CN117038062A - Clinical decision support system, method and storage medium - Google Patents
Clinical decision support system, method and storage medium Download PDFInfo
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- CN117038062A CN117038062A CN202311069396.0A CN202311069396A CN117038062A CN 117038062 A CN117038062 A CN 117038062A CN 202311069396 A CN202311069396 A CN 202311069396A CN 117038062 A CN117038062 A CN 117038062A
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- 238000012545 processing Methods 0.000 claims abstract description 4
- 239000003814 drug Substances 0.000 claims description 20
- 229940079593 drug Drugs 0.000 claims description 16
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- G—PHYSICS
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- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
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Abstract
The invention relates to the technical field of intelligent diagnosis and treatment, in particular to a clinical decision support system, an acquisition module, a medical knowledge graph module, an inference decision module and a scheme processing module; according to the invention, the medical knowledge graph module is used for forming a disease data sample knowledge base, the reasoning decision module decides a treatment scheme set based on the formed knowledge base disease data sample knowledge base and the clinical manifestation data of the patient acquired by the acquisition module, and a doctor determines or modifies the treatment scheme set based on the quality scheme set to form a target treatment scheme, so that the problems of irregular and random in the diagnosis and treatment process are avoided, and the work efficiency is improved.
Description
Technical Field
The invention relates to the technical field of intelligent diagnosis and treatment, in particular to a clinical decision support system, a method and a storage medium.
Background
The clinical decision Support System (Clinical Decision, support System, CDSS) refers to a computer application System for automatically judging and giving relevant clinical advice according to clinical information of patients and clinical knowledge of the System, and assisting doctors in making clinical decisions, thereby improving medical quality and service.
With the rapid development of medical, biomedical, precision medical, etc., clinician knowledge updates are becoming more and more difficult to synchronize with rapidly growing changing medical knowledge. The medical subdivision field is more and more studied, and the period of medical knowledge update is shorter and shorter. Evidence-based medicine is one of the important basic principles of modern medicine, and a doctor needs to comprehensively consider factors such as clinical research evidence, own clinical experience, actual conditions of patients and the like in the diagnosis and treatment process so as to achieve an optimal diagnosis and treatment result.
At present, along with the arrival of evidence-based medical era, scientific and effective clinical diagnosis and treatment need to be based on evidence-based medical evidence, although clinical guidelines and diagnosis and treatment specifications written by experts in the field are used as references, doctors have different degrees of problems on understanding and applying the guidelines in a limited time, the actual landing of the guidelines is affected, the triage of the traditional medical institutions is mainly carried out by virtue of experience by medical staff, a scientific and reasonable treatment reference scheme is not provided for clinical manifestations of patients, so that the diagnosis and treatment process is not standard, the randomness is high, and the working efficiency is low.
Disclosure of Invention
The invention aims to provide a clinical decision support system, a clinical decision support method and a storage medium, which are not only helpful for avoiding the problems of nonstandard and high randomness in the diagnosis and treatment process, but also for improving the working efficiency.
The technical scheme provided by the invention is as follows:
a clinical decision support system comprising:
the acquisition module is used for acquiring clinical manifestation data of a patient;
the medical knowledge graph module is used for collecting various resources related to diseases to form a disease data sample knowledge base for inquiry and reading;
the reasoning decision module is used for reasoning out a disease data sample most relevant to the acquired clinical manifestation data of the patient based on a disease data sample knowledge base formed by the medical knowledge graph module, performing machine learning on the most relevant disease data sample, deciding a treatment scheme set, and recommending the treatment scheme set to a doctor, wherein the patient treatment scheme comprises examination to be implemented, relevant drug treatment and special disease early warning;
the plan processing module is used for selecting one treatment plan from the recommended treatment plan set by a doctor, and determining or modifying the selected treatment plan based on the clinical manifestation data of the patient to form a target treatment plan.
Further, the medical knowledge graph module comprises a data collection unit, a medical knowledge graph construction unit and a medical graph verification unit;
the data collection unit is used for collecting data of a data source in the medical industry;
the medical knowledge graph construction unit is used for establishing a knowledge graph data mode of the mutual correlation mapping based on the collected data to form a medical knowledge graph;
and the medical map verification unit is used for verifying the formed medical knowledge map so as to ensure accuracy.
Further, the system also comprises a rationality checking module;
the rationality checking module is used for checking the rationality of the examination, the related drug treatment and the special disease early warning which need to be implemented in the target treatment scheme;
and the alarm module is used for alarming when any one of examination to be implemented, related drug treatment and special disease early warning in the target treatment scheme is unreasonable.
