数据科学
领域(数学)
计算机科学
健康档案
钥匙(锁)
质量(理念)
分析
非结构化数据
医疗保健
大数据
数据挖掘
政治学
哲学
法学
纯数学
认识论
计算机安全
数学
出处
期刊:Annual review of biomedical data science
[Annual Reviews]
日期:2021-07-20
卷期号:4 (1): 165-187
被引量:25
标识
DOI:10.1146/annurev-biodatasci-030421-030931
摘要
Electronic health records (EHRs) are becoming a vital source of data for healthcare quality improvement, research, and operations. However, much of the most valuable information contained in EHRs remains buried in unstructured text. The field of clinical text mining has advanced rapidly in recent years, transitioning from rule-based approaches to machine learning and, more recently, deep learning. With new methods come new challenges, however, especially for those new to the field. This review provides an overview of clinical text mining for those who are encountering it for the first time (e.g., physician researchers, operational analytics teams, machine learning scientists from other domains). While not a comprehensive survey, this review describes the state of the art, with a particular focus on new tasks and methods developed over the past few years. It also identifies key barriers between these remarkable technical advances and the practical realities of implementation in health systems and in industry.
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