Patient information automatic processing system and method

Abstract

The present invention relates to the field of digital processing technology, and in particular to a system and method for automatically processing patient information; the method comprises the following steps: S1, obtaining first collected information, wherein the first collected information comprises historical case information; S2, screening a plurality of test patients that match the drug to be clinically tested based on the historical case information and key information of the drug to be clinically tested; S3, obtaining second collected information; S4, judging the safety level of each test patient based on the second collected information, wherein the safety level judged by the second processing module comprises low safety, medium safety and high safety, and for test patients judged to have low safety, stopping the clinical drug trial; S5, screening the information of each test patient during the clinical drug trial stage into routine data and key data; the present invention ensures the accuracy of the clinical drug trial and the safety of the test patients.

Description

Technical Field

[0001] The present invention relates to the field of digital processing technology, and in particular to a system and method for automatically processing patient information. Background Art

[0002] Clinical trials of drugs require systematic research on drugs on patients or healthy volunteers to confirm or test the clinical and pharmacological effects, adverse reactions, etc. of the drugs in order to ensure the safety and effectiveness of the drugs. Whether it is the selection and confirmation stage of patients or healthy volunteers, or the clinical trial stage of drugs, the information of the subjects needs to be collected and analyzed to ensure the safety of the subjects and verify the effectiveness of the drugs.

[0003] Chinese patent CN114187983A discloses a method and apparatus for grouping subjects for a clinical trial project, wherein the method comprises: receiving a target request, wherein the target request is used to request grouping of subjects for a target clinical trial project; responding to the target request, obtaining pre-configured parameters related to the target clinical trial project; and generating subject grouping results based on the pre-configured parameters. This patent solves the problem that human participation is required for subject enrollment and trial drug distribution during clinical trials, resulting in an inability to ensure the credibility and safety of clinical trials. It enables trial personnel to perform enrollment and drug distribution operations at the front desk. The entire process is completed within the system and is invisible to personnel in a black box, thus ensuring data reliability, avoiding human bias, and ensuring the credibility and safety of the entire clinical trial.

[0004] However, existing clinical trial projects do not monitor patient information throughout the entire process from the preparation stage to the end of the drug clinical trial, resulting in inaccurate patient information monitoring, which in turn affects the accuracy of drug clinical trial results and patient safety.

[0005] Therefore, there is an urgent need to provide a system and method for automatically processing patient information, which can ensure the accuracy of drug clinical trial results and the safety of test patients compared with existing technologies. Summary of the Invention

[0006] The present invention solves the above technical problems existing in the prior art and provides a system and method for automatically processing patient information.

[0007] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0008] A method for automatically processing patient information comprises the following steps:

[0009] S1. Acquire first collected information, where the first collected information includes historical case information;

[0010] S2. Screen multiple patients who are matched with the drug to be tested based on historical case information and key information of the drug to be tested;

[0011] S3. Obtain second collected information;

[0012] S4. Determine the safety level of each patient under investigation based on the second collected information. The safety level determined by the second processing module includes low safety, medium safety, and high safety. For patients determined to have low safety, the clinical drug trial is stopped.

[0013] S5. The information of each patient during the clinical drug trial phase is screened into routine data and key data.

[0014] Furthermore, J intervals are set during the clinical drug trial phase, and multiple set times are set within each interval. The test patients undergo a medical consultation at each set time to obtain a corresponding diagnosis report and BMI value. The second collected information includes the diagnosis report and BMI value.

[0015] Furthermore, in step S4, the corresponding safety judgment value is calculated based on the second collected information obtained during each interval, and the first safety threshold is set. and the second safety threshold , the first safety threshold is less than the second safety threshold, and the safety judgment value calculated each time is compared with 、 For comparison, specifically:

[0016] When the safety judgment value When the safety level of the patient is judged to be high;

[0017] when Safety judgment value When the safety level of the patient is judged to be moderate;

[0018] when When the safety judgment value is reached, the safety level of the patient under investigation is judged to be low.

[0019] Furthermore, the safety judgment value is calculated by the following formula:

[0020] ;

[0021] In the above formula, It represents the safety judgment value of each patient after the jth interval, where j ranges from 1 to J. represents the first weight value, represents the second weight value, represents the detection error coefficient of BMI value, Indicates the total number of set times included in each interval, n is 1 to N, It represents the average of the maximum and minimum BMI values corresponding to the age of the patient. Indicates the similarity value between the diagnostic report corresponding to the nth set time and the standard diagnostic report, Indicates the The similarity value between the diagnostic report corresponding to the set time and the standard diagnostic report.