Further, the system also includes a solution evaluation module;
and the scheme evaluation module is used for evaluating the implementation effect of the target treatment scheme and determining the current disease condition of the patient.
In another aspect, the present invention provides a method of clinical decision support, the method further comprising the steps of:
s1, acquiring clinical manifestation data of a patient;
s2, collecting various resources related to the diseases to form a disease data sample knowledge base for inquiry and reading;
s3, deducing a disease data sample most relevant to the acquired clinical manifestation data of the patient based on a disease data sample knowledge base formed by the medical knowledge graph module, performing machine learning on the most relevant disease data sample, deciding a treatment scheme set, and recommending the treatment scheme set to a doctor, wherein the patient treatment scheme comprises examination to be implemented, relevant drug treatment and special disease early warning;
s4, a doctor selects one treatment scheme from the recommended treatment scheme set, and determines or modifies the selected treatment scheme based on clinical manifestation data of the patient to form a target treatment scheme.
Further, the step S1 includes: data collection is carried out on a data source in the medical industry;
based on the collected data, establishing a knowledge graph data mode of the inter-related mapping to form a medical knowledge graph;
and verifying the formed medical knowledge graph to ensure accuracy.
Further, the method further comprises:
checking the rationality of the examination to be implemented, the related drug treatment and the special disease early warning in the target treatment scheme;
alerting is performed when one of the examination to be performed, the associated medication, and the special disease pre-warning in the targeted therapy regimen is unreasonable.
Further, the method further comprises:
and evaluating the implementation effect of the target treatment scheme to determine the current disease condition of the patient.
In yet another aspect, the present invention provides a computer readable storage medium storing a computer program which when executed by a processor implements a method as claimed in any one of the above methods.
The invention has the beneficial effects that:
according to the invention, the medical knowledge graph module is used for forming a disease data sample knowledge base, a treatment scheme set is decided based on the formed knowledge base disease data sample knowledge base, and a doctor determines or modifies the treatment scheme set on the basis of the quality scheme set to form a target treatment scheme, so that the problems of non-standardization and larger randomness in the diagnosis and treatment process are avoided, and the working efficiency is improved.
In addition, the invention has reliable design principle, simple structure and very wide application prospect.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 illustrates a block diagram of one embodiment of a clinical decision support system provided by the present invention.
Fig. 2 shows a flow chart of a clinical decision support method of one embodiment provided by the present invention.
Detailed Description
In order to make the technical solution of the present invention better understood by those skilled in the art, the technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
Example 1:
the present embodiment provides a clinical decision support system, comprising: the acquisition module 1 is used for acquiring clinical manifestation data of a patient through oral dictation of the patient or filling of clinical manifestations by doctors, wherein the clinical manifestation data comprise basic information (age, sex and occupation) of the patient, current medical history, past medical history and the like.
The medical knowledge graph module 2 is used for collecting various resources related to diseases to form a disease data sample knowledge base for inquiry and reading. Specifically, the data collection unit 2-1 is used for collecting data of medical industry data sources, wherein the medical industry data sources comprise high-quality data sources such as standard medical records, clinical guidelines, diagnosis and treatment specifications and the like. The medical knowledge-graph construction unit 2-2 establishes knowledge-graph data patterns of the inter-correlation mapping based on the collected data, forming a medical knowledge graph. The medical atlas checking unit 2-3 can automatically check the medical knowledge atlas, or check the medical knowledge atlas by a medical knowledge expert, and if the formed medical knowledge atlas has a problem, the formed medical knowledge atlas is modified by the medical atlas checking unit 2-3, which is further beneficial to accuracy and authority.
The reasoning decision module 3 reasoning out the most relevant disease data sample of the acquired clinical manifestation data of the patient based on the disease database sample knowledge base formed by the medical knowledge graph module 2, and carries out machine learning on the most relevant disease data sample, decides out a treatment scheme set, and recommends the treatment scheme set to a doctor, wherein the patient treatment scheme comprises examination to be implemented, relevant drug treatment and special disease early warning.
The physician selects one treatment regimen from the recommended treatment regimen set via the regimen processing module 4 and determines or modifies the selected treatment regimen based on clinical presentation data for each patient to form a target treatment regimen.
