Improving Hospital Mortality Prediction with Medical Named Entities and
Multimodal Learning
release_nkvmgs4hvzbcjgpl4ecfavd3gq
by
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 (+1 others)
2018
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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