on the prediction result according to the influence parameter comprises:
and when the influence parameter is smaller than a preset critical value, the clinical index to be analyzed is considered to be negatively correlated with the prediction result.
Preferably, the determining the influence of the clinical index to be analyzed on the prediction result according to the influence parameter comprises:
and when the influence parameter is equal to a preset critical value, the clinical index to be analyzed is considered to have no correlation with the prediction result.
Preferably, the method further comprises the following steps:
and when the numerical value of the first proportion or the second proportion is 0, calculating the expected numerical value of the first proportion or the second proportion through the normal distribution of the sample data in the sample set.
In a second aspect, the present invention provides an apparatus for obtaining influence of clinical index, comprising:
the system comprises a sample set model, a prediction model and a data processing model, wherein the sample set model is used for establishing a sample set, the sample set comprises a plurality of sample data, and the sample data comprises a prediction result and a plurality of clinical indexes;
an index determination module for determining a clinical index to be analyzed from the plurality of clinical indexes;
the proportion calculation module is used for determining that the proportion corresponding to the clinical index to be analyzed is a first proportion in the sample data of which the prediction result is a first result; determining that the proportion corresponding to the clinical index to be analyzed in the sample data with the prediction result as the second result is the second proportion;
and the index analysis module is used for calculating the influence parameter of the clinical index to be analyzed according to the first proportion and the second proportion and determining the influence of the clinical index to be analyzed on the prediction result according to the influence parameter.
In a third aspect, the invention provides a readable medium comprising executable instructions, which when executed by a processor of an electronic device, perform the method according to any of the first aspect.
In a fourth aspect, the present invention provides an electronic device, comprising a processor and a memory storing execution instructions, wherein when the processor executes the execution instructions stored in the memory, the processor performs the method according to any one of the first aspect.
The invention provides a method and a device for acquiring the influence of clinical indexes, which are used for acquiring influence parameters by statistically calculating the proportional relation between the clinical indexes to be analyzed and a prediction result; therefore, the relevance and the influence of the clinical indexes to be analyzed on the prediction result are analyzed through the influence parameters; therefore, the influence analysis can assist some prediction models to explain the prediction result, and the medical analysis value of the prediction result is improved.
Further effects of the above-mentioned unconventional preferred modes will be described below in conjunction with specific embodiments.
Drawings
In order to more clearly illustrate the embodiments or the prior art solutions of the present invention, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
Fig. 1 is a schematic flow chart of a method for obtaining influence of clinical indicators according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating another method for obtaining influence of clinical indicators according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of an apparatus for obtaining influence of clinical indicators according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail and completely with reference to the following embodiments and accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As previously known, the predictive models built based on certain machine learning algorithms belong to the black box model. That is, the predicted result is obtained and the explanation of the predicted result cannot be provided, so that the influence and relevance of the inputted various clinical indexes on the predicted result cannot be analyzed. This results in prediction results that are difficult to use effectively for further medical analysis.
Aiming at the situation, the invention provides a method and a device for acquiring the influence of clinical indexes.
Referring to fig. 1, a specific embodiment of the method for obtaining the influence of clinical index according to the present invention is shown. In this embodiment, the method includes the steps of:
In this embodiment, the influence of the clinical index is analyzed for the sample set to determine the influence and relevance of the specific clinical index to be analyzed on the prediction result. The sample set includes a plurality of sample data. Each sample data may in turn comprise a predicted outcome and a plurality of clinical indicators.
Specifically, each sample data may be clinical data corresponding to one patient, and the clinical data includes a plurality of clinical indicators such as blood pressure, blood sugar, heart rate, weight, and blood fat, and numerical values of the clinical indicators. And also includes a predicted outcome for the patient's clinical data. The outcome is predicted, for example, "whether there is a risk of heart disease".
In this embodiment, the predicted result is generally a binary result. Namely, the prediction result only has two cases of yes or no. In medicine, it is also commonly referred to as "positive" or "negative" two cases. In this embodiment, then "positive" may be referred to as a first result; "negative" is referred to as a second result.
And 102, determining a clinical index to be analyzed from the plurality of clinical indexes.
In this embodiment, the clinical index to be analyzed is specifically a clinical index with a specific numerical value. For example, a blood pressure index having a value of (150,100) mmHg can be determined as a clinical index to be analyzed, and its influence on the prediction result "presence or absence of heart disease risk" can be judged in the subsequent step.
And 103, determining that the ratio corresponding to the clinical index to be analyzed in the sample data with the prediction result as the first result is the first ratio.
All the sample data with the prediction result of "the first result", namely "the sample data with heart disease risk" are classified from the sample set. And calculating the proportion of the sample data with the blood pressure index numerical value of (150,100) mmHg in the sample data, and taking the proportion as a first proportion.
And 104, determining that the ratio corresponding to the clinical index to be analyzed in the sample data with the prediction result as the second result is the second ratio.
In the same manner, step 103, all the sample data with the prediction result of "the second result", that is, "without heart disease risk", are classified from the sample set in this step. And calculating the proportion of the sample data with the blood pressure index value of (150,100) mmHg in the sample data, and taking the proportion as a second proportion.
