Clinical auxiliary decision method and device

Abstract

The invention discloses a clinical auxiliary decision method and a device, wherein the clinical auxiliary decision method comprises the steps of receiving medical record data; analyzing the medical record data based on different statistics strategies, and obtaining preliminary diagnosis information in different statistics strategies; and fusing the preliminary diagnosis information for obtaining afinal diagnosis conclusion candidate result. According to the method, different statistics strategies with different focuses are utilized for analyzing the medical record data in multiple dimensions,thereby obtaining a plurality of sets of preliminary diagnosis information; and finally the preliminary diagnosis information in multiple strategies are fused, thereby obtaining the final diagnosis conclusion candidate result after comprehensive processing, thereby replacing an immediate inference mode of a signal statistics model, performing comprehensive decision according to the analysis results of multiple strategies, and effectively assisting a doctor in performing accurate and comprehensive disease diagnosis.

Description

technical field [0001] The invention relates to the field of intelligent medical treatment, in particular to a clinical assistant decision-making method and device. Background technique [0002] With the improvement of the national economy, my country's medical industry has developed rapidly in recent years, but it still cannot meet the huge medical needs of the people. The problem of unbalanced development of medical care between regions is still very serious. Therefore, the country is currently vigorously supporting primary medical care. development of. The core of the development of grassroots medical care is to improve the diagnosis and treatment level of grassroots doctors, so as to better provide diagnosis and treatment services for the general public. [0003] Clinical Decision Support System (Clinical Decision Support System, CDSS) is a computer-aided application system for doctors' diagnosis and treatment. Assistance and tips in various aspects, involving multiple ...

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Problems solved by technology

[0007] However, the main problem of the above-mentioned prior art solutions is that the medical field is relatively more complicated, for example, there will be multiple diseases with similar symptoms on a single organ tissue that need to be distinguished, and diseases between different organ tissues also have similar symptoms, At the same time, the classification system of diseases is related to the actual treatment methods; moreover, there are diseases such as trauma and oral diseases that require accurate location of the disease site, and there are diseases such as infants and pregnant women who have different symptoms and physical indicators due to their own particularities. Situations that are very different from those of normal patients, such as the above, etc., it is difficult to solve all medical diagnosis problems with only a single model

[0009] 1) The construction of the knowledge base is complex: modern medicine covers various tissues and organs of the human body, and also involves multiple disciplines such as pathological examination. In order to ensure the rationality of the system, the construction of the knowledge base often has high requirements for the comprehensiveness and correctness of knowledge , which requires the manual input of a large number of medical experts

[0010] 2) Difficult to maintain and update the knowledge base: Modern medicine is a science with rapid development and progress in both academic research and clinical practice. For clinical decision support systems, the knowledge base needs to be kept updated in real time, which poses a great challenge to the maintenance of the knowledge base. High requirements also require a lot of labor

[0011] 3) Limited expression ability: due to the complexity of medical knowledge, on the one hand, it is not easy to summarize these knowledge and express them as rules that can be used for logical reasoning, and requires a lot of expert input; on the other hand, a lot of knowledge and experience in medicine are vague It is not easy to express as rules, that is, the established rules cannot represent all medical knowledge and experience

At the same time, when the inference rules reach a certain number, there may be logical conflicts, and it is difficult to resolve conflicts based on a large number of rules.

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