[0022] Furthermore, 、 Calculated according to the following formula:

[0023] ;

[0024] ;

[0025] In the above formula, Indicates the BMI value of the patient before the clinical drug trial. Indicates the similarity between the historical case information of the tested patient and the standard diagnostic report.

[0026] Furthermore, in step S5, the specific method for screening is: screen out the similarity values between the diagnostic report of each test patient at each set time and the standard diagnostic report, and for each test patient, establish a change curve in the same coordinate system with the horizontal axis as the set time and the vertical axis as the similarity value. Each change curve and the horizontal axis form a change curve area, and the common area of the change curve area corresponding to the test patient who was consulted at the last set time is selected. The points falling in the common area are used as routine data for clinical drug evaluation, and the points not falling in the common area are used as key data for clinical drug evaluation.

[0027] Furthermore, 、 Calculated by the following formula:

[0028] ;

[0029] ;

[0030] In the above formula, Indicates the maximum BMI value corresponding to the age of the patient being tested. Indicates the minimum BMI value corresponding to the age of the patient being tested, It represents the average value of the similarity between all diagnostic reports and the standard diagnostic report during the jth interval.

[0031] Furthermore, for the subjects judged to have moderate safety, the dosage of the clinical drug trial in the next interval is reduced. The dosage of the clinical drug trial that needs to be reduced for the subjects in the next interval is calculated by the following formula:

[0032] ;

[0033] In the above formula, Indicates that the patients who were judged to have moderate safety were The amount of clinical drug trials that needs to be reduced during the interval, It indicates the dosage required for clinical drug trials in patients who are judged to have moderate safety during the first interval.

[0034] Furthermore, in step S2, key information of the drug to be clinically tested is obtained and set as the first key information; natural language processing technology is used to extract professional terms from the historical case information in the first collected information to form multiple second key information; a semantic similarity calculation method is used to calculate the semantic similarity between each second key information and the first key information to form multiple similarity values, and the multiple similarity values are clustered to form K clusters. A distance threshold is calculated based on all similarity values in the cluster where the minimum similarity value is located, and clusters whose distance from the center point of the cluster where the minimum similarity value is located does not exceed the distance threshold are screened out, and patients corresponding to all similarity values in the screened clusters are used as test patients; the distance threshold is calculated by the following formula:

[0035] ;

[0036] In the above formula, represents the distance threshold, The horizontal coordinate of the center point of the cluster where the minimum similarity value is located, and y represents the vertical coordinate of the center point of the cluster where the minimum similarity value is located. Indicates the horizontal coordinate of the i-th point in the cluster with the minimum similarity value, It represents the ordinate of the i-th point in the cluster where the minimum similarity value is located, and I represents the total number of points in the cluster where the minimum similarity value is located.

[0037] A patient information automatic processing system includes a data acquisition module, a data processing module and a data screening module. The data acquisition module includes a first acquisition module and a second acquisition module. The data processing module includes a first processing module and a second processing module. The first acquisition module is connected to the first processing module, the second acquisition module is connected to the second processing module, and the second acquisition module is connected to the data screening module.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The present invention is based on the first acquisition module and the first processing module to screen the most suitable test patients for clinical drugs. In the process of conducting clinical drug trials on the test patients, multiple interval times are set according to the second acquisition module and the second processing module. Multiple set times are also set in each interval time. Each test patient is required to undergo medical consultation and obtain BMI values at each set time to obtain a corresponding diagnosis report. At the same time, a standard diagnosis report is set according to the clinical drug being tested. The diagnosis report after the medical consultation of each test patient is compared and analyzed with the standard diagnosis report to obtain a similarity value. At each interval time, the safety level of each test patient is judged according to the similarity value and BMI value obtained in the interval time. For test patients judged to have low safety, their clinical drug trials are stopped; finally, the data processed in the second processing module is classified into routine data and key data by the data screening module, which is used for consideration in the subsequent clinical trial evaluation stage of the drug. While ensuring the accuracy of the results of the clinical drug trials, the present invention can also ensure the safety of patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a structural block diagram of the system of the present invention.