According to the invention, the medical knowledge graph module 2 is used for forming a disease data sample knowledge base, a treatment scheme set is decided based on the formed knowledge base disease data sample knowledge base, and a doctor determines or modifies the treatment scheme set on the basis of the quality scheme set to form a target treatment scheme, so that the problems of non-standardization and larger randomness in the diagnosis and treatment process are avoided, and the work efficiency is improved.
The embodiment also comprises a rationality checking module 5, an alarm module 6 and a scheme evaluating module 7. The rationality of the examination, the related medicine treatment and the special disease early warning which need to be implemented in the target quality scheme is checked through the rationality checking module 5, if one of the rationality checking module and the rationality checking module has unreasonable content, the alarm module 6 can be used for alarming to remind medical staff and patients, wherein the rationality checking module 5 can also check whether doctor orders and the like are reasonable or not. For example, when the rationality verification module verifies that the dosage of the medicine A for the B disease is three times a day and 20 milligrams once, the alarm module 6 carries out intelligent alarm to prompt medical staff that the dosage is abnormal, thereby avoiding errors and improving the accuracy of diagnosis.
The scheme evaluation module 7 evaluates the implementation effect of the target treatment scheme to determine the current disease condition of the patient, and can judge the rationality of the target scheme according to the evaluation of the implementation effect of the target treatment scheme so as to adjust the target treatment scheme in time, so that a better treatment effect can be achieved.
Example 2:
the embodiment provides a clinical decision support method, which comprises the following steps:
q1, acquiring clinical manifestation data of a patient.
Specifically, the patient clinical manifestation data including patient basic information (age, sex, and occupation), present medical history, past medical history, and the like may be obtained through dictation of the patient itself or by a doctor according to clinical manifestations, examination results, and the like.
Q2, collecting various resources related to the diseases to form a disease data sample knowledge base for inquiry and reading.
Specifically, the data collection unit is used for collecting data of the medical industry data sources, such as data in the medical industry data sources including high-quality data sources such as standard medical records, clinical guidelines, diagnosis and treatment specifications and the like, through the data collection unit. The medical knowledge graph construction unit establishes a knowledge graph data mode of the inter-correlation mapping based on the collected data to form a medical knowledge graph. The medical knowledge expert checks the formed medical knowledge graph, and if a problem exists, the graph is modified through the medical graph checking unit, so that accuracy is guaranteed.
And Q3, deducing a disease data sample most relevant to the acquired clinical manifestation data of the patient based on a disease data sample knowledge base formed by the medical knowledge graph module, performing machine learning on the most relevant disease data sample, deciding a treatment scheme set, and recommending the treatment scheme set to a doctor, wherein the patient treatment scheme comprises examination to be implemented, relevant drug treatment and special disease early warning.
Q4, selecting one treatment scheme from the recommended treatment scheme set by the doctor, and determining or modifying the selected treatment scheme based on the clinical manifestation data of the patient to form a target treatment scheme.
Q5, checking the rationality of the examination, the related drug treatment and the special disease early warning which need to be implemented in the target treatment scheme, and if the unreasonable phenomenon exists in the content of one of the examination, the related drug treatment and the special disease early warning, alarming can be carried out through an alarming module.
Medical staff and patients are reminded through alarming, so that errors are avoided, and the diagnosis accuracy is improved.
And Q6, evaluating the implementation effect of the target treatment scheme, and determining the current disease condition of the patient.
Specifically, the implementation effect of the target treatment scheme is evaluated, the current disease condition of the patient is determined, and meanwhile, the rationality of the target scheme can be judged according to the evaluation of the implementation effect of the target treatment scheme, so that the target treatment scheme can be adjusted in time, and a better treatment effect can be achieved.
Example 3:
the present embodiment provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the method described in embodiment 2.
It will be apparent to those skilled in the art that the techniques of embodiments of the present invention may be implemented in software plus a necessary general purpose hardware platform. Based on such understanding, the technical solution in the embodiments of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium such as a U-disc, a mobile hard disc, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, etc. various media capable of storing program codes, including several instructions for causing a computer terminal (which may be a personal computer, a server, or a second terminal, a network terminal, etc.) to execute all or part of the steps of the method described in the embodiments of the present invention.
