Patient course monitoring management method and system based on clinical big data

Abstract

The present invention relates to the field of disease course monitoring technology, and in particular to a patient disease course monitoring management method and system based on clinical big data, the steps of which include: obtaining patient symptom monitoring data, historical medical examination data and medical examination radiation data; evaluating symptom change trends through the patient symptom monitoring data, and determining the medical examination value index of each medical examination item in combination with the evaluation result; determining the historical medical examination reference index of the historical medical examination item; determining the health impact index of each medical examination item on the patient; determining the necessary medical examination items of the patient through the medical examination value index, the historical medical examination reference index and the health impact index, and monitoring the patient's disease course through the examination results of the necessary medical examination items. The present invention improves the accuracy and reliability of patient disease course monitoring by quantifying the comprehensive matching degree of each medical examination item with the patient's symptoms.

Description

Technical Field

[0001] The present invention relates to the technical field of disease course monitoring, and in particular to a patient disease course monitoring management method and system based on clinical big data. Background Art

[0002] With the advancement of treatment and early screening, the survival time of cancer patients is increasing. Patients will experience different symptoms at different stages of the disease. By monitoring the patient's symptoms, we can understand the patient's condition in a timely manner and provide support and guidance for clinical decision-making and medical quality improvement.

[0003] The disease course of cancer patients usually includes the incubation period, prodromal period, symptomatic period and outcome period. Disease course monitoring is the continuous monitoring of the patient's condition and treatment process, which forms clinical big data including changes in the patient's condition, medical auxiliary examination results and clinical significance, diagnosis and treatment measures taken and treatment effects. Among them, medical auxiliary examinations can detect and diagnose cancer early, thereby achieving early treatment or even ultra-early intervention, further improving patient survival rates.

[0004] The prior art usually relies on the clinical experience and subjective judgment of doctors when determining which auxiliary medical examinations patients need. However, the doctor's judgment may be limited by the richness of personal experience, the speed of knowledge updating and the influence of subjective factors. At the same time, there is a problem of repeated examinations of the same medical examination items in the process of auxiliary medical examinations for patients, which not only causes a waste of medical resources, but also brings unnecessary burdens and potential health risks to patients. Therefore, in order to improve the accuracy and rationality of patient disease course monitoring, the present invention provides a patient disease course monitoring management method and system based on clinical big data. Summary of the invention

[0005] In order to overcome the defects and shortcomings of the prior art, the present invention provides a patient disease course monitoring management method and system based on clinical big data, which improves the accuracy and reliability of patient disease course monitoring by quantifying the comprehensive matching degree between each medical examination item and the patient's symptoms.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a patient course monitoring and management method based on clinical big data, comprising the following steps:

[0008] Access patient symptom monitoring data, historical medical examination data, and medical examination radiation data;

[0009] Evaluate the symptom change trend through the patient symptom monitoring data, and determine the medical examination value index of each medical examination item based on the symptom change trend evaluation result;

[0010] Determine a historical medical examination reference index for a historical medical examination item through the patient symptom monitoring data and the historical medical examination data;

[0011] Determine the health impact index of each medical examination item on the patient through the historical medical examination data and the medical examination radiation data;

[0012] The necessary medical examination items for the patient are determined through the medical examination value index, the historical medical examination reference index and the health impact index, and the patient's disease course is monitored through the examination results of the necessary medical examination items.

[0013] As a possible implementation method, the specific steps of determining the medical examination value index of each medical examination item include:

[0014] Obtaining the symptom change trend assessment result and the medical examination symptom set corresponding to the medical examination item;

[0015] The medical examination value index of the medical examination item is calculated by the symptom change trend evaluation result and the medical examination symptom set. The calculation formula of the medical examination value index is:

[0016] ;

[0017] In the formula represents the patient's current set of symptoms, Indicates the set of symptoms that have become more severe in the symptom change trend assessment results. Representation calculation and The number of intersection elements, Representation calculation and The number of intersection elements, Representation calculation and the number of elements in the union, Indicates The set of medical examination symptoms corresponding to each medical examination item, represents the first adjustment factor, Represents the medical examination value index.

