Diagnosis basis detection method and system and storage medium

Abstract

The invention provides a diagnosis basis detection method and system and a storage medium, and the method comprises the steps: carrying out the preliminary diagnosis of diagnosis diseases and disease grade information in a result, and determining a target disease history and associated risk factors in a to-be-detected diagnosis basis according to the diagnosis diseases; acquiring a disease parameter value in the history of the target disease, and determining target grade information according to the diagnosis disease, the disease parameter value and the associated risk factor; if the disease grade information is the same as the target grade information, judging that the diagnosis basis to be detected is sufficient for diagnosis of the diagnosed disease; and if the disease grade information is different from the target grade information, judging that the diagnosis basis to be detected is insufficient in diagnosis of the diagnosed disease. According to the embodiment of the invention, by judging whether the disease grade information is the same as the target grade information, whether the diagnosis of the to-be-detected diagnosis basis aiming at the diagnosis disease is sufficient can be automatically detected, and the diagnosis basis detection efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of smart medical technology, and in particular to a diagnostic evidence detection method, system and storage medium. Background Art

[0002] With the popularization of medical electronic information, electronic medical record information has become an essential way for major hospitals to record medical information. In the existing electronic medical record information, medical workers need to fill in the patient's first medical record in the medical record. The diagnostic basis is an important component of the first medical record. The accuracy of its content will have a great impact on the diagnosis of the patient's disease. Therefore, the issue of diagnostic basis testing is receiving more and more attention.

[0003] In the existing diagnostic evidence detection process, the diagnostic evidence is generally detected manually, which leads to low efficiency of diagnostic evidence detection. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide a diagnostic evidence detection method, system and storage medium to solve the problem of low diagnostic evidence detection efficiency in the prior art.

[0005] The embodiment of the present invention is implemented as follows: a diagnostic basis detection method, the method comprising:

[0006] Obtaining the patient's first medical record, and extracting the preliminary diagnosis result and the diagnostic basis to be tested from the first medical record;

[0007] Obtaining the diagnosed disease and disease grade information in the preliminary diagnosis result, and determining the target disease history and associated risk factors in the diagnostic basis to be tested based on the diagnosed disease;

[0008] Acquiring disease parameter values ​​in the medical history of the target disease, and determining target level information according to the diagnosed disease, the disease parameter values ​​and the associated risk factors;

[0009] If the disease level information is the same as the target level information, it is determined that the diagnostic basis to be detected is sufficient for the diagnosis of the diagnosed disease;

[0010] If the disease level information is different from the target level information, it is determined that the diagnostic basis to be detected is insufficient for the diagnosis of the diagnosed disease.

[0011] Preferably, determining target level information according to the diagnosed disease, the disease parameter value and the associated risk factor includes:

[0012] Determine a disease grade table and a disease stratification table according to the diagnosed disease, and match the disease parameter value with the disease grade table to obtain a disease grade value;

[0013] Obtaining the number of factors of the associated risk factors, and matching the number of factors, the disease grade value and the disease stratification table to obtain a disease stratification value;

[0014] The diagnosed disease, the disease level value and the disease stratification value are combined to obtain the target level information.

[0015] Preferably, after matching the disease parameter value with the disease grade table to obtain the disease grade value, the method further includes:

[0016] Determining whether there is target organ damage information in the diagnostic basis to be detected;

[0017] If the target organ damage information exists in the diagnostic basis to be detected, the target organ damage quantity and the disease grade value in the target organ damage information are matched with the disease stratification table to obtain the disease stratification value.

[0018] Preferably, after matching the disease parameter value with the disease grade table to obtain the disease grade value, the method further includes:

[0019] Determining whether there is complication information in the diagnostic basis to be tested;

[0020] If the complication information exists in the diagnostic basis to be detected, the preset stratification value is set as the disease stratification value.

[0021] Preferably, after matching the disease parameter value with the disease grade table to obtain the disease grade value, the method further includes:

[0022] If the associated risk factor does not exist in the diagnostic basis to be detected, the disease stratification value is determined according to the disease grade value.