It should be noted that, in the method of the embodiments of the present disclosure shown in the flowchart of the drawings or the corresponding description in the block diagrams, operations or steps corresponding to different blocks may also occur in different orders than that disclosed in the description, and sometimes no specific order exists between the different operations or steps. For example, two consecutive operations or steps may actually be performed substantially in parallel, they may sometimes be performed in reverse order, which may be dependent on the functions involved. Each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Although the present invention has been described in detail by way of preferred embodiments with reference to the accompanying drawings, the present invention is not limited thereto. Various equivalent modifications and substitutions may be made in the embodiments of the present invention by those skilled in the art without departing from the spirit and scope of the present invention, and it is intended that all such modifications and substitutions be within the scope of the present invention/be within the scope of the present invention as defined by the appended claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (9)
1. A clinical decision support system, comprising:
the acquisition module is used for acquiring clinical manifestation data of a patient;
the medical knowledge graph module is used for collecting various resources related to diseases to form a disease data sample knowledge base for inquiry and reading;
the reasoning decision module is used for reasoning out a disease data sample most relevant to the acquired clinical manifestation data of the patient based on a disease data sample knowledge base formed by the medical knowledge graph module, performing machine learning on the most relevant disease data sample, deciding a treatment scheme set, and recommending the treatment scheme set to a doctor, wherein the patient treatment scheme comprises examination to be implemented, relevant drug treatment and special disease early warning;
the plan processing module is used for selecting one treatment plan from the recommended treatment plan set by a doctor, and determining or modifying the selected treatment plan based on the clinical manifestation data of the patient to form a target treatment plan.
2. The clinical decision support system according to claim 1 wherein,
the medical knowledge graph module comprises a data collection unit, a medical knowledge graph construction unit and a medical graph verification unit;
the data collection unit is used for collecting data of a data source in the medical industry;
the medical knowledge graph construction unit is used for establishing a knowledge graph data mode of the mutual correlation mapping based on the collected data to form a medical knowledge graph;
and the medical map verification unit is used for verifying the formed medical knowledge map so as to ensure accuracy.
3. The clinical decision support system of claim 1 wherein the system further comprises a rationality check module;
the rationality checking module is used for checking the rationality of the examination, the related drug treatment and the special disease early warning which need to be implemented in the target treatment scheme;
and the alarm module is used for alarming when any one of examination to be implemented, related drug treatment and special disease early warning in the target treatment scheme is unreasonable.
4. The clinical decision support system of claim 1 wherein the system further comprises a regimen evaluation module;
and the scheme evaluation module is used for evaluating the implementation effect of the target treatment scheme and determining the current disease condition of the patient.
5. A method of clinical decision support, the method further comprising the steps of:
s1, acquiring clinical manifestation data of a patient;
s2, collecting various resources related to the diseases to form a disease data sample knowledge base for inquiry and reading;
s3, deducing a disease data sample most relevant to the acquired clinical manifestation data of the patient based on a disease data sample knowledge base formed by the medical knowledge graph module, performing machine learning on the most relevant disease data sample, deciding a treatment scheme set, and recommending the treatment scheme set to a doctor, wherein the patient treatment scheme comprises examination to be implemented, relevant drug treatment and special disease early warning;
s4, a doctor selects one treatment scheme from the recommended treatment scheme set, and determines or modifies the selected treatment scheme based on clinical manifestation data of the patient to form a target treatment scheme.
6. The clinical decision support method according to claim 5, wherein the step S1 comprises: data collection is carried out on a data source in the medical industry;
based on the collected data, establishing a knowledge graph data mode of the inter-related mapping to form a medical knowledge graph;
and verifying the formed medical knowledge graph to ensure accuracy.
7. The clinical decision support method according to claim 5, wherein the method further comprises:
checking the rationality of the examination to be implemented, the related drug treatment and the special disease early warning in the target treatment scheme;
alerting is performed when one of the examination to be performed, the associated medication, and the special disease pre-warning in the targeted therapy regimen is unreasonable.
8. The clinical decision support method according to claim 5, wherein the method further comprises:
and evaluating the implementation effect of the target treatment scheme to determine the current disease condition of the patient.
9. A computer readable storage medium storing a computer program, wherein the program when executed by a processor implements the method of any one of claims 5 to 8.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117438079A (en) * | 2023-12-19 | 2024-01-23 | 北京万方医学信息科技有限公司 | Method and medium for evidence-based knowledge extraction and clinical decision assistance |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117438079A (en) * | 2023-12-19 | 2024-01-23 | 北京万方医学信息科技有限公司 | Method and medium for evidence-based knowledge extraction and clinical decision assistance |
| CN117438079B (en) * | 2023-12-19 | 2024-03-12 | 北京万方医学信息科技有限公司 | Method and medium for evidence-based knowledge extraction and clinical decision assistance |
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