在住院患者中抑郁症的案例鉴定 电子医疗记录:范围审查
Allison Grothman1, William J Ma1, Kendra G Tickner1
1Centre for Health Informatics, Cumming School of Medicine, University of Calgary, CWPH Building, 3280 Hospital Drive NW, Calgary, AB, T2N 4Z6, Canada, 1 4032202779, 1 4032109744.
JMIR medical informatics
|October 14, 2024
概括
对抑郁症的电子病历 (EMR) 审查是无效的. 机器学习和EMR中的自然语言处理可以改善抑郁症表型,但需要更多的验证才能获得可靠的病例识别算法.
科学领域:
- 医疗信息学 医疗信息学
- 计算精神病学是一种计算精神病学.
- 健康 数据科学 数据科学
背景情况:
- 电子病历 (EMR) 包含大量的临床数据,但手动审查以识别疾病是低效的.
- 使用机器学习 (ML) 和自然语言处理 (NLP) 的基于EMR的表型化为心理健康研究提供了一个有前途的方法.
- 从EMR中开发精确的抑郁症病例识别算法对于推进研究和临床实践至关重要.
研究的目的:
- 对基于EMR的抑郁症表型化算法进行范围审查.
- 评估基于EMR的抑郁病例识别方法的现状.
- 为利用和开发新的抑郁症表型算法提供指导.
主要方法:
- 根据PRISMA-ScR指南进行了全面的范围审查.
- 从2000年1月到2023年5月,在Embase,MEDLINE和APA PsycInfo数据库中进行了搜索.
- 关键词集中在EMR,病例识别和抑郁症,导致对20项相关研究的评估.
主要成果:
- 该审查确定了20项基于EMR的抑郁症表型算法研究.
- 大多数研究来自美国 (75%),其次是英国 (15%) 和西班牙 (10%).
- 采用了数据驱动和基于临床规则的方法,算法性能根据数据可访问性而异.
结论:
- 通过ML和NLP加强对结构化和非结构化EMR数据的利用,可以显著改善抑郁症表型.
- 基于EMR的抑郁病例识别算法的进一步验证对于确保其可靠性和临床实用性至关重要.
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