[0023] Preferably, determining the target disease history and associated risk factors in the diagnostic basis to be detected according to the diagnosed disease includes:

[0024] Determine medical history-related words and risk-related words according to the diagnosed disease, and match the medical history-related words, risk-related words with the diagnostic evidence to be tested;

[0025] According to the association word matching result, the text sentence matching the medical history association word is determined as the target disease medical history, and the text word matching the risk association word is determined as the associated risk factor.

[0026] Preferably, after determining the target disease history and associated risk factors in the diagnostic basis to be detected according to the diagnosed disease, the method further includes:

[0027] If the disease parameter value is not obtained in the medical history of the target disease, it is determined that the diagnostic basis to be detected is insufficient for the diagnosis of the diagnosed disease.

[0028] Another object of an embodiment of the present invention is to provide a diagnostic evidence detection system, the system comprising:

[0029] A record acquisition module is used to acquire the patient's first medical record and extract the preliminary diagnosis result and the diagnostic basis to be tested from the first medical record;

[0030] A medical history determination module is used to obtain the diagnosed disease and disease grade information in the preliminary diagnosis result, and determine the target disease medical history and associated risk factors in the diagnostic basis to be tested according to the diagnosed disease;

[0031] a level determination module, configured to obtain disease parameter values ​​in the medical history of the target disease, and determine target level information based on the diagnosed disease, the disease parameter values ​​and the associated risk factors;

[0032] A result output module, for determining that the diagnostic evidence to be tested is sufficient for the diagnosis of the diagnosed disease if the disease level information is the same as the target level information;

[0033] If the disease level information is different from the target level information, it is determined that the diagnostic basis to be detected is insufficient for the diagnosis of the diagnosed disease.

[0034] Preferably, the level determination module is further used for:

[0035] Determine a disease grade table and a disease stratification table according to the diagnosed disease, and match the disease parameter value with the disease grade table to obtain a disease grade value;

[0036] Obtaining the number of factors of the associated risk factors, and matching the number of factors, the disease grade value and the disease stratification table to obtain a disease stratification value;

[0037] The diagnosed disease, the disease level value and the disease stratification value are combined to obtain the target level information.

[0038] The embodiments of the present invention can effectively determine the target disease history and associated risk factors in the diagnostic basis to be tested by obtaining the diagnosed disease in the preliminary diagnosis result, and can effectively determine the target level information based on the diagnosed disease, disease parameter values ​​and associated risk factors by obtaining the disease parameter values ​​in the target disease history. By judging whether the disease level information is the same as the target level information, it can automatically detect whether the diagnostic basis to be tested is sufficient for the diagnosis of the diagnosed disease, without the need to manually detect the diagnostic basis, thereby improving the detection efficiency of the diagnostic basis. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is a flow chart of a diagnostic evidence detection method provided by a first embodiment of the present invention;

[0040] Figure 2 is a schematic diagram of the first medical record provided by the first embodiment of the present invention;

[0041] Figure 3 is a schematic diagram of a disease stratification table provided in the first embodiment of the present invention;

[0042] Figure 4 is a schematic diagram of the structure of a diagnostic evidence detection system provided by a second embodiment of the present invention;

[0043] Figure 5 is a specific implementation diagram of the diagnostic evidence detection system provided by the second embodiment of the present invention;

[0044] Figure 6 It is a schematic diagram of the structure of a terminal device provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0046] In order to illustrate the technical solution of the present invention, a specific embodiment is provided below for illustration.

[0047] Embodiment 1

[0048] See also Figure 1 , is a flow chart of a diagnostic basis detection method provided by a first embodiment of the present invention. The diagnostic basis detection method can be applied to any device or system. The diagnostic basis detection method comprises the steps of:

[0049] Step S10, obtaining the patient's first medical record, and extracting the preliminary diagnosis result and the diagnostic basis to be tested from the first medical record;

[0050] The first medical record of the patient is obtained through a designated data interface, which is connected to the hospital inpatient management system (Hospital Information System, HIS for short). After the first medical record is obtained, the title of the first medical record is located, and the preliminary diagnosis result and the diagnostic basis to be tested are extracted based on the title location result. The title location can use text matching to determine the text paragraph where the preliminary diagnosis result and the diagnostic basis to be tested are located. Please refer to Figure 2 The first medical record includes information such as the recording date, medical record characteristics, initial diagnosis results, diagnostic basis, differential diagnosis, condition assessment and treatment plan nodes.