[0018] As a possible implementation method, the specific steps of determining the historical medical examination reference index of the historical medical examination item include:

[0019] Acquiring the patient symptom monitoring data and the historical medical examination data;

[0020] Calculating the correlation coefficient between the historical medical examination items and the patient's current symptoms through the patient's symptom monitoring data and the historical medical examination data;

[0021] The historical medical examination reference index of the historical medical examination item is calculated by the correlation coefficient and the historical medical examination data. The calculation formula of the historical medical examination reference index is:

[0022] ;

[0023] In the formula Indicates The correlation coefficient between the historical medical examination items and the patient's current symptoms, Indicates the current time and The inspection interval of each historical medical inspection item, Indicates The doctor's order review cycle for each historical medical examination item, represents the second adjustment factor, Represents the historical medical examination reference index.

[0024] As a possible implementation, the calculation formula of the correlation coefficient is:

[0025] ;

[0026] In the formula Indicates that the patient The symptom collection of the historical medical examination items, represents the patient's current set of symptoms, express and The difference of , that is, the number of disappeared symptoms, express and The difference of , that is, the number of new symptoms, Indicates The correlation coefficient between historical medical examination items and the patient's current symptoms.

[0027] As a possible implementation method, the specific steps of determining the health impact index of each medical examination item on the patient include:

[0028] Acquiring the historical medical examination data and the medical examination radiation data;

[0029] The health impact index is calculated by using the historical medical examination data and the medical examination radiation data. The health impact index calculation formula is:

[0030] ;

[0031] In the formula Indicates the average effective radiation dose of medical examination items. Indicates that the patient The radiation dose of each medical examination is Indicates that the patient Tissue weighting factor for each medical examination, Indicates the number of medical examinations the patient has received this year, Indicates the annual effective radiation dose limit, Denotes the health impact index.

[0032] As a possible implementation manner, the symptom change trend assessment result is obtained through any one of the MSAS symptom assessment scale and the MDASI symptom assessment scale.

[0033] As a possible implementation method, the specific steps of determining the necessary medical examination items for the patient include:

[0034] Obtaining the medical examination value index, the historical medical examination reference index and the health impact index;

[0035] The medical examination comprehensive matching index of the medical examination item is calculated by the medical examination value index, the historical medical examination reference index and the health impact index. The calculation formula of the medical examination comprehensive matching index is:

[0036] ;

[0037] In the formula represents the medical examination value index, represents the historical medical examination reference index, represents the health impact index, represents the value weight of medical examination, represents the historical medical examination reference weight, represents the health impact weight, represents the comprehensive matching index of medical examination;

[0038] The medical examination items whose comprehensive matching index of the medical examination is greater than a preset comprehensive matching threshold of the medical examination are determined as the necessary medical examination items.

[0039] It should be noted here that the first adjustment factor, the second adjustment factor, the medical examination value weight, the historical medical examination reference weight, the health impact weight and the preset medical examination comprehensive matching threshold are determined as follows: 5,000 sets of patient symptom monitoring data, historical medical examination data and medical examination radiation data are collected to distinguish whether the patient's medical examination items are necessary medical examination items, and the symptom monitoring data, historical medical examination data and medical examination radiation data are substituted into the medical examination comprehensive matching index calculation formula for calculation, and the calculated medical examination comprehensive matching index and the distinction result are simultaneously imported into the fitting software, and the optimal first adjustment factor, the second adjustment factor, the medical examination value weight, the historical medical examination reference weight, the health impact weight and the preset medical examination comprehensive matching threshold that meet the distinction accuracy of the distinction result are output.

[0040] In a second aspect, the present invention provides a patient course monitoring and management system based on clinical big data, comprising:

[0041] A data acquisition module, used to acquire patient symptom monitoring data, historical medical examination data, and medical examination radiation data;

[0042] A medical examination value index calculation module, used to evaluate the symptom change trend through the patient symptom monitoring data, and determine the medical examination value index of each medical examination item based on the symptom change trend evaluation result;

[0043] A historical medical examination reference index calculation module, used to determine a historical medical examination reference index of a historical medical examination item through the patient symptom monitoring data and the historical medical examination data;

[0044] A health impact index calculation module, used to determine the health impact index of each medical examination item on the patient through the historical medical examination data and the medical examination radiation data;

[0045] A necessary medical examination item determination module, used to determine the necessary medical examination items for the patient through the medical examination value index, the historical medical examination reference index and the health impact index, and to monitor the patient's disease course through the examination results of the necessary medical examination items;

[0046] A control module is used to control the operation of the data acquisition module, the medical examination value index calculation module, the historical medical examination reference index calculation module, the health impact index calculation module and the necessary medical examination item determination module.

