Machine Learning, Natural Language Processing, and the Electronic Health Record: Innovations in Mental Health Services Research

被引:35
作者
Edgcomb, Juliet Beni [1 ]
Zima, Bonnie [1 ,2 ]
机构
[1] Univ Calif Los Angeles, Dept Psychiat & Behav Sci, Los Angeles, CA 90095 USA
[2] Univ Calif Los Angeles, Ctr Hlth Serv & Soc, Los Angeles, CA USA
关键词
VALIDATION;
D O I
10.1176/appi.ps.201800401
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
An unprecedented amount of clinical information is now available via electronic health records (EHRs). These massive data sets have stimulated opportunities to adapt computational approaches to track and identify target areas for quality improvement in mental health care. In this column, three key areas of EHR data science are described: EHR phenotyping, natural language processing, and predictive modeling. For each of these computational approaches, case examples are provided to illustrate their role in mental health services research. Together, adaptation of these methods underscores the need for standardization and transparency while recognizing the opportunities and challenges ahead.
引用
收藏
页码:346 / 349
页数:4
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