[0051] Step S20, obtaining the diagnosed disease and disease grade information in the preliminary diagnosis result, and determining the target disease history and associated risk factors in the diagnostic basis to be detected according to the diagnosed disease;

[0052] Among them, the diagnosed disease is obtained by extracting the disease name in the preliminary diagnosis result, and the disease level description corresponding to the diagnosed disease in the preliminary diagnosis result is obtained to obtain the disease level information. The disease level information includes the diagnosis level value and the diagnosis stratification value. For example, when the disease level information is hypertension level 1 (low risk), the diagnosed disease is hypertension, the diagnosis level value is level 1, and the diagnosis stratification value is low risk.

[0053] Optionally, determining the target disease history and associated risk factors in the diagnostic basis to be detected according to the diagnosed disease includes:

[0054] Determine medical history-related words and risk-related words according to the diagnosed disease, and match the medical history-related words, the risk-related words with the diagnostic basis to be tested; wherein the diagnosed disease is matched with a correlation word query table to obtain medical history-related words and risk-related words, and the correlation word query table stores the correspondence between different diagnosed diseases and corresponding medical history-related words and risk-related words, and matches the medical history-related words, risk-related words with the diagnostic basis to be tested to query whether there are text descriptions corresponding to the medical history-related words and risk-related words in the diagnostic basis to be tested;

[0055] According to the results of the associated word matching, the text sentences matching the medical history associated words are determined as the medical history of the target disease, and the text words matching the risk associated words are determined as the associated risk factors; wherein the associated risk factors are factors that affect the severity of the corresponding diagnosed disease. For example, when the diagnosed disease is hypertension, the associated risk factors determined include smoking, blood cholesterol>220mg / dl, diabetes, male age>55 years old, female age>65 years old, family history of premature cardiovascular disease (age of onset: female <65 years old, male <55 years old), etc.

[0056] Furthermore, after determining the target disease history and associated risk factors in the diagnostic basis to be tested according to the diagnosed disease, it also includes: if the disease parameter value is not obtained in the target disease history, then it is determined that the diagnostic basis to be tested is insufficient for the diagnosis of the diagnosed disease; wherein, when the disease parameter value is not described in the target disease history, then it is determined that the diagnostic basis to be tested is insufficient for the diagnosis of the diagnosed disease.

[0057] Step S30, obtaining the disease parameter value in the medical history of the target disease, and determining the target level information according to the diagnosed disease, the disease parameter value and the associated risk factor;

[0058] Among them, the disease parameter value is used to represent the patient's physical sign status value for the corresponding diagnosed disease. For example, when the diagnosed disease is hypertension, the disease parameter value includes systolic pressure, diastolic pressure and blood pressure value, etc.

[0059] Optionally, determining target level information according to the diagnosed disease, the disease parameter value and the associated risk factor includes:

[0060] According to the diagnosed disease, a disease level table and a disease stratification table are determined, and the disease parameter value is matched with the disease level table to obtain a disease level value; wherein the disease level table stores the correspondence between different disease parameter values ​​and corresponding disease level values, for example, the disease level table includes:

[0061] Stage 1 hypertension: systolic blood pressure 140-159 mmHg and / or diastolic blood pressure 90-99 mmHg;

[0062] Stage 2 hypertension: systolic blood pressure 160-179 mmHg and / or diastolic blood pressure 100-109 mmHg;

[0063] Grade 3 hypertension: systolic blood pressure ≥180 mmHg and / or diastolic blood pressure ≥110 mmHg;

[0064] Obtain the number of factors of the associated risk factors, and match the number of factors, the disease level value and the disease stratification table to obtain the disease stratification value; wherein the disease stratification table stores the corresponding relationship between the number of factors, the disease level value and the corresponding disease stratification value, for example, refer to Figure 3 , when there are 1-2 associated risk factors and the disease level of hypertension is 1 or 2, the disease stratification value is medium risk; when there are 1-2 associated risk factors and the disease level of hypertension is 3, the disease stratification value is very high risk;

[0065] The diagnosed disease, the disease level value and the disease stratification value are combined to obtain the target level information.

