Combining LSTM and Latent Topic Modeling for Mortality Prediction

2017/09/08 by Yohan Jo, Lisa Lee, Jo, Yohan +3 · 2 citations
Computer Science · Medicine · #COVID-19 diagnosis using AI #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning in Healthcare #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1709.02842

openalex publication_date 2017/09/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

There is a great need for technologies that can predict the mortality of patients in intensive care units with both high accuracy and accountability. We present joint end-to-end neural network architectures that combine long short-term memory (LSTM) and a latent topic model to simultaneously train a classifier for mortality prediction and learn latent topics indicative of mortality from textual clinical notes. For topic interpretability, the topic modeling layer has been carefully designed as a single-layer network with constraints inspired by LDA. Experiments on the MIMIC-III dataset show that our models significantly outperform prior models that are based on LDA topics in mortality prediction. However, we achieve limited success with our method for interpreting topics from the trained models by looking at the neural network weights.

Citations

• Learning to Diagnose with LSTM Recurrent Neural Networks
• Modeling Documents with Deep Boltzmann Machines

Cited by

• Unsupervised patient representations from clinical notes with interpretable classification decisions
• ISeeU2: Visually Interpretable ICU mortality prediction using deep learning and free-text medical notes

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