[0047] An electronic device of the present invention comprises: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a patient disease course monitoring and management method based on clinical big data by calling the computer program stored in the memory.

[0048] A computer-readable storage medium of the present invention stores instructions, and when the instructions are executed on a computer, the computer executes a patient disease course monitoring and management method based on clinical big data.

[0049] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0050] The present invention first evaluates the patient's symptom change trend through the patient's symptom monitoring data, and quantifies the medical examination value of each medical examination item in combination with the evaluation result, and then quantifies the reference value of historical medical examinations and the degree of health impact of each medical examination item on the patient by calculating the historical medical examination reference index and the health impact index. Finally, the necessary medical examination items for the patient are determined based on the comprehensive quantification results, which effectively improves the accuracy and rationality of the patient's disease course monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0052] Figure 1 It is a schematic diagram of the overall process of the patient course monitoring and management method based on clinical big data of the present invention;

[0053] Figure 2 It is a schematic diagram of a process for determining the historical medical examination reference index in the patient course monitoring and management method based on clinical big data of the present invention;

[0054] Figure 3 It is a schematic diagram of a process for determining necessary medical examination items for a patient in the patient course monitoring and management method based on clinical big data of the present invention;

[0055] Figure 4 It is a structural schematic diagram of the patient course monitoring and management system based on clinical big data of the present invention. DETAILED DESCRIPTION

[0056] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. The embodiments of the present invention and the technical features in the embodiments may be combined with each other unless there is a conflict.

[0057] Example 1

[0058] refer to Figure 1 , Figure 1 The overall process diagram of the patient course monitoring management method based on clinical big data of the present invention is as follows. This embodiment provides a patient course monitoring management method based on clinical big data, which specifically includes the following steps:

[0059] S1: Obtain patient symptom monitoring data, historical medical examination data, and medical examination radiation data.

[0060] S2: evaluating the symptom change trend through the patient's symptom monitoring data, and determining the medical examination value index of each medical examination item based on the symptom change trend evaluation result;

[0061] The symptom trend assessment results are obtained through either the MSAS symptom assessment scale or the MDASI symptom assessment scale. The MSAS (Memorial Symptom Assessment Scale) symptom assessment scale is a multidimensional symptom assessment scale used to assess the frequency, intensity, and distress of various symptoms experienced by patients in a specific period of time. The MSAS symptom assessment scale assesses 32 physiological and psychological symptoms, and distinguishes three main symptom groups through factor analysis of variance: psychological symptoms (PSYCH), high incidence physical symptoms (PHYS H), and low incidence physical symptoms (PHYS L). By regularly assessing the patient's symptom changes, doctors can adjust the treatment plan in a timely manner to improve the treatment effect and the patient's quality of life.

[0062] The MDASI (MD Anderson Symptom Inventory) symptom assessment scale is a multi-symptom self-rating scale developed by the University of Texas MD Anderson Cancer Center in 2000. It is used to assess the severity of common symptoms of cancer patients and the impact of these symptoms on patients' daily lives. The scale consists of two parts: the severity of common symptoms and the degree of distress. The first part is used to assess the severity of 13 common symptoms such as pain, fatigue, nausea and vomiting reported by patients in the past. Each item is scored from 0 to 10 points. The higher the score, the more severe the symptom. The second part is used to assess the degree of distress of the patient's self-rated symptom level on 6 aspects of daily life such as general activities. Each item is scored from 0 to 10 points. The higher the score, the more severe the symptom distress.

[0063] By comparing the patient's current symptom set with the aggravated symptom set in the symptom change trend assessment results, and combining the symptom set corresponding to the medical examination items to calculate the medical examination value index, the medical examination value index is used to quantitatively assess the potential value of each medical examination item for the patient's current condition diagnosis and treatment guidance, that is, whether the medical examination item can effectively capture symptom changes and then guide treatment decisions. The specific steps for determining the medical examination value index of each medical examination item include:

[0064] Obtaining the symptom change trend assessment result and the medical examination symptom set corresponding to the medical examination item;

[0065] The medical examination value index of the medical examination item is calculated by the symptom change trend evaluation result and the medical examination symptom set. The calculation formula of the medical examination value index is:

[0066] ;

[0067] In the formula represents the patient's current set of symptoms, Indicates the set of symptoms that have become more severe in the symptom change trend assessment results. Representation calculation and The number of intersection elements, Representation calculation and The number of intersection elements, Representation calculation and the number of elements in the union, Indicates The set of medical examination symptoms corresponding to each medical examination item, represents the first adjustment factor, Represents the medical examination value index.