[0066] Furthermore, after matching the disease parameter value with the disease level table to obtain the disease level value, the method further includes:

[0067] Determining whether there is target organ damage information in the diagnostic basis to be detected;

[0068] If the target organ damage information exists in the diagnostic basis to be detected, the target organ damage quantity and the disease grade value in the target organ damage information are matched with the disease stratification table to obtain the disease stratification value;

[0069] Among them, the target organ keywords are determined according to the diagnosed disease, and the target organ keywords are matched with the diagnostic basis to be detected to determine whether there is target organ damage information in the diagnostic basis to be detected. The target organ damage information corresponding to different diagnosed diseases may be different. For example, when the diagnosed disease is hypertension, the target organ damage information includes left ventricular hypertrophy (diagnosed by electrocardiogram or echocardiography), proteinuria and / or mild increase in blood creatinine (1.2-2mg / dl), ultrasound or X-ray confirmed atherosclerotic plaques (carotid, iliac, femoral or aorta), focal or extensive stenosis of retinal arteries, etc.

[0070] Furthermore, after matching the disease parameter value with the disease level table to obtain the disease level value, the method further includes:

[0071] Determining whether there is complication information in the diagnostic basis to be tested;

[0072] If the complication information exists in the diagnostic basis to be detected, the preset stratification value is set to the disease stratification value;

[0073] Among them, the preset stratification value can be set according to needs, for example, the preset stratification value can be set to extremely high risk. In this step, the concurrent keywords are determined according to the diagnosed disease, and the concurrent keywords are matched with the diagnostic basis to be tested to determine whether there is complication information in the diagnostic basis to be tested. For example, when the diagnosed disease is hypertension, the complication information includes heart disease (angina pectoris, myocardial infarction, post-coronary artery revascularization, heart failure), cerebrovascular disease (cerebral hemorrhage, ischemic stroke, transient ischemic attack), kidney disease (diabetic nephropathy, increased blood creatinine >2.0 mg / dl), vascular disease (aortic dissection, peripheral artery disease), severe hypertensive retinal lesions (hemorrhage, exudation, papilledema), etc.

[0074] Preferably, after matching the disease parameter value with the disease level table to obtain the disease level value, it also includes: if the associated risk factor does not exist in the diagnostic basis to be detected, determining the disease stratification value according to the disease level value; for example, when the diagnosed disease is hypertension, the disease parameter value is level 1, and there is no associated risk factor, target organ damage information and complication information, then the determined disease stratification value is low risk.

[0075] Step S40, if the disease level information is the same as the target level information, then determining that the diagnostic basis to be detected is sufficient for the diagnosis of the diagnosed disease;

[0076] Among them, if the disease level information is the same as the target level information, it means that the diagnosis of the diagnosis to be tested is sufficient and can effectively provide preliminary suggestions for doctors.

[0077] Step S50, if the disease level information is different from the target level information, it is determined that the diagnosis basis to be tested is insufficient for the diagnosis of the diagnosed disease;

[0078] Among them, if the disease level information is different from the target level information, it means that the diagnosis of the diagnostic basis to be tested is insufficient, and the diagnostic basis to be tested needs to be supplemented, which improves the doctor's diagnostic accuracy in diagnosing the disease and reduces the risk of misdiagnosis.

[0079] Optionally, in this embodiment, the disease level information corresponding to the diagnosed disease may also only include the disease level value, and the disease level value of the diagnosed disease is not stratified, that is, the disease level information can be directly determined based on the matching result between the disease parameter value and the disease level table.

[0080] In this embodiment, by obtaining the diagnosed disease in the preliminary diagnosis result, the target disease history and associated risk factors in the diagnostic basis to be tested can be effectively determined; by obtaining the disease parameter value in the target disease history, the target level information can be effectively determined based on the diagnosed disease, disease parameter values ​​and associated risk factors; by judging whether the disease level information is the same as the target level information, it can automatically detect whether the diagnostic basis to be tested is sufficient for the diagnosis of the diagnosed disease, without the need to manually test the diagnostic basis, thereby improving the efficiency of diagnostic basis detection.

[0081] Embodiment 2

[0082] See also Figure 4 , is a schematic diagram of the structure of a diagnostic evidence detection system 100 provided in a second embodiment of the present invention, comprising:

[0083] The record acquisition module 10 is used to acquire the patient's first medical record and extract the preliminary diagnosis result and the diagnostic basis to be tested from the first medical record.

