使用机器学习与电子健康记录数据预测和诊断抑郁症:系统性审查
David Nickson1, Caroline Meyer2, Lukasz Walasek3
1WMG, University of Warwick, Coventry, UK. david.nickson@warwick.ac.uk.
BMC medical informatics and decision making
|November 28, 2023
概括
机器学习和电子健康记录显示出预测抑郁症的前景. 虽然性能强,但这些先进的抑郁症预测模型的概括性和解释性仍然是这些先进抑郁症预测模型的关键挑战.
科学领域:
- 计算精神病学是一种计算精神病学.
- 医疗信息学 医疗信息学
- 机器学习在医疗保健中的应用
背景情况:
- 抑郁症对个人和社会产生重大影响.
- 电子健康记录 (EHR) 为健康预测提供了大量数据.
- 机器学习 (ML) 方法越来越多地应用于EHR数据.
结论:
- ML与EHR相结合,对抑郁症的预测具有显著的潜力.
- 在审查的研究中,模型的表现与初级保健发现相匹配或超过.
- 关于ML模型在抑郁症预测中的概括性和解释性的挑战仍然存在.
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