Improving Hospital Mortality Prediction with Medical Named Entities and Multimodal Learning

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...

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Bibliographic Details
Published in:arXiv.org 2018-11
Main Authors: Jin, Mengqi, Mohammad Taha Bahadori, Colak, Aaron, Bhatia, Parminder, Celikkaya, Busra, Bhakta, Ram, Senthivel, Selvan, Khalilia, Mohammed, Navarro, Daniel, Zhang, Borui, Doman, Tiberiu, Ravi, Arun, Liger, Matthieu, Kass-hout, Taha
Format: Article
Language:English
Subjects:
Acuity
Health services
Mortality
Natural language processing
Neural networks
Predictions
Representations
Unstructured data
Online Access:Get full text
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Summary: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.
ISSN:2331-8422