A disease assessment method, a terminal apparatus and a computer readable medium

Abstract

The embodiments of the invention provide a disease assessment method, a terminal apparatus and a computer readable medium. The method comprises the steps of: acquiring target sleep apnea data in a first unit time, and inputting improved HMM models with the number of a to obtain a sleep apnea assessment result output by a target improved HMM model to indicate whether a user corresponding to the target sleep apnea data has experienced sleep apnea in the first unit time, wherein the target improved HMM model is the improved HMM model with the maximum likelihood in the improved HMM models with thenumber of a, the observation probability matrixes of the improved HMM models are RF models, and the RF models of the different improved HMM models are obtained by inputting and training different first sleep apnea sample data based on a first classification disease level. According to the embodiments, the method can intelligently diagnose the sleep apnea disease for users and improve the accuracy, convenience and rapidness of disease assessment.

Description

technical field [0001] The present invention relates to the field of intelligent medical technology, in particular to a disease assessment method, a terminal device and a computer-readable medium. Background technique [0002] Sleep apnea syndrome (sleep apnea syndrome, SAS) is a symptom whose etiology and pathogenesis are not clear at present. Because apnea can cause recurrent hypercapnia and nighttime hypoxia, it can lead to complications such as coronary heart disease, diabetes, and cerebrovascular disease, and severe cases can even cause sudden death at night. How to accurately diagnose sleep apnea syndrome is an important part of night medicine. [0003] In order to solve the above problems, the SAS detection methods proposed in the prior art mainly include: X-ray projection detection, polysomnogram (Polysolnogram, PSG) detection, and nasopharyngeal fiberscope detection. However, it has been found in practice that the SAS detection methods provided by the prior art al...

AI Technical Summary

Problems solved by technology

However, it has been found in practice that the SAS detection methods provided by the prior art all rely on expensive medical equipment and are difficult to popularize.

In particular, the PSG detection method needs to be tested for more than 7 hours in a sleep monitoring room equipped with expensive medical equipment, and there are many monitoring signals, and professional and technical personnel are required to make a diagnosis to analyze whether the user suffers from sleep apnea syndrome And the severity of the user's condition when suffering from sleep apnea syndrome, etc., which brings a lot of labor costs and equipment costs, and is not easy to popularize

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