Clinical text provides essential information to estimate the acuity of a patient during hospital stays in addition to structured clinical data. In this study, we explore how clinical text can complement a clinical predictive learning task. We leverage an internal medical natural language processing service to perform named entity extraction and negation detection on clinical notes and compose selected entities into a new text corpus to train document representations. We then propose a multimodal neural network to jointly train time series signals and unstructured clinical text representations to predict the in-hospital mortality risk for ICU patients. Our model outperforms the benchmark by 2% AUC.
@article{arxiv.1811.12276,
title = {Improving Hospital Mortality Prediction with Medical Named Entities and Multimodal Learning},
author = {Mengqi Jin and Mohammad Taha Bahadori and Aaron Colak and Parminder Bhatia and Busra Celikkaya and Ram Bhakta and Selvan Senthivel and Mohammed Khalilia and Daniel Navarro and Borui Zhang and Tiberiu Doman and Arun Ravi and Matthieu Liger and Taha Kass-hout},
journal= {arXiv preprint arXiv:1811.12276},
year = {2018}
}
Comments
Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.07216