event-icon
Description

Abstract: Mental health is increasingly recognized an important topic in healthcare. Information concerning psychiatric symp-toms is critical for the timely diagnosis of mental disorders, as well as for the personalization of interventions. How-ever, the diversity and sparsity of psychiatric symptoms make it challenging for conventional natural language pro-cessing techniques to automatically extract such information from clinical text. To address this problem, this study takes the initiative to use and adapt word embeddings from four source domains – intensive care, biomedical litera-ture, Wikipedia and Psychiatric Forum – to recognize symptoms in the target domain of psychiatry. We investigated four different approaches including 1) only using word embeddings of the source domain, 2) directly combining data of the source and target to generate word embeddings, 3) assigning different weights to word embeddings, and 4) retraining the word embedding model of the source domain using a corpus of the target domain. To the best of our knowledge, this is the first work of adapting multiple word embeddings of external domains to improve psychiatric symptom recognition in clinical text. Experimental results showed that the last two approaches outperformed the baseline methods, indicating the effectiveness of our new strategies to leverage embeddings from other domains.

Learning Objective 1: Learning about using deep leaerning based algorithm for clinical named entity recogntion
Learning about using domain adaptation methods to leverage word embeddings of multiple domains

Authors:

Yaoyun Zhang (Presenter)
The University of Texas Health Science Center at Houston

HEE-JIN LEE, The University of Texas Health Science Center at Houston
Jingqi Wang, The University of Texas Health Science Center at Houston
Trevor Cohen, The University of Texas Health Science Center at Houston
Kirk Roberts, The University of Texas Health Science Center at Houston
Hua Xu, The University of Texas Health Science Center at Houston

Presentation Materials:

Keywords