Analysis method and system of dynamic disease model base

Abstract

The invention relates to an analysis method and a system of a dynamic disease model base. The method comprises the following steps of S1, establishing a disease model according to a symptom; S2, inputting an effective medical record set into the disease model so as to carry out analysis and processing; S3, according to effective medical records, updating the disease model and then acquiring a newdisease model; and S4, inputting patient correlation symptom information into the model, selecting a disease list according with a strong relation symptom in the disease model according to the symptominformation, then using a disease derivation formula of the corresponding disease list to calculate various diseases and probabilities and pushing a result to a clinician. The system comprises a dynamic reasoning machine. The dynamic reasoning machine is connected to an information inputting end and a user end in a communication connection mode. In the invention, a static clinical knowledge baseand a large data method are effectively fused so as to generate a dynamic disease model. Simultaneously, medical data is converted into a factor used for promoting the disease model to be optimized atany time and the disease model base is dynamically adjusted. A diagnosis speed and accuracy are increased and clinical data is instantly converted into experience.

Description

technical field [0001] The invention relates to the technical field of medical data analysis, and relates to an analysis method and system for a dynamic disease model library. Background technique [0002] According to literature review, the three main reasons for misdiagnosis in the process of clinical diagnosis and treatment are inexperienced doctors, failure to carry out the most powerful examination items, and lack of consensus on consultation and examination. Inexperienced doctors have several manifestations. The doctor’s seniority is relatively young, or the professional field is very strong but not familiar with other fields; the most powerful examination is before the diagnosis, when the attending doctor has a preliminary impression or a preliminary diagnosis, in order to To further confirm the results, additional inspection items will be added to verify the preliminary speculation. At this time, due to the complexity of the disease, it is very likely that the inspec...

AI Technical Summary

Problems solved by technology

[0005] Although the method of static knowledge base is more effective for some common diseases with easily identifiable symptoms, there are still more diseases whose symptom combination patterns are not clear. It is also constantly enriched. The static knowledge base is often clinically summarized, and then applied and promoted after academic discussions. The timeliness is low

[0006] The multi-factor regression method relies on the analysis of big data, which naturally has certain requirements for data quality and quantity. Due to the rapid development of domestic medical information and the complexity and variety of information systems, the quality of current historical data is not high, and the information is also insufficient. Due to the incomplete current situation, it is extremely difficult for most small and medium-sized medical institutions to implement this method. Therefore, from the perspective of objective conditions, not all hospitals have this condition. In terms of clinical application, the current popularity and usability Big discount

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