[0068] S3: determining a historical medical examination reference index for a historical medical examination item through the patient symptom monitoring data and the historical medical examination data;

[0069] The historical medical examination reference index can quantify the correlation and time correlation between historical examination items and patients' current symptoms. The correlation coefficient reflects the degree of correlation between historical examination items and patients' current symptoms, that is, the possible contribution of historical examination results to the explanation of current symptoms. The examination time interval and the doctor's order review cycle take into account the time factor and the cyclical impact of medical advice. Therefore, the historical medical examination reference index is of great significance for optimizing examination decisions, avoiding repeated or invalid examinations, and improving diagnosis and treatment efficiency. Figure 2 , Figure 2This is a flow chart of determining the historical medical examination reference index in the patient course monitoring management method based on clinical big data of the present invention. The specific steps of determining the historical medical examination reference index of the historical medical examination items include:

[0070] Acquiring the patient symptom monitoring data and the historical medical examination data;

[0071] Calculating the correlation coefficient between the historical medical examination items and the patient's current symptoms through the patient's symptom monitoring data and the historical medical examination data;

[0072] The historical medical examination reference index of the historical medical examination item is calculated by the correlation coefficient and the historical medical examination data. The calculation formula of the historical medical examination reference index is:

[0073] ;

[0074] In the formula Indicates The correlation coefficient between the historical medical examination items and the patient's current symptoms, Indicates the current time and The inspection interval of each historical medical inspection item, Indicates The doctor's order review cycle for each historical medical examination item, represents the second adjustment factor, represents the historical medical examination reference index;

[0075] By obtaining the difference between the symptom set of the historical medical examination items and the patient's current symptom set, that is, the number of disappeared symptoms and the number of new symptoms, the correlation coefficient is calculated to quantify the trend of symptom changes. The higher the correlation coefficient, the stronger the correlation between the symptoms of the historical examination items and the patient's current symptoms, which means that the results of the historical examination items have a higher reference value for current diagnosis and treatment. The calculation formula of the correlation coefficient is:

[0076] ;

[0077] In the formula Indicates that the patient The symptom collection of the historical medical examination items, represents the patient's current set of symptoms, express and The difference of , that is, the number of disappeared symptoms, express and The difference of , that is, the number of new symptoms, Indicates The correlation coefficient between historical medical examination items and the patient's current symptoms.

[0078] S4: Determine the health impact index of each medical examination item on the patient through the historical medical examination data and the medical examination radiation data;

[0079] The health impact index is calculated through historical medical examination data and medical examination radiation data. The historical medical examination data provides the number of medical examinations the patient received this year and the specific radiation exposure of each examination, that is, the exposure dose and tissue weighting factor. The tissue weighting factor adopts the tissue weighting factor in the ICRP103 version recommended by the International Commission on Radiological Protection (ICRP). The medical examination radiation data includes the average effective radiation dose of the medical examination items and the annual effective radiation dose limit. The average effective radiation dose adopts the reference value of the average effective radiation dose of common imaging examinations jointly formulated by ACR, RSNA and other organizations. The annual effective radiation dose limit adopts the dose limit for radiation workers specified in the Basic Standard for Ionizing Radiation Protection and Radiation Source Safety (GB18871-2002). The health impact index is used to quantify the radiation dose received by the patient during the medical examination this year, and to evaluate the potential impact of subsequent medical examinations on the patient's health. The higher the health impact index, the greater the radiation risk to the patient. The health impact index can help doctors balance the diagnostic value and radiation risk of medical examinations and avoid unnecessary repeated examinations. The specific steps to determine the health impact index of each medical examination item on the patient include:

[0080] Acquiring the historical medical examination data and the medical examination radiation data;

[0081] The health impact index is calculated by using the historical medical examination data and the medical examination radiation data. The health impact index calculation formula is:

[0082] ;

[0083] In the formula Indicates the average effective radiation dose of medical examination items. Indicates that the patient The radiation dose of each medical examination is Indicates that the patient Tissue weighting factor for each medical examination, Indicates the number of medical examinations the patient has received this year, Indicates the annual effective radiation dose limit, Denotes the health impact index.