[0084] The medical history determination module 11 is used to obtain the diagnosed disease and disease grade information in the preliminary diagnosis result, and determine the target disease history and associated risk factors in the diagnostic basis to be detected according to the diagnosed disease.

[0085] Optionally, the medical history determination module 10 is further used to: determine medical history association words and risk association words according to the diagnosed disease, and perform association word matching on the medical history association words, the risk association words and the diagnostic basis to be detected;

[0086] According to the association word matching result, the text sentence matching the medical history association word is determined as the target disease medical history, and the text word matching the risk association word is determined as the associated risk factor.

[0087] The level determination module 12 is used to obtain the disease parameter values ​​in the medical history of the target disease, and determine the target level information according to the diagnosed disease, the disease parameter values ​​and the associated risk factors.

[0088] Optionally, the level determination module 12 is further used to: determine a disease level table and a disease stratification table according to the diagnosed disease, and match the disease parameter value with the disease level table to obtain a disease level value;

[0089] Obtaining the number of factors of the associated risk factors, and matching the number of factors, the disease grade value and the disease stratification table to obtain a disease stratification value;

[0090] The diagnosed disease, the disease level value and the disease stratification value are combined to obtain the target level information.

[0091] Furthermore, the level determination module 12 is also used to: determine whether there is target organ damage information in the diagnostic basis to be detected;

[0092] If the target organ damage information exists in the diagnostic basis to be detected, the target organ damage quantity and the disease grade value in the target organ damage information are matched with the disease stratification table to obtain the disease stratification value.

[0093] Furthermore, the level determination module 12 is also used to: determine whether there is complication information in the diagnostic basis to be detected;

[0094] If the complication information exists in the diagnostic basis to be detected, the preset stratification value is set as the disease stratification value.

[0095] Preferably, the level determination module 12 is further used to determine the disease stratification value according to the disease level value if the associated risk factor does not exist in the diagnostic basis to be detected.

[0096] A result output module 13 is used to determine that the diagnosis basis to be detected is sufficient for the diagnosis of the diagnosed disease if the disease level information is the same as the target level information;

[0097] If the disease level information is different from the target level information, it is determined that the diagnostic basis to be detected is insufficient for the diagnosis of the diagnosed disease.

[0098] Optionally, the result output module 13 is also used to: if the disease parameter value is not obtained in the medical history of the target disease, determine that the diagnosis basis to be tested is insufficient for the diagnosis of the diagnosed disease.

[0099] See also Figure 5 The diagnosis basis detection system 100 can be set in the medical engine. The front end of the medical engine is provided with a data docking module, which is responsible for docking, splitting and storing the first medical course data. The data docking module is connected to the HIS system in real time to the first medical course record document medical record. When the doctor finishes writing the first medical course record, the HIS system pushes the data to the data docking module to generate the first medical course record document table.

[0100] When the diagnosis basis detection system 100 is used to detect the diagnosis basis of hypertension, the medical engine first screens out the hypertension diagnosis medical records based on the preliminary diagnosis node information. If the hypertension diagnosis is included, the hypertension diagnosis basis judgment logic is entered:

[0101] Data analysis and judgment are performed based on the diagnostic basis nodes of the first medical record:

[0102] Judgment rules:

[0103] 1. If the diagnosis basis only contains a history of diabetes but no mention of the type of diabetes, and the diagnosis contains the type of diabetes, the diagnosis basis can be considered insufficient;

[0104] 2. The diagnostic basis for hypertension only mentions the history of hypertension without mentioning the specific blood pressure value, and the diagnosis is graded, which can be considered as insufficient;

[0105] 3. The specific blood pressure value given in the diagnostic basis does not match the hypertension classification in the diagnosis, and the diagnostic basis is considered insufficient. If there are multiple blood pressure values ​​(such as the highest blood pressure reaches 180 / 100mmHg, regular medication treatment, and the current self-measured blood pressure is mostly around 140 / 90mmHg), the classification is determined based on the highest blood pressure given;

[0106] 4 Determine whether the diagnostic basis for hypertension contains the following information: associated risk factors, target organ damage information, and complication information; if not, determine the disease stratification value based on the disease grade value; when the diagnostic basis for hypertension contains associated risk factors, target organ damage information, and complication information, determine the disease stratification value based on the disease stratification table.

