机器学习算法在自杀风险预测中的作用:对临床研究进行系统性审查-元分析
Houriyeh Ehtemam1, Shabnam Sadeghi Esfahlani1, Alireza Sanaei1
1School of Engineering and the Built Environment, Anglia Ruskin University, Chelmsford, UK.
BMC medical informatics and decision making
|May 27, 2024
概括
机器学习 (ML) 模型在预测自杀风险方面表现有希望,随机森林和XGBoost实现了高准确度. 关键的风险因素包括年龄,性别,药物滥用和心理健康状况.
科学领域:
- 公共卫生 公共卫生
- 计算精神病学是一种计算精神病学.
- 数据科学数据科学数据科学
背景情况:
- 自杀是一个重要的公共卫生问题,需要有效的预测和预防策略.
- 机器学习 (ML) 提供了提高自杀企图预测的潜力.
- 了解自杀风险因素对于干预至关重要.
研究的目的:
- 系统地审查和评估ML算法在自杀风险预测中的性能.
- 综合关于ML算法有效性的证据,并确定自杀风险因素.
- 为了识别在应用 ML 预防自杀的知识差距.
主要方法:
- 对PubMed,Scopus,Web of Science和SID数据库进行系统审查.
- 包括41项研究 (2011-2022年) 使用ML进行自杀风险预测 (不包括NLP和图像处理).
- 混合方法方法分析算法性能和识别风险因素.
主要成果:
- 随机森林 (精度0.94) 和XGBoost (AUC 0.97) 在评估的ML算法中表现优越.
- 通常识别的自杀风险因素包括年龄,性别,药物滥用,抑郁,焦虑,酒精消费,婚姻状况,收入,教育和职业.
- 算法特定的性能有所不同,突出显示了模型选择对自杀风险预测的重要性.
结论:
- 机器学习算法显示出潜力,但它们在预测自杀风险方面的临床有效性仍在争论中.
- 需要在临床环境中对ML进行进一步的研究,以及澄清伦理考虑.
- 本综述提供了关于针对性预防的ML性能和风险因素的见解.
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