[0084] S5: determining the necessary medical examination items for the patient through the medical examination value index, the historical medical examination reference index and the health impact index, and monitoring the course of the patient's disease through the examination results of the necessary medical examination items;

[0085] The comprehensive matching index of medical examinations is calculated by combining the medical examination value index, the historical medical examination reference index and the health impact index. The medical examination value index reflects the importance of medical examination items in explaining and diagnosing the patient's current symptoms, the historical medical examination reference index reflects the reference value of historical examination results to current medical decisions, and the health impact index quantifies the potential radiation risks of medical examination items to the patient's health. The comprehensive matching index of medical examinations can quantitatively evaluate the necessity of medical examination items, ensure the effective use of medical resources, and protect the patient's health to the greatest extent possible. Figure 3 , Figure 3 This is a flow chart of determining necessary medical examination items for a patient in the patient course monitoring management method based on clinical big data of the present invention. The specific steps of determining necessary medical examination items for a patient include:

[0086] Obtaining the medical examination value index, the historical medical examination reference index and the health impact index;

[0087] The medical examination comprehensive matching index of the medical examination item is calculated by the medical examination value index, the historical medical examination reference index and the health impact index. The calculation formula of the medical examination comprehensive matching index is:

[0088] ;

[0089] In the formula represents the medical examination value index, represents the historical medical examination reference index, represents the health impact index, represents the value weight of medical examination, represents the historical medical examination reference weight, represents the health impact weight, represents the comprehensive matching index of medical examination;

[0090] The medical examination items whose comprehensive matching index of the medical examination is greater than a preset comprehensive matching threshold of the medical examination are determined as the necessary medical examination items.

[0091] Example 2

[0092] refer to Figure 4 , Figure 4: is a schematic diagram of the structure of the patient course monitoring and management system based on clinical big data of the present invention. This embodiment provides a patient course monitoring and management system based on clinical big data, including:

[0093] A data acquisition module, used to acquire patient symptom monitoring data, historical medical examination data, and medical examination radiation data;

[0094] A medical examination value index calculation module, used to evaluate the symptom change trend through the patient symptom monitoring data, and determine the medical examination value index of each medical examination item based on the symptom change trend evaluation result;

[0095] A historical medical examination reference index calculation module, used to determine a historical medical examination reference index of a historical medical examination item through the patient symptom monitoring data and the historical medical examination data;

[0096] A health impact index calculation module, used to determine the health impact index of each medical examination item on the patient through the historical medical examination data and the medical examination radiation data;

[0097] A necessary medical examination item determination module, used to determine the necessary medical examination items for the patient through the medical examination value index, the historical medical examination reference index and the health impact index, and to monitor the patient's disease course through the examination results of the necessary medical examination items;

[0098] A control module is used to control the operation of the data acquisition module, the medical examination value index calculation module, the historical medical examination reference index calculation module, the health impact index calculation module and the necessary medical examination item determination module.

[0099] In this embodiment, the medical examination value index calculation module is used to evaluate the symptom change trend through the patient symptom monitoring data, and determine the medical examination value index of each medical examination item in combination with the symptom change trend evaluation result, and the symptom change trend evaluation result is obtained through any one of the MSAS symptom evaluation scale and the MDASI symptom evaluation scale;

[0100] The specific steps for determining the medical examination value index of each medical examination item include:

[0101] Obtaining the symptom change trend assessment result and the medical examination symptom set corresponding to the medical examination item;

[0102] The medical examination value index of the medical examination item is calculated by the symptom change trend evaluation result and the medical examination symptom set. The calculation formula of the medical examination value index is:

[0103] ;

[0104] In the formula represents the patient's current set of symptoms, Indicates the set of symptoms that have become more severe in the symptom change trend assessment results. Representation calculation and The number of intersection elements, Representation calculation and The number of intersection elements, Representation calculation and the number of elements in the union, Indicates The set of medical examination symptoms corresponding to each medical examination item, represents the first adjustment factor, Represents the medical examination value index.

