HomeHome

Readmission prediction via deep contextual embedding of clinical concepts

Cao Xiao; Tengfei Ma; Adji Bousso Dieng; David Meir Blei; Fei Wang

Title:
Readmission prediction via deep contextual embedding of clinical concepts
Author(s):
Xiao, Cao
Ma, Tengfei
Dieng, Adji Bousso
Blei, David Meir
Wang, Fei
Date:
Type:
Articles
Department(s):
Computer Science
Statistics
Volume:
13
Persistent URL:
Book/Journal Title:
PLoS ONE
Abstract:
Objective Hospital readmission costs a lot of money every year. Many hospital readmissions are avoidable, and excessive hospital readmissions could also be harmful to the patients. Accurate prediction of hospital readmission can effectively help reduce the readmission risk. However, the complex relationship between readmission and potential risk factors makes readmission prediction a difficult task. The main goal of this paper is to explore deep learning models to distill such complex relationships and make accurate predictions. Materials and methods We propose CONTENT, a deep model that predicts hospital readmissions via learning interpretable patient representations by capturing both local and global contexts from patient Electronic Health Records (EHR) through a hybrid Topic Recurrent Neural Network (TopicRNN) model. The experiment was conducted using the EHR of a real world Congestive Heart Failure (CHF) cohort of 5,393 patients. Results The proposed model outperforms state-of-the-art methods in readmission prediction (e.g. 0.6103 ± 0.0130 vs. second best 0.5998 ± 0.0124 in terms of ROC-AUC). The derived patient representations were further utilized for patient phenotyping. The learned phenotypes provide more precise understanding of readmission risks. Discussion Embedding both local and global context in patient representation not only improves prediction performance, but also brings interpretable insights of understanding readmission risks for heterogeneous chronic clinical conditions. Conclusion This is the first of its kind model that integrates the power of both conventional deep neural network and the probabilistic generative models for highly interpretable deep patient representation learning. Experimental results and case studies demonstrate the improved performance and interpretability of the model.
Subject(s):
Computer science
Hospital utilization--Forecasting
Hospital records
Neural networks (Computer science)
Publisher DOI:
https://doi.org/10.1371/journal.pone.0195024
Item views
14
Metadata:
text | xml
Suggested Citation:
Cao Xiao, Tengfei Ma, Adji Bousso Dieng, David Meir Blei, Fei Wang, , Readmission prediction via deep contextual embedding of clinical concepts, Columbia University Academic Commons, .

Columbia University Libraries | Policies | FAQ