The Design of Critical Care Information System Supporting Clinical Decision Based on Deep Learning Recognition Method

Abstract

In recent years, the clinical decision support system (CDSS) has been gradually improved, which effectively reduces the probability of doctors’ misdiagnosis or missed diagnosis. Therefore, the clinical decision support system has always been a research hotspot, deep learning and collaborative filtering technologies are developing rapidly, and more and more are applied to different fields. Based on the deep learning technology, this paper conducts in-depth research on the methods of assisted diagnosis of clinical diseases and prediction of clinical high-risk diseases in the field of CDSS. Aiming at the problem of clinical decision support system, this article analyzes the deep learning identification method in depth and is committed to applying machine deep learning to clinical decision-making, changing the lack of information and its challenges to clinical decision-making. Based on previous studies, two unsupervised learning methods based on machine learning are proposed, namely user collaborative filtering and RBM, to improve CDSS. The experimental results show that the overall performance of the RBM-based method is the best. When the missing degree of the two data sets is 30.6%, the classification accuracy rate is still more than 92.8%.