Predicting healthcare trajectories from medical records: a deep learning approach

Trang Pham, Truyen Tran, Dinh Phung, Svetha Venkatesh

Research output: Contribution to journalArticleResearchpeer-review

160 Citations (Scopus)

Abstract

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, stored in electronic medical records are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors and models patient health state trajectories by the memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces methods to handle irregularly timed events by moderating the forgetting and consolidation of memory. DeepCare also explicitly models medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden – diabetes and mental health – the results show improved prediction accuracy.

Original languageEnglish
Pages (from-to)218-229
Number of pages12
JournalJournal of Biomedical Informatics
Volume69
DOIs
Publication statusPublished - May 2017
Externally publishedYes

Keywords

  • Electronic medical records
  • Healthcare processes
  • Irregular timing
  • Long-Short Term Memory
  • Predictive medicine

Cite this