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arXiv · 1811.12276

Improving Hospital Mortality Prediction with Medical Named Entities and Multimodal Learning

Mengqi Jin·Mohammad Taha Bahadori·Aaron Colak·Parminder Bhatia·Busra Celikkaya·Ram Bhakta·Selvan Senthivel·Mohammed Khalilia·Daniel Navarro·Borui Zhang·Tiberiu Doman·Arun Ravi·Matthieu Liger·Taha Kass-hout
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Abstract

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.

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Mengqi Jin, Mohammad Taha Bahadori, Aaron Colak, Parminder Bhatia, Busra Celikkaya, Ram Bhakta, Selvan Senthivel, Mohammed Khalilia, Daniel Navarro, Borui Zhang, Tiberiu Doman, Arun Ravi, Matthieu Liger, Taha Kass-hout. 2018-11-29. Improving Hospital Mortality Prediction with Medical Named Entities and Multimodal Learning. https://arxiv.org/abs/1811.12276

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