[0041] Figure 2 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0042] The technical solution of the present invention will be clearly described below in conjunction with the accompanying drawings. Obviously, the described embodiments are not all embodiments of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0043] like Figure 1 As shown, the present invention provides an automatic patient information processing system, including a data acquisition module, a data processing module and a data screening module. The data acquisition module includes a first acquisition module and a second acquisition module. The data processing module includes a first processing module and a second processing module. The first acquisition module is connected to the first processing module, the second acquisition module is connected to the second processing module, and the second processing module is connected to the data screening module.

[0044] The first acquisition module is used to collect patient information during the patient screening phase, and the first processing module is used to process the patient information collected by the first acquisition module. The first processing module obtains multiple test patients that match the symptoms treated by the clinical drug to be tested. The second acquisition module is used to collect patient information from the test patients screened by the first processing module, and the second processing module is used to process the patient information collected by the second acquisition module. The second processing module is used to determine the safety of the test patients during the drug clinical trial phase. The data screening module uses the data processed by the second processing module throughout the entire test process to screen out routine data and endpoint data, providing support for the subsequent effectiveness evaluation phase of the clinical drug.

[0045] like Figure 2 As shown, the present invention also provides a method for automatically processing patient information, comprising the following steps:

[0046] S1. Obtain the first collected information. The specific method is: set a patient set to be screened, the patient set to be screened includes a large number of patients, and collect patient information of each patient in the patient set to be screened, which is the first collected information. The first collected information includes basic information and historical case information. The basic information includes name, gender, home address, mobile phone number, etc.

[0047] S2. Based on the first collected information, a plurality of test patients matching the symptoms treated by the clinical drug to be tested are obtained, specifically: obtaining key information of the drug to be tested clinically, set as first key information, the first key information including the name, efficacy, ingredients, indications, and other information of the clinical drug to be tested; using natural language processing technology to extract professional terms from the historical case information in the first collected information to form a plurality of second key information, the professional terms extracted from the historical case information of each patient forming one key information, the second key information including disease-related terms in the patient's historical case, the name of the drug used, the ingredients of the drug, the indications of the drug, and other information; using a semantic similarity calculation method to calculate the semantic similarity between each second key information and the first key information to form a plurality of similarity values, performing cluster analysis on the plurality of similarity values to form K clusters, calculating a distance threshold based on all similarity values within the cluster where the smallest similarity value is located, screening out clusters whose distance from the center point of the cluster where the smallest similarity value is located does not exceed the distance threshold, and taking patients corresponding to all similarity values within the screened-out clusters as test patients; the distance threshold is calculated by the following formula:

[0048] ;

[0049] In the above formula, represents the distance threshold, The horizontal coordinate of the center point of the cluster where the minimum similarity value is located, and y represents the vertical coordinate of the center point of the cluster where the minimum similarity value is located. Indicates the horizontal coordinate of the i-th point in the cluster with the minimum similarity value, It represents the ordinate of the i-th point in the cluster where the minimum similarity value is located, and I represents the total number of points in the cluster where the minimum similarity value is located.

[0050] S3. Obtain the second collected information. The specific method is: for each subject patient, a medical consultation is required at set intervals during the clinical drug trial to obtain a corresponding diagnosis report and the subject patient's BMI value (standard body mass index). The diagnosis report includes multiple test indicators, each of which is associated with the indication of the clinical drug. At each set time, the patient information of each subject patient is collected, which is the second collected information. The second collected information includes the diagnosis report and BMI value.

[0051] S4. During the clinical drug trial, J intervals are set, each interval contains multiple set times, and each interval contains at least three set times. After each interval, a safety judgment value is calculated based on all the second collected information obtained during the interval, and the safety of each test patient after the interval is judged according to the safety judgment value.

[0052] The specific method is:

[0053] A standard diagnostic report is set up. The multiple indicators included in the standard diagnostic report correspond to the various test indicators in the diagnostic report after each consultation at a set time. The various indicators in the standard diagnostic report are set according to human medical standards.

[0054] The safety judgment value of each patient after the jth interval is calculated according to the following formula:

[0055] ;

[0056] ;

[0057] ;

[0058] In the above formula, represents the safety judgment value of each patient after the jth interval time, represents the first weight value, represents the second weight value, represents the detection error coefficient of BMI value, Indicates the total number of set times included in each interval, n is 1 to N, It represents the average of the maximum and minimum BMI values corresponding to the age of the patient. Indicates the similarity value between the diagnostic report corresponding to the nth set time and the standard diagnostic report, Indicates the The similarity value between the diagnostic report corresponding to the set time and the standard diagnostic report; Indicates the BMI value of the patient before the clinical drug trial. Indicates the similarity between the historical case information of the patient under investigation and the standard diagnostic report, where j ranges from 1 to J.

