A Feature Optimized Deep Learning Model for Clinical Data Mining

WU Tianshu; CHEN Shuyu; TIAN Yingming; WU Peng

doi:10.1049/cje.2020.03.004

Volume 29 Issue 3

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WU Tianshu, CHEN Shuyu, TIAN Yingming, et al., “A Feature Optimized Deep Learning Model for Clinical Data Mining,” Chinese Journal of Electronics, vol. 29, no. 3, pp. 476-481, 2020, doi: 10.1049/cje.2020.03.004

Citation:

WU Tianshu, CHEN Shuyu, TIAN Yingming, et al., “A Feature Optimized Deep Learning Model for Clinical Data Mining,” Chinese Journal of Electronics, vol. 29, no. 3, pp. 476-481, 2020, doi: 10.1049/cje.2020.03.004

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A Feature Optimized Deep Learning Model for Clinical Data Mining

doi: 10.1049/cje.2020.03.004

1.
College of Computer Science, Chongqing University, Chongqing 400044, China;
2.
College of Automation, Chongqing University, Chongqing 400044, China

Funds: This work is supported by National Natural Science Foundation of China (No.61272399, No.61572090), University Doctoral Program Fund of the Ministry of Education (No.20110191110038), and the Chongqing Population Health Big Data Engineering Technology Research Center (No.cstc2016yfpt_gcjsyjzx0189)

More Information

Corresponding author: CHEN Shuyu (corresponding author) was born in 1963. He received the B.S., M.S. and Ph.D. degrees from Chongqing University. He is a professor and Ph.D. supervisor in Chongqing University. His main research interests include distributed computing, operating system and embedded system. (Email:sychen@cqu.edu.cn)
Received Date: 2019-09-25
Rev Recd Date: 2019-12-09
Publish Date: 2020-05-10

Abstract

Abstract

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

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References

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