[0107] For example:

[0108] Negative example:

[0109] Preliminary diagnosis: dizziness caused by posterior circulation ischemia; Parkinson's disease - orthostatic hypotension; stage 2 hypertension; type 2 diabetes; small nodule in the right lung; tortuosity of the right subclavian artery.

[0110] Diagnosis basis: Patient xxx, male, 67 years old, was admitted to the hospital due to "dizziness and unsteady walking for 1 day". He had a poor physical condition in the past, with "hypertension" for 25 years, the highest of 160 / 95mmHg, and was treated with oral Diovan, and his blood pressure was well controlled. Recently, the patient's self-measured blood pressure was low, 110 / 60mmhg, with a history of "diabetes" for 23 years, and he was usually treated with Glucose and Jieluwei; in July 2018, our hospital considered the diagnosis of "Parkinson's disease", and now "Medopar 1 tablet / time, 3 times / day, Senforo 1 tablet / time, 3 times / day" treatment. The limbs showed slightly high muscle tension, and the sign of difficulty standing with eyes closed was positive. No obvious abnormalities were found in the rest of the nervous system examination.

[0111] The diagnosis was based on test results: small right pulmonary nodule and tortuosity of the right subclavian artery, which was insufficient basis for diagnosis.

[0112] Positive example:

[0113] Initial diagnosis: type 2 diabetic nephropathy, type 2 diabetes, and hypertension stage 3 (very high risk);

[0114] Diagnosis basis: The patient had a history of diabetes for 7 years and usually took gliquidone tablets and pioglitazone hydrochloride tablets to control blood sugar. The kidneys were affected and microalbuminuria appeared. The patient had a history of hypertension for 50 years, with the highest blood pressure reaching 230 / 130 mmHg. He usually took Bayxintong, Cozaar, and Bisoprolol Fumarate Tablets (Bosu) to control blood pressure.

[0115] Diagnosis is based on test results: The diagnosis is based on sufficient evidence.

[0116] In this embodiment, by obtaining the diagnosed disease in the preliminary diagnosis result, the target disease history and associated risk factors in the diagnostic basis to be tested can be effectively determined; by obtaining the disease parameter value in the target disease history, the target level information can be effectively determined based on the diagnosed disease, disease parameter values ​​and associated risk factors; by judging whether the disease level information is the same as the target level information, it can automatically detect whether the diagnostic basis to be tested is sufficient for the diagnosis of the diagnosed disease, without the need to manually test the diagnostic basis, thereby improving the efficiency of diagnostic basis detection.

[0117] Embodiment 3

[0118] Figure 6 2 is a block diagram of a terminal device 2 provided in the third embodiment of the present application. Figure 6 As shown, the terminal device 2 of this embodiment includes: a processor 20, a memory 21, and a computer program 22 stored in the memory 21 and executable on the processor 20, such as a program of the diagnostic evidence detection method. When the processor 20 executes the computer program 22, the steps in each embodiment of the above-mentioned diagnostic evidence detection method are implemented.

[0119] Exemplarily, the computer program 22 may be divided into one or more modules, which are stored in the memory 21 and executed by the processor 20 to complete the present application. The one or more modules may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 22 in the terminal device 2. The terminal device may include, but is not limited to, a processor 20 and a memory 21.

[0120] The processor 20 may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0121] The memory 21 may be an internal storage unit of the terminal device 2, such as a hard disk or memory of the terminal device 2. The memory 21 may also be an external storage device of the terminal device 2, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device 2. Further, the memory 21 may also include both an internal storage unit and an external storage device of the terminal device 2. The memory 21 is used to store the computer program and other programs and data required by the terminal device. The memory 21 may also be used to temporarily store data that has been output or is to be output.

[0122] In addition, each functional module in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional unit.

[0123] If the integrated module is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Among them, the computer-readable storage medium can be non-volatile or volatile. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in computer-readable storage media can be appropriately increased or decreased according to the requirements of legislation and patent practices in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practices, computer-readable storage media do not include electrical carrier signals and telecommunication signals.

