University of Pittsburgh

Monitoring Mortality Risk with Long Short-Term Memory Recurrent Neural Network

graduate student
Date: 
Friday, April 12, 2019 - 1:00pm - 1:30pm

In intensive care units (ICU), mortality prediction is a critical factor not only for effective medical intervention but also for allocation of clinical resources. Structured EHR data contains valuable information for resolving this task, but current solutions usually require human-engineered features, which are both laborious and sensitive to missing values.
Inspired by language-related models, we design a new framework for dynamic monitoring of patients’ mortality risk. Our framework relies on automatically extracted feature representations from all relevant medical events based on most recent history. Specifically, our model uses latent semantic analysis (LSA) to encode the patients’ states into low-dimensional embeddings, which are further fed to long short-term memory networks for mortality risk prediction. We observe that bidirectional long short-term memory demonstrates competitive performance, probably due to successful capture of both forward and backward temporal dependencies.

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