Readmission prediction via deep contextual embedding of clinical concepts. Academic Article uri icon

Overview

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.

publication date

  • April 9, 2018

Research

keywords

  • Electronic Health Records
  • Heart Failure
  • Models, Statistical
  • Patient Discharge
  • Patient Readmission

Identity

PubMed Central ID

  • PMC5890980

Scopus Document Identifier

  • 85045142498

Digital Object Identifier (DOI)

  • 10.1016/j.jbi.2014.03.016

PubMed ID

  • 29630604

Additional Document Info

volume

  • 13

issue

  • 4