[0124] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A diagnostic basis detection method, characterized in that: The method comprises: Obtaining the patient's first medical record, and extracting the preliminary diagnosis result and the diagnostic basis to be tested from the first medical record; Obtaining the diagnosed disease and disease grade information in the preliminary diagnosis result, and determining the target disease history and associated risk factors in the diagnostic basis to be tested based on the diagnosed disease; Acquiring disease parameter values ​​in the medical history of the target disease, and determining target level information according to the diagnosed disease, the disease parameter values ​​and the associated risk factors; If the disease level information is the same as the target level information, it is determined that the diagnostic basis to be detected is sufficient for the diagnosis of the diagnosed disease; If the disease level information is different from the target level information, it is determined that the diagnostic basis to be detected is insufficient for the diagnosis of the diagnosed disease.

2. The diagnostic evidence detection method according to claim 1, characterized in that: Determining target level information according to the diagnosed disease, the disease parameter value and the associated risk factor includes: Determine a disease grade table and a disease stratification table according to the diagnosed disease, and match the disease parameter value with the disease grade table to obtain a disease grade value; Obtaining the number of factors of the associated risk factors, and matching the number of factors, the disease grade value and the disease stratification table to obtain a disease stratification value; The diagnosed disease, the disease level value and the disease stratification value are combined to obtain the target level information.

3. The diagnostic evidence detection method according to claim 2, characterized in that: After matching the disease parameter value with the disease grade table to obtain the disease grade value, the method further includes: Determining whether there is target organ damage information in the diagnostic basis to be detected; If the target organ damage information exists in the diagnostic basis to be detected, the target organ damage quantity and the disease grade value in the target organ damage information are matched with the disease stratification table to obtain the disease stratification value.

4. The diagnostic evidence detection method according to claim 2, characterized in that: After matching the disease parameter value with the disease grade table to obtain the disease grade value, the method further includes: Determining whether there is complication information in the diagnostic basis to be tested; If the complication information exists in the diagnostic basis to be detected, the preset stratification value is set as the disease stratification value.

5. The diagnostic evidence detection method according to claim 2, characterized in that: After matching the disease parameter value with the disease grade table to obtain the disease grade value, the method further includes: If the associated risk factor does not exist in the diagnostic basis to be detected, the disease stratification value is determined according to the disease grade value.

6. The diagnostic evidence detection method according to claim 1, characterized in that: Determining the target disease history and associated risk factors in the diagnostic basis to be tested according to the diagnosed disease includes: Determine medical history-related words and risk-related words according to the diagnosed disease, and match the medical history-related words, risk-related words with the diagnostic evidence to be tested; According to the association word matching result, the text sentence matching the medical history association word is determined as the target disease medical history, and the text word matching the risk association word is determined as the associated risk factor.

7. The diagnostic evidence detection method according to claim 1, characterized in that: After determining the target disease history and associated risk factors in the diagnostic basis to be detected according to the diagnosed disease, the method further includes: If the disease parameter value is not obtained in the medical history of the target disease, it is determined that the diagnostic basis to be detected is insufficient for the diagnosis of the diagnosed disease.

8. A diagnostic evidence detection system, characterized in that: The system comprises: A record acquisition module is used to acquire the patient's first medical record and extract the preliminary diagnosis result and the diagnostic basis to be tested from the first medical record; A medical history determination module is used to obtain the diagnosed disease and disease grade information in the preliminary diagnosis result, and determine the target disease medical history and associated risk factors in the diagnostic basis to be tested according to the diagnosed disease; a level determination module, configured to obtain disease parameter values ​​in the medical history of the target disease, and determine target level information based on the diagnosed disease, the disease parameter values ​​and the associated risk factors; A result output module, for determining that the diagnostic evidence to be tested is sufficient for the diagnosis of the diagnosed disease if the disease level information is the same as the target level information; If the disease level information is different from the target level information, it is determined that the diagnostic basis to be detected is insufficient for the diagnosis of the diagnosed disease.

9. The diagnostic evidence detection system according to claim 8, characterized in that: The level determination module is also used for: Determine a disease grade table and a disease stratification table according to the diagnosed disease, and match the disease parameter value with the disease grade table to obtain a disease grade value; Obtaining the number of factors of the associated risk factors, and matching the number of factors, the disease grade value and the disease stratification table to obtain a disease stratification value; The diagnosed disease, the disease level value and the disease stratification value are combined to obtain the target level information.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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