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

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Electronics > 2020 > 29(3): 476-481

WU Tianshu, CHEN Shuyu, TIAN Yingming, WU Peng. A Feature Optimized Deep Learning Model for Clinical Data Mining[J]. Chinese Journal of Electronics, 2020, 29(3): 476-481. doi: 10.1049/cje.2020.03.004

Citation:

WU Tianshu, CHEN Shuyu, TIAN Yingming, WU Peng. A Feature Optimized Deep Learning Model for Clinical Data Mining[J]. Chinese Journal of Electronics, 2020, 29(3): 476-481. doi: 10.1049/cje.2020.03.004

WU Tianshu, CHEN Shuyu, TIAN Yingming, WU Peng. A Feature Optimized Deep Learning Model for Clinical Data Mining[J]. Chinese Journal of Electronics, 2020, 29(3): 476-481. doi: 10.1049/cje.2020.03.004

Citation:

PDF( 804 KB)

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)

FullText(HTML)

References(19)

References

D. W. Bates, S. Saria, L. O. Machado, et al., “Big data in health care: Using analytics to identify and manage highrisk and high-cost patients”, Health Affairs, Vol.33, No.7, pp.1123-1131, 2014.

C. H. Zhou Q. Wang, K. Z. Wu, et al., “Averaged one-dependence decision trees ensemble algorithm”, Acta Electronica Sinica, Vol.38, No.2, pp.434-438, 2010. (in Chinese)

G. Falavigna, G. Costantino, R. Furlan, et al., “Artificial neural networks and risk stratification in emergency departments”, Internal and Emergency Medicine, Vol.14, No.2, pp.291-299, 2019.

X. G. Gao, F. Li and K. F. Wan, “Accelerated learning for restricted boltzmann machine with a novel momentum algorithm”, Chinese Journal of Electronics, Vol.27, No.3, pp.483-487, 2018.

J. Shu, S. Liu, L. L. Liu, et al., “Research on link quality estimation mechanism for wireless sensor networks based on support vector machine”, Chinese Journal of Electronics, Vol.26, No.2, pp.377-384, 2017.

D. M. Mu and K. Ren, “Comparison of three data mining algorithms in knowledge discovery of EMR”, Modern Library and Information Technology, Vol.32, No.6, pp.102-109, 2016. (in Chinese)

R. Duggal, S. Shukla, S. Chandra, et al., “Impact of selected pre-processing techniques on prediction of risk of early readmission for diabetic patients in India”, International Journal of Diabetes in Developing Countries, Vol.36, No.4, pp.469-476. 2016.

K. S. Li, J. P. Fan, F. F. Zhou, et al., “Early diagnosis algorithms for ICU emergencies”, Integration Technology, Vol.1, No.2, pp.13-19, 2012. (in Chinese)

N. Lavrac and B. Zupan, Data Mining in Medicine, Springer, New York, USA, pp.1111-1136, 2010.

U. R. Acharya, S. L. Fernandes, J. E. WeiKoh, et al., “Automated detection of alzheimer’s disease using brain MRI images-A study with various feature extraction techniques”, Journal of Medical Systems, Vol.43, No.9, pp.1-14, 2019.

M. Khalilia, S. Chakraborty and M. Popescu, “Predicting disease risks from highly imbalanced data using random forest”, BMC Medical Informatics and Decision Making, Vol.11, No.1, pp.51-51, 2011.

S. P. Zeng and J. L. Wang, “Predicted molecular ratio of aluminum reduction based on random forest and neural network”, Light Metals, No.12, pp.21-25. 2018. (in Chinese)

G. Litjens, T. Kooi, B. E. Bejnordi, et al., “A survey on deep learning in medical image analysis”, Medical Image Analysis, Vol.42, No.9, pp.60-88, 2017.

E. Lima, X. Sun, J. Dong, et al., “Learning and transferring convolutional neural network knowledge to ocean front recognition”, IEEE Geoscience and Remote Sensing Letters, Vol.14, No.3, pp.354-358, 2017.

F. Ghasemi, A. Fassihi, H. P. Sanchez, et al., “The role of different sampling methods in improving biological activity prediction using deep belief network”, Journal of Computational Chemistry, Vol.38, No.4, pp.195-203. 2017.

Y. S. Zhang, J. Zheng, Y. L. Jia, et al., “A text sentiment classification modeling method based on coordinated CNNLSTM-attention model”, Chinese Journal of Electronics, Vol.28, No.1, pp.120-126, 2019.

A. E. Johnson, T. J. Pollard, L. Shen, et al., “MIMIC-III, a freely accessible critical care database”, Scientific Data, Vol.3, No.160035, DOI:10.1038/sdata.2016.35, 2016.

M. Kumari and S. Godara, “Comparative study of data mining classification methods in cardiovascular disease prediction”, International Journal of Computer Science and Technology, Vol.2, No.2, pp.304-308, 2011.

International Classification of Diseases, Ninth Revision ICD-9 PDF. Available at:https://simba.isr.umich.edu/restricted/docs/Mortality/icd_09_codes.pdf.

Relative Articles

Supplements(0)

Cited By

Proportional views