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Abstract: In this paper, we propose a novel neural network architecture for clinical text mining. We formulate this hybrid neural network model (HNN), composed of recurrent neural network and deep residual network, to jointly predict the presence and period assertion values associated with medical events in clinical texts. We evaluate the effectiveness of our model on a corpus of expert-annotated longitudinal Electronic Health Records (EHR) notes from Cancer patients. Our experiments show that HNN improves the joint assertion classification accuracy as compared to conventional baselines.

Learning Objective 1: Formulate an effective approach to address the classification task of the presence and period assertions ascribed to medical entity in clinical text.

Authors:

RUMENG LI (Presenter)
University of Massachusetts, Amherst

Abhyuday Jagannatha, University of Massachusetts, Amherst
Hong Yu, University of Massachusetts Medical School

Presentation Materials:

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