[0105] In this embodiment, the historical medical examination reference index calculation module is used to determine the historical medical examination reference index of the historical medical examination item through the patient symptom monitoring data and the historical medical examination data. The specific steps of determining the historical medical examination reference index of the historical medical examination item include:

[0106] Acquiring the patient symptom monitoring data and the historical medical examination data;

[0107] Calculating the correlation coefficient between the historical medical examination items and the patient's current symptoms through the patient's symptom monitoring data and the historical medical examination data;

[0108] The historical medical examination reference index of the historical medical examination item is calculated by the correlation coefficient and the historical medical examination data. The calculation formula of the historical medical examination reference index is:

[0109] ;

[0110] In the formula Indicates The correlation coefficient between the historical medical examination items and the patient's current symptoms, Indicates the current time and The inspection interval of each historical medical inspection item, Indicates The doctor's order review cycle for each historical medical examination item, represents the second adjustment factor, represents the historical medical examination reference index;

[0111] The calculation formula of the correlation coefficient is:

[0112] ;

[0113] In the formula Indicates that the patient The symptom collection of the historical medical examination items, represents the patient's current set of symptoms, express and The difference of , that is, the number of disappeared symptoms, express and The difference of , that is, the number of new symptoms, Indicates The correlation coefficient between historical medical examination items and the patient's current symptoms.

[0114] In this embodiment, the health impact index calculation module is used to determine the health impact index of each medical examination item on the patient through the historical medical examination data and the medical examination radiation data. The specific steps of determining the health impact index of each medical examination item on the patient include:

[0115] Acquiring the historical medical examination data and the medical examination radiation data;

[0116] The health impact index is calculated by using the historical medical examination data and the medical examination radiation data. The health impact index calculation formula is:

[0117] ;

[0118] In the formula Indicates the average effective radiation dose of medical examination items. Indicates that the patient The radiation dose of each medical examination is Indicates that the patient Tissue weighting factor for each medical examination, Indicates the number of medical examinations the patient has received this year, Indicates the annual effective radiation dose limit, Denotes the health impact index.

[0119] In this embodiment, the necessary medical examination item determination module is used to determine the necessary medical examination items of the patient through the medical examination value index, the historical medical examination reference index and the health impact index, and monitor the patient's disease course through the examination results of the necessary medical examination items. The specific steps of determining the necessary medical examination items of the patient include:

[0120] Obtaining the medical examination value index, the historical medical examination reference index and the health impact index;

[0121] The medical examination comprehensive matching index of the medical examination item is calculated by the medical examination value index, the historical medical examination reference index and the health impact index. The calculation formula of the medical examination comprehensive matching index is:

[0122] ;

[0123] In the formula represents the medical examination value index, represents the historical medical examination reference index, represents the health impact index, represents the value weight of medical examination, represents the historical medical examination reference weight, represents the health impact weight, represents the comprehensive matching index of medical examination;

[0124] The medical examination items whose comprehensive matching index of the medical examination is greater than a preset comprehensive matching threshold of the medical examination are determined as the necessary medical examination items.

[0125] The above-mentioned parameters and steps for each unit module to realize the corresponding functions in the patient disease course monitoring and management system based on clinical big data of the present invention can refer to the parameters and steps in the embodiment of the patient disease course monitoring and management method based on clinical big data above, and will not be repeated here.

[0126] Example 3

[0127] A computer-readable storage medium according to an embodiment of the present invention stores a computer program thereon, and when the computer program is executed by a processor, the above-mentioned patient course monitoring and management method based on clinical big data is implemented. It should be noted that all computer programs of the patient course monitoring and management method based on clinical big data are implemented in C language, wherein the data acquisition module, the medical examination value index calculation module, the historical medical examination reference index calculation module, the health impact index calculation module, the necessary medical examination item determination module and the control module are all controlled by a remote server.

[0128] Example 4

[0129] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0130] When the computer program runs on a computer device, the computer device executes the above-mentioned patient disease course monitoring and management method based on clinical big data.

[0131] For example, the computer readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

