General patient personalized diagnosis and treatment scheme recommendation system based on cognitive map

Abstract

The invention discloses a general patient personalized diagnosis and treatment scheme recommendation system based on a cognitive map, and the system comprises a data collection module, a data preprocessing module, a data analysis and reasoning module and a recommendation result display module. And then, based on information such as diseases, symptoms and medication of the patient during treatment and the constructed general knowledge map, establishing a cognitive map of a personalized disease development trajectory of the patient, and further giving a personalized diagnosis and treatment scheme recommendation for the patient. A reasoning method based on a cognitive map is used, so that the system can really simulate the diagnosis and treatment thought of a clinician, and a clinical auxiliary decision-making tool which is interpretable and high in acceptability is provided for the clinician; according to the method, a personalized diagnosis and treatment scheme is formulated for a patient from symptoms, the patient is helped to find disease causes as soon as possible and receive targeted treatment, meanwhile, early screening of dangerous diseases can be achieved, and the patient is prompted to be transferred to a special department for treatment in time.

Description

technical field [0001] The invention belongs to the technical field of medical and health information, and in particular relates to a system for recommending personalized diagnosis and treatment plans for general patients based on cognitive maps. Background technique [0002] In my country, general practice medicine is a comprehensive medical professional discipline that is oriented to communities and families and integrates clinical medicine, preventive medicine, rehabilitation medicine, and humanities and social sciences. It was established in the 1990s and covers various fields. age, sex, organ systems, and health problems. The main service area of ​​general practice medicine is primary health care, with families and communities as the background, mainly dealing with common problems, and most of them are health problems in the undifferentiated stage of disease. General undifferentiated diseases refer to medically unexplained somatic symptoms or diseases that have not been...

AI Technical Summary

Problems solved by technology

[0004] 1. Existing technical solutions seldom analyze different types or different systems of diseases that patients may suffer from based on symptoms, and most of them focus on one or one type of disease that has been identified, such as diabetes and autism , infectious diseases, etc., but in general practice clinics, doctors are often faced with undifferentiated diseases, and patients with the same symptoms may need to be referred to different departments for follow-up treatment, so the current technical solutions cannot solve the problem well Current problems encountered in the field of general practice;

[0005] 2. The existing technical solutions are mainly used in the guidance or pre-interrogation process before the patient and the doctor face-to-face consultation and the self-monitoring of the patient at home. They are not embedded in the real-time communication activities with the doctor and cannot be truly applied to clinical scenarios. middle;

[0006] 3. Existing technical solutions basically use data-driven methods to extract a large number of qualified patient cohorts, and then based on the clinical data of patients before diagnosis, use traditional machine learning or deep learning methods to analyze and assist doctors in clinical decision support , the whole process is not integrated into clinical guidelines, expert consensus, etc., and lacks knowledge drive;

[0007] 4. Existing technical solutions are often based on the big data of a certain group of people for model training and data analysis. After changing the group of people, the effect of the model will drop a lot, that is, the generalization performance of the model is poor. At the same time, there is a "black box" problem in deep learning. The model Not interpretable or poorly interpretable

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