UMMS Affiliation

Department of Quantitative Health Sciences

Publication Date

2018-04-16

Document Type

Conference Proceeding

Disciplines

Artificial Intelligence and Robotics | Databases and Information Systems | Health Information Technology

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.

Keywords

clinical text mining, data mining, neural networks, electronic health records

Rights and Permissions

Copyright ©2017 AMIA - All rights reserved. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose

Source

AMIA Annu Symp Proc. 2018 Apr 16;2017:1149-1158. eCollection 2017.

Journal/Book/Conference Title

AMIA ... Annual Symposium proceedings. AMIA Symposium

Related Resources

Link to Article in PubMed

PubMed ID

29854183

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