A Feature Optimized Deep Learning Model for Clinical Data Mining

被引：0

作者：

WU Tianshu ^{[1
]}

CHEN Shuyu ^{[1
]}

TIAN Yingming ^{[2
]}

WU Peng ^{[2
]}

机构：

[1] College of Computer Science, Chongqing University

[2] College of Automation, Chongqing University

来源：

Chinese Journal of Electronics | 2020年 / 29卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Clinical data mining; Random forests(RF); Long short-term memory(LSTM);

D O I：

暂无

中图分类号：

R-05 [医学与其他学科的关系]; TP311.13 []; TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1001 ; 1201 ; 1405 ;

摘要：

the Artificial intelligence(AI) has gradually changed from frontier technology to practical application with the continuous progress of deep learning technology in recent years. In this paper, the Random forest(RF) algorithm is adopted to preprocess and optimize the feature subset of ICU data sets. Then these optimized feature subsets are used as input of Long shortterm memory(LSTM) deep learning model, and the early disease prediction of ICU inpatients is carried out by the method of neural network deep learning. Experiments show that this prediction method has higher prediction accuracy compared with other machine learning and deep learning models.

引用

页码：476 / 481

页数：6

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