And 105, calculating the influence parameter of the clinical index to be analyzed according to the first proportion and the second proportion.
And 106, determining the influence of the clinical index to be analyzed on the prediction result according to the influence parameter.
In this embodiment, a quotient obtained by dividing the first ratio by the second ratio may be specifically used as the influence parameter. The larger the influence parameter is, the more influence of the clinical index to be analyzed on the prediction result is, i.e. the more easily the "heart disease risk" is caused.
According to the technical scheme, the beneficial effects of the embodiment are as follows: calculating the proportional relation between the clinical index to be analyzed and the prediction result through statistics to obtain an influence parameter; therefore, the relevance and the influence of the clinical indexes to be analyzed on the prediction result are analyzed through the influence parameters; therefore, the influence analysis can assist some prediction models to explain the prediction result, and the medical analysis value of the prediction result is improved.
Fig. 1 shows only a basic embodiment of the method of the present invention, and based on this, certain optimization and expansion can be performed, and other preferred embodiments of the method can also be obtained.
Fig. 2 shows another embodiment of the method for obtaining the influence of a clinical index according to the present invention. The embodiment is based on the foregoing embodiment, and performs a more detailed description and a certain degree of optimization on the calculation and analysis process of the influence force parameter. For convenience of explanation, the present embodiment will be described with reference to the following specific scenarios.
In this embodiment, it is determined that the clinical index to be analyzed is "age 39 years", and the corresponding prediction result is also "whether there is a risk of heart disease".
Of course, it should be understood that, in other related scenarios, the method described in this embodiment is also applicable to the method described in this embodiment, which includes the following steps:
In this embodiment, one sample data may be represented mathematically as follows:
f＝(f1，f2，f3...fm) (ii) a Wherein f represents sample data, f1～fmM clinical indicators in the sample data. Assume that the clinical index "age" referred to in this example is fjAnd j is more than or equal to 1 and less than or equal to m. In the present embodiment, the prediction result "first result" is represented by y being 1, and the prediction result "second result" is represented by y being 0.
Clinical index f in this examplejValue f ofj0(ii) a I.e. determine fj0Clinical index f ═ 39jAs clinical index to be analyzed.
And 204, determining that the ratio corresponding to the clinical index to be analyzed in the sample data with the prediction result as the second result is the second ratio.
In the present embodiment, the first ratio is represented as P (f)j＝fj01 |; the second ratio is expressed as P (f)j＝fj0|y＝0)。
Also in some cases, the sample set may not be comprehensive. In connection with the scenario of the present embodiment, there is just no sample data for a "39 year old patient". This will result in either the first ratio or the second ratio being 0 in value. In practice this is a violation of medical knowledge, since it is obviously unlikely that a "heart disease" will not occur in the very 39 years of age.
Therefore, to solve such sample errors, the expected value of the first ratio or the second ratio can be calculated according to the normal distribution of the sample data in the sample set in this embodiment.
In this embodiment, it is assumed that the "age" clinical indicators of all the first result sample data conform to a normal distribution with a mean value of 65 years and a standard deviation of 10 years; all second result sample data had "age" clinical scores that fit a normal distribution with a mean of 30 years and a standard deviation of 10 years.
The expected value of the first ratio is calculated as follows:
the expected value of the second ratio is calculated as follows:
and step 205, dividing the quotient of the first proportion and the second proportion to serve as the influence parameter.
In this embodiment, the influence parameter is expressed as follows:
and step 206, determining the influence of the clinical index to be analyzed on the prediction result according to the influence parameter.
In this embodiment, the determining the influence of the clinical indicator to be analyzed on the prediction result according to the influence parameter includes: and when the influence parameter is larger than a preset critical value, the clinical index to be analyzed is considered to be positively correlated with the prediction result. And when the influence parameter is smaller than a preset critical value, the clinical index to be analyzed is considered to be negatively correlated with the prediction result. And when the influence parameter is equal to a preset critical value, the clinical index to be analyzed is considered to have no correlation with the prediction result.
Specifically, the threshold value may be set to 1. When S isj> 1, this clinical index "age 39 years" enhances "heart disease risk". On the contrary, when SjIf < 1, it means "age 39 years" this clinical index reduces "heart disease risk". When S isj1 means that the clinical index "age 39 years" has no obvious correlation with "heart disease risk".
According to the technical scheme, on the basis of the foregoing embodiment, the present embodiment further has the following beneficial effects: the calculation and analysis process of the influence force parameter is disclosed in detail, and the estimation method in case of sample error is further solved; the whole technical scheme is more complete.
Fig. 3 shows an embodiment of the apparatus for obtaining influence of clinical index according to the present invention. The apparatus of this embodiment is a physical apparatus for performing the method described in fig. 1-2. The technical solution is essentially the same as that in the above embodiment, and the corresponding description in the above embodiment is also applicable to this embodiment. The device in this embodiment includes:
the sample set model 301 is configured to establish a sample set, where the sample set includes a plurality of sample data, and the sample data includes a predicted result and a plurality of clinical indicators.