[0059] 、 、 The specific method is as follows: using the GloVe algorithm, the corresponding diagnosis report is compared and analyzed with the standard diagnosis report to obtain the similarity of the two diagnosis reports, which is a similarity value.

[0060] The safety of the patients in the clinical trial of the drug is judged at different levels, including high safety, medium safety and low safety. The specific method for judging the safety of each patient after the jth interval time according to the safety judgment value is as follows: a first safety threshold and a second safety threshold are set, the first safety threshold is less than the second safety threshold, and the first safety threshold is recorded as , the second safety threshold is recorded as ,Will and 、 For comparison, specifically:

[0061] when When the safety level of the patient is judged to be high.

[0062] when When the safety level of the patient is judged to be moderate.

[0063] when When the safety level of the patient is judged to be low.

[0064] 、 Calculated by the following formula:

[0065] ;

[0066] ;

[0067] In the above formula, Indicates the maximum BMI value corresponding to the age of the patient being tested. Indicates the minimum BMI value corresponding to the age of the patient being tested, It represents the average value of the similarity between all diagnostic reports and the standard diagnostic report during the jth interval.

[0068] For patients judged to have low safety, clinical drug trials for these patients will be stopped. For patients judged to have moderate safety, their clinical drug trial dosage will be reduced during the next interval. The dosage of clinical drug trials that needs to be reduced for these patients during the next interval is calculated using the following formula:

[0069] ;

[0070] In the above formula, Indicates that the patients who were judged to have moderate safety were The amount of clinical drug trials that needs to be reduced during the interval, It indicates the dosage required for clinical drug trials in patients who are judged to have moderate safety during the first interval.

[0071] S5. The similarity values between the diagnostic reports of each test patient obtained at each set time and the standard diagnostic reports are screened out, and for each test patient, a change curve is established in the same coordinate system with the horizontal axis being the set time and the vertical axis being the similarity value. Each change curve and the horizontal axis form a change curve area. For the change curve area corresponding to the test patient who was consulted at the last set time, the common area of these change curve areas is selected, and the points falling in the common area are used as routine data for clinical drug evaluation, and the points not falling in the common area are used as key data for clinical drug evaluation. The data screening module outputs routine data and key data, which is helpful to better utilize non-routine data in the subsequent clinical drug effectiveness evaluation stage and improve the efficiency and accuracy of drug effectiveness evaluation.

[0072] The present invention is for screening the most suitable test patients for clinical drugs. In the process of conducting clinical drug trials on test patients, multiple interval times are set, and multiple set times are also set within each interval time. Each test patient is required to undergo medical consultation and obtain BMI values at each set time to obtain a corresponding diagnosis report. At the same time, a standard diagnosis report is set according to the clinical drug being conducted. The diagnosis report after each test patient's medical consultation is compared and analyzed with the standard diagnosis report to obtain a similarity value. At each interval time, the safety level of each test patient is judged based on the similarity value and BMI value obtained within the interval time. For test patients judged to have low safety, their clinical drug trials are stopped; finally, the data processed in the second processing module is classified into routine data and key data for consideration in the subsequent clinical trial evaluation stage of the drug. While ensuring the accuracy of the results of the clinical drug trials, the present invention can also ensure the safety of patients.

[0073] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, rather than to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions of the technical solution of the present invention by ordinary technicians in this field do not deviate from the essence and scope of the technical solution of the present invention.

Claims

1. A method for automatically processing patient information, characterized in that: The following steps are involved: S1. Acquire first collected information, where the first collected information includes historical case information; S2. Screen multiple patients who are matched with the drug to be tested based on historical case information and key information of the drug to be tested; S3. Obtain second collected information; S4. Based on the second collected information, a safety level is determined for each patient according to a safety judgment value. The determined safety levels include low safety, medium safety, and high safety. For patients determined to have low safety, the clinical drug trial is stopped. The safety judgment value is calculated using the following formula: ； In the above formula, It represents the safety judgment value of each patient after the jth interval, where j ranges from 1 to J. represents the first weight value, represents the second weight value, represents the detection error coefficient of BMI value, Indicates the total number of set times included in each interval, n is 1 to N, It represents the average of the maximum and minimum BMI values corresponding to the age of the patient. Indicates the similarity value between the diagnostic report corresponding to the nth set time and the standard diagnostic report, Indicates the The similarity value between the diagnostic report corresponding to the set time and the standard diagnostic report; S5. The information of each patient during the clinical drug trial phase is screened into routine data and key data.