Claims

1. A patient course monitoring and management method based on clinical big data, characterized in that: The steps include: Access patient symptom monitoring data, historical medical examination data, and medical examination radiation data; Evaluate the symptom change trend through the patient symptom monitoring data, and determine the medical examination value index of each medical examination item based on the symptom change trend evaluation result; Determine a historical medical examination reference index for a historical medical examination item through the patient symptom monitoring data and the historical medical examination data; Determine the health impact index of each medical examination item on the patient through the historical medical examination data and the medical examination radiation data; Determine the necessary medical examination items for the patient through the medical examination value index, the historical medical examination reference index and the health impact index, and monitor the course of the patient's disease through the examination results of the necessary medical examination items; The specific steps of determining the medical examination value index of each medical examination item include: Obtaining the symptom change trend assessment result and the medical examination symptom set corresponding to the medical examination item; The medical examination value index of the medical examination item is calculated by the symptom change trend evaluation result and the medical examination symptom set. The calculation formula of the medical examination value index is: ； In the formula represents the patient's current set of symptoms, Indicates the set of symptoms that have become more severe in the symptom change trend assessment results. Representation calculation and The number of intersection elements, Representation calculation and The number of intersection elements, Representation calculation and the number of elements in the union, Indicates The set of medical examination symptoms corresponding to each medical examination item, represents the first adjustment factor, represents the medical examination value index; The specific steps of determining the historical medical examination reference index of the historical medical examination items include: Acquiring the patient symptom monitoring data and the historical medical examination data; Calculating the correlation coefficient between the historical medical examination items and the patient's current symptoms through the patient's symptom monitoring data and the historical medical examination data; The historical medical examination reference index of the historical medical examination item is calculated by the correlation coefficient and the historical medical examination data. The calculation formula of the historical medical examination reference index is: ； In the formula Indicates The correlation coefficient between the historical medical examination items and the patient's current symptoms, Indicates the current time and The inspection interval of each historical medical inspection item, Indicates The doctor's order review cycle for each historical medical examination item, represents the second adjustment factor, represents the historical medical examination reference index; The calculation formula of the correlation coefficient is: ； In the formula Indicates that the patient The symptom collection of the historical medical examination items, represents the patient's current set of symptoms, express and The difference of , that is, the number of disappeared symptoms, express and The difference of , that is, the number of new symptoms, Indicates The correlation coefficient between the historical medical examination items and the patient's current symptoms; The specific steps of determining the health impact index of each medical examination item on the patient include: Acquiring the historical medical examination data and the medical examination radiation data; The health impact index is calculated by using the historical medical examination data and the medical examination radiation data. The health impact index calculation formula is: ； In the formula Indicates the average effective radiation dose of medical examination items. Indicates that the patient The radiation dose of each medical examination is Indicates that the patient Tissue weighting factor for each medical examination, Indicates the number of medical examinations the patient has received this year, Indicates the annual effective radiation dose limit, represents the health impact index; The symptom change trend assessment result is obtained by using any one of the MSAS symptom assessment scale and the MDASI symptom assessment scale; The specific steps of determining the necessary medical examination items for the patient include: Obtaining the medical examination value index, the historical medical examination reference index and the health impact index; The medical examination comprehensive matching index of the medical examination item is calculated by the medical examination value index, the historical medical examination reference index and the health impact index. The calculation formula of the medical examination comprehensive matching index is: ； In the formula represents the medical examination value index, represents the historical medical examination reference index, represents the health impact index, represents the value weight of medical examination, represents the historical medical examination reference weight, represents the health impact weight, represents the comprehensive matching index of medical examination; The medical examination items whose comprehensive matching index of the medical examination is greater than a preset comprehensive matching threshold of the medical examination are determined as the necessary medical examination items.

2. A patient course monitoring and management system based on clinical big data, which is implemented based on the patient course monitoring and management method based on clinical big data described in claim 1, characterized in that: The system comprises: A data acquisition module, used to acquire patient symptom monitoring data, historical medical examination data, and medical examination radiation data; A medical examination value index calculation module, used to evaluate the symptom change trend through the patient symptom monitoring data, and determine the medical examination value index of each medical examination item based on the symptom change trend evaluation result; A historical medical examination reference index calculation module, used to determine a historical medical examination reference index of a historical medical examination item through the patient symptom monitoring data and the historical medical examination data; A health impact index calculation module, used to determine the health impact index of each medical examination item on the patient through the historical medical examination data and the medical examination radiation data; A necessary medical examination item determination module, used to determine the necessary medical examination items for the patient through the medical examination value index, the historical medical examination reference index and the health impact index, and to monitor the patient's disease course through the examination results of the necessary medical examination items; A control module is used to control the operation of the data acquisition module, the medical examination value index calculation module, the historical medical examination reference index calculation module, the health impact index calculation module and the necessary medical examination item determination module.

3. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the patient disease course monitoring and management method based on clinical big data as described in claim 1 by calling the computer program stored in the memory.

4. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer executes the patient disease course monitoring and management method based on clinical big data as claimed in claim 1.

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