An index determination module 302 configured to determine a clinical index to be analyzed from the plurality of clinical indexes.
The proportion calculation module 303 is configured to determine that, in sample data of which a prediction result is a first result, a proportion corresponding to the clinical index to be analyzed is a first proportion; and determining that the proportion corresponding to the clinical index to be analyzed in the sample data with the prediction result as the second result is the second proportion.
And the index analysis module 304 is configured to calculate an influence parameter of the clinical index to be analyzed according to the first ratio and the second ratio, and determine an influence of the clinical index to be analyzed on the prediction result according to the influence parameter.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. On the hardware level, the electronic device comprises a processor and optionally an internal bus, a network interface and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (peripheral component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 4, but that does not indicate only one bus or one type of bus.
And the memory is used for storing the execution instruction. In particular, a computer program that can be executed by executing instructions. The memory may include both memory and non-volatile storage and provides execution instructions and data to the processor.
In a possible implementation manner, the processor reads the corresponding execution instruction from the nonvolatile memory into the memory and then runs the corresponding execution instruction, and the corresponding execution instruction can also be obtained from other equipment, so as to form a device for obtaining the influence of the clinical index on a logic level. The processor executes the execution instructions stored in the memory to implement the method for obtaining the influence of the clinical index provided in any embodiment of the present invention by the executed execution instructions.
The method performed by the apparatus for obtaining influence of clinical index according to the embodiment of the present invention shown in fig. 3 may be applied to or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
An embodiment of the present invention further provides a readable storage medium, which stores an execution instruction, and when the stored execution instruction is executed by a processor of an electronic device, the electronic device can be caused to execute the method for acquiring the influence of a clinical index provided in any embodiment of the present invention, and is specifically configured to execute the method shown in fig. 1 or fig. 2.
The electronic device described in the foregoing embodiments may be a computer.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects.
The embodiments of the present invention are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only an example of the present invention, and is not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (10)
1. A method of obtaining the influence of a clinical index, comprising:
establishing a sample set, wherein the sample set comprises a plurality of sample data, and the sample data comprises a prediction result and a plurality of clinical indexes;
determining a clinical index to be analyzed from the plurality of clinical indexes;
determining that the proportion corresponding to the clinical index to be analyzed in the sample data with the prediction result as the first result is the first proportion; determining that the proportion corresponding to the clinical index to be analyzed in the sample data with the prediction result as the second result is the second proportion;
and calculating the influence parameter of the clinical index to be analyzed according to the first proportion and the second proportion, and determining the influence of the clinical index to be analyzed on the prediction result according to the influence parameter.
2. The method of claim 1, wherein determining the clinical index to be analyzed from the plurality of clinical indexes comprises:
and determining a specific numerical value of the clinical index as the clinical index to be analyzed.
3. The method of claim 1, wherein calculating the influence parameter of the clinical index to be analyzed according to the first ratio and the second ratio comprises:
and dividing the quotient of the first proportion and the second proportion to be used as the influence parameter.
4. The method of claim 3, wherein determining the influence of the clinical indicator to be analyzed on the prediction result according to the influence parameter comprises:
and when the influence parameter is larger than a preset critical value, the clinical index to be analyzed is considered to be positively correlated with the prediction result.
5. The method of claim 3, wherein determining the influence of the clinical indicator to be analyzed on the prediction result according to the influence parameter comprises:
and when the influence parameter is smaller than a preset critical value, the clinical index to be analyzed is considered to be negatively correlated with the prediction result.
6. The method of claim 3, wherein determining the influence of the clinical indicator to be analyzed on the prediction result according to the influence parameter comprises:
and when the influence parameter is equal to a preset critical value, the clinical index to be analyzed is considered to have no correlation with the prediction result.
7. The method according to any one of claims 1 to 6, further comprising:
when the numerical value of the first proportion or the second proportion is 0, calculating an expected numerical value of the first proportion or the second proportion through normal distribution of sample data in the sample set;
taking the expected value as the value of the first ratio or the second ratio.
8. An apparatus for obtaining influence of a clinical index, comprising:
the system comprises a sample set model, a prediction model and a data processing model, wherein the sample set model is used for establishing a sample set, the sample set comprises a plurality of sample data, and the sample data comprises a prediction result and a plurality of clinical indexes;
an index determination module for determining a clinical index to be analyzed from the plurality of clinical indexes;
the proportion calculation module is used for determining that the proportion corresponding to the clinical index to be analyzed is a first proportion in the sample data of which the prediction result is a first result; determining that the proportion corresponding to the clinical index to be analyzed in the sample data with the prediction result as the second result is the second proportion;
and the index analysis module is used for calculating the influence parameter of the clinical index to be analyzed according to the first proportion and the second proportion and determining the influence of the clinical index to be analyzed on the prediction result according to the influence parameter.
9. A readable medium comprising executable instructions which, when executed by a processor of an electronic device, cause the electronic device to perform the method of any of claims 1 to 7.
10. An electronic device comprising a processor and a memory storing execution instructions, the processor performing the method of any of claims 1-7 when the processor executes the execution instructions stored by the memory.
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