Multi-disease prediction using LSTM recurrent neural networks

被引:63
作者
Men, Lu [1 ]
Ilk, Noyan [2 ]
Tang, Xinlin [2 ]
Liu, Yuan [1 ]
机构
[1] Zhejiang Univ, Coll Management, Dept Data Sci & Engn Management, 866 Yuhangtang Rd, Hangzhou 310058, Zhejiang, Peoples R China
[2] Florida State Univ, Coll Business, Dept Business Analyt Informat Syst & Supply Chain, 821 Acad Way, Tallahassee, FL 32306 USA
关键词
Deep learning; Electronic health records; Healthcare informatics; Disease prediction; DISEASE PROGRESSION; BIG-DATA; CLASSIFICATION; MODEL; RISK;
D O I
10.1016/j.eswa.2021.114905
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Prediction of future clinical events (e.g., disease diagnoses) is an important machine learning task in healthcare informatics research. In this work, we propose a deep learning approach to perform multi-disease prediction for intelligent clinical decision support. The proposed approach utilizes a long short-term memory network and extends it with two mechanisms (i.e., time-aware and attention-based) to conduct multi-label classification based on patients' clinical visit records. The former mechanism (time-aware) is used to handle the temporal irregularity across clinical visits whereas the latter mechanism (attention-based) assists in determining the importance of each visit for the prediction task. Using a large clinical record data set (over 5 million records) collected from a hospital in Southeast China, we show that our proposed approach outperforms a variety of traditional and deep learning methods in predicting future disease diagnoses. We further study the impacts of different time interval choices for the time-aware mechanism and compare the performances of existing attention-based mechanisms with the one proposed in our study. Our work has implications for supporting physician diagnoses via the use of intelligent systems and more broadly for improving the quality of healthcare service.
引用
收藏
页数:11
相关论文
共 56 条
[1]   Deep learning for biological image classification [J].
Affonso, Carlos ;
Debiaso Rossi, Andre Luis ;
Antunes Vieira, Fabio Henrique ;
de Leon Ferreira de Carvalho, Andre Carlos Ponce .
EXPERT SYSTEMS WITH APPLICATIONS, 2017, 85 :114-122
[2]  
[Anonymous], 2013, GENERATING SEQUENCES
[3]  
[Anonymous], 2017, ADV NEURAL INFORM PR
[4]  
[Anonymous], 2017, ADV NEURAL INFORM PR
[5]  
[Anonymous], 2011, NIPS 2011 WORKSH PER
[6]  
[Anonymous], 2016, P 1 MACH LEARN HEALT
[7]  
[Anonymous], 2011, Acm T. Intel. Syst. Tec., DOI DOI 10.1145/1961189.1961199
[8]   Informal Health Provider and Practical Approach to Lung Health interventions to improve the detection of chronic airways disease and tuberculosis at primary care level in Malawi: study protocol for a randomised controlled trial [J].
Banda, Hastings T. ;
Mortimer, Kevin ;
Bello, George A. F. ;
Mbera, Grace B. ;
Namakhoma, Ireen ;
Thomson, Rachael ;
Nyirenda, Moffat J. ;
Faragher, Brian ;
Madan, Jason ;
Malmborg, Rasmus ;
Stenberg, Berthe ;
Mpunga, James ;
Mwagomba, Beatrice ;
Gama, Elvis ;
Piddock, Katherine ;
Squire, Stephen B. .
TRIALS, 2015, 16
[9]   Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients [J].
Bates, David W. ;
Saria, Suchi ;
Ohno-Machado, Lucila ;
Shah, Anand ;
Escobar, Gabriel .
HEALTH AFFAIRS, 2014, 33 (07) :1123-1131
[10]  
Bayati Mohsen, 2015, AMIA Annu Symp Proc, V2015, P329