Predicting readmission risk from doctors' notes

Abstract

We develop a model using deep learning techniques and natural language processing on unstructured text from medical records to predict hospital-wide $30$-day unplanned readmission, with c-statistic $.70$. Our model is constructed to allow physicians to interpret the significant features for prediction.

Accepted poster at NIPS 2017 Workshop on Machine Learning for Health (https://ml4health.github.io/2017/)

References in corpus (1)

• Comparing Rule-Based and Deep Learning Models for Patient Phenotyping

Cited by in corpus (1)

• Multimodal Ensemble Approach to Incorporate Various Types of Clinical Notes for Predicting Readmission