2. A method for automatically processing patient information according to claim 1, characterized in that: During the clinical drug trial phase, J intervals are set, and multiple set times are set within each interval. The test patients undergo a medical consultation at each set time to obtain a corresponding diagnosis report and BMI value. The second collected information includes the diagnosis report and BMI value.

3. A method for automatically processing patient information according to claim 2, characterized in that: In step S4, the corresponding safety judgment value is calculated based on the second collected information obtained during each interval, and the first safety threshold is set. and the second safety threshold , the first safety threshold is less than the second safety threshold, and the safety judgment value calculated each time is compared with 、 For comparison, specifically: When the safety judgment value When the safety level of the patient is judged to be high; when Safety judgment value When the safety level of the patient is judged to be moderate; when When the safety judgment value is reached, the safety level of the patient under investigation is judged to be low.

4. A method for automatically processing patient information according to claim 3, characterized in that: 、 Calculated according to the following formula: ； ； In the above formula, Indicates the BMI value of the patient before the clinical drug trial. Indicates the similarity between the historical case information of the tested patient and the standard diagnostic report.

5. A method for automatically processing patient information according to claim 3, characterized in that: In step S5, the specific method for screening is: screen out the similarity values between the diagnostic report of each test patient at each set time and the standard diagnostic report, and for each test patient, establish a change curve with the horizontal axis as the set time and the vertical axis as the similarity value in the same coordinate system, each change curve and the horizontal axis form a change curve area, and select the common area of the change curve area corresponding to the test patient who was consulted at the last set time, and the points falling in the common area are used as routine data for clinical drug evaluation, and the points not falling in the common area are used as key data for clinical drug evaluation.

6. A method for automatically processing patient information according to claim 3, characterized in that: 、 Calculated by the following formula: ； ； In the above formula, Indicates the maximum BMI value corresponding to the age of the patient being tested. Indicates the minimum BMI value corresponding to the age of the patient being tested, It represents the average value of the similarity between all diagnostic reports and the standard diagnostic report during the jth interval.

7. A method for automatically processing patient information according to claim 3, characterized in that: For patients judged to be of moderate safety, the dosage of clinical drug trials in the next interval is reduced. The dosage of clinical drug trials that needs to be reduced for the patients in the next interval is calculated by the following formula: ； In the above formula, Indicates that the patients who were judged to have moderate safety were The amount of clinical drug trials that needs to be reduced during the interval, It indicates the dosage required for clinical drug trials in patients who are judged to have moderate safety during the first interval.

8. A method for automatically processing patient information according to claim 1, characterized in that: In step S2, key information of the drug to be clinically tested is obtained and set as the first key information; natural language processing technology is used to extract professional terms from the historical case information in the first collected information to form multiple second key information; The semantic similarity calculation method is used to calculate the semantic similarity between each second key information and the first key information to form multiple similarity values. The multiple similarity values are clustered and analyzed to form K clusters. The distance threshold is calculated based on all similarity values in the cluster with the minimum similarity value. The clusters whose distance from the center point of the cluster with the minimum similarity value does not exceed the distance threshold are screened out, and the patients corresponding to all similarity values in the screened clusters are used as test patients. The distance threshold is calculated by the following formula: ； In the above formula, represents the distance threshold, The horizontal coordinate of the center point of the cluster where the minimum similarity value is located, and y represents the vertical coordinate of the center point of the cluster where the minimum similarity value is located. Indicates the horizontal coordinate of the i-th point in the cluster with the minimum similarity value, It represents the ordinate of the i-th point in the cluster where the minimum similarity value is located, and I represents the total number of points in the cluster where the minimum similarity value is located.

9. A patient information automatic processing system, characterized in that: The method is carried out using a patient information automatic processing method according to any one of claims 1 to 8, comprising a data acquisition module, a data processing module and a data screening module, wherein the data acquisition module comprises a first acquisition module and a second acquisition module, and the data processing module comprises a first processing module and a second processing module, the first acquisition module is connected to the first processing module, the second acquisition module is connected to the second processing module, and the second acquisition module is connected to the data screening module.

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