Open Access<p>Deep Learning-Based Method of Diagnosing Hyperlipidemia and Providing Diagnostic Markers Automatically</p>
Author(s) -
Yuliang Liu,
Quan Zhang,
Geng Zhao,
Guohua Liu,
Zhiang Liu
Publication year - 2020
Publication title -
diabetes, metabolic syndrome and obesity
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.853
H-Index - 43
ISSN - 1178-7007
DOI - 10.2147/dmso.s242585
Subject(s) - interpretability , diagnostic accuracy , diagnostic model , artificial intelligence , machine learning , computer science , reliability (semiconductor) , clinical diagnosis , deep learning , data mining , medicine , intensive care medicine , power (physics) , physics , quantum mechanics
The research of auxiliary diagnosis has always been one of the hotspots in the world. The implementation of auxiliary diagnosis support algorithm for medical text data faces challenges with interpretability and creditability. The improvement of clinical diagnostic techniques means not only the improvement of diagnostic accuracy but also the further study of diagnostic basis. Traditional research methods for diagnostic markers often require a large amount of time and economic costs. Research objects are often dozens of samples, and it is, therefore, difficult to synthesize large amounts of data. Therefore, the comprehensiveness and reliability of traditional methods have yet to be improved. Therefore, the establishment of a model that can automatically diagnose diseases and automatically provide a diagnostic basis at the same time has a positive effect on the improvement of medical diagnostic techniques.