Deep Contextual Clinical Prediction with Reverse Distillation

Authors

  • Rohan Kodialam MIT CSAIL & IMES
  • Rebecca Boiarsky MIT CSAIL & IMES
  • Justin Lim MIT CSAIL & IMES
  • Aditya Sai Independence Blue Cross
  • Neil Dixit Independence Blue Cross
  • David Sontag MIT CSAIL & IMES

Keywords:

Healthcare, Medicine & Wellness, Time-Series/Data Streams, (Deep) Neural Network Algorithms

Abstract

Healthcare providers are increasingly using machine learning to predict patient outcomes to make meaningful interventions. However, despite innovations in this area, deep learning models often struggle to match performance of shallow linear models in predicting these outcomes, making it difficult to leverage such techniques in practice. In this work, motivated by the task of clinical prediction from insurance claims, we present a new technique called reverse distillation which pretrains deep models by using high-performing linear models for initialization. We make use of the longitudinal structure of insurance claims datasets to develop Self Attention with Reverse Distillation, or SARD, an architecture that utilizes a combination of contextual embedding, temporal embedding and self-attention mechanisms and most critically is trained via reverse distillation. SARD outperforms state-of-the-art methods on multiple clinical prediction outcomes, with ablation studies revealing that reverse distillation is a primary driver of these improvements. Code is available at https://github.com/clinicalml/omop-learn.

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Published

2021-05-18

How to Cite

Kodialam, R., Boiarsky, R., Lim, J., Sai, A., Dixit, N., & Sontag, D. (2021). Deep Contextual Clinical Prediction with Reverse Distillation. Proceedings of the AAAI Conference on Artificial Intelligence, 35(1), 249-258. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16099

Issue

Section

AAAI Technical Track on Application Domains