Department of Psychiatry and Behavioral Sciences (Edgcomb, Zima) and Center for Health Services and Society (Zima), University of California, Los Angeles, Los Angeles.
Psychiatr Serv. 2019 Apr 1;70(4):346-349. doi: 10.1176/appi.ps.201800401. Epub 2019 Feb 20.
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.
现在，通过电子健康记录 (EHR) 可以获得前所未有的大量临床信息。这些大数据集为适应计算方法提供了机会，以跟踪和确定精神保健质量改进的目标领域。在本专栏中，描述了 EHR 数据科学的三个关键领域：EHR 表型分析、自然语言处理和预测建模。对于这些计算方法中的每一种，都提供了案例示例来说明它们在精神卫生服务研究中的作用。这些方法的结合强调了标准化和透明度的必要性，同时也认识到了未来的机遇和挑战。
