大型抑郁症治疗结果预测中的机器学习方法:系统性审查
Veronica Atemnkeng Ntam1, Tatjana Huebner1, Michael Steffens1
1Research Division, Federal Institute for Drugs and Medical Devices, Bonn, North Rhine-Westphalia, Germany.
机器学习 (ML) 在预测主要抑郁症 (MDD) 治疗结果方面具有前景. 然而,目前的ML方法缺乏通用性,并面临监管挑战,限制其在欧盟的临床适用性.
科学领域:
- 医学的人工智能
- 临床决策支持系统
- 心理健康信息学
背景情况:
- 由于各种影响因素,预测严重抑郁症 (MDD) 的治疗成功是复杂的.
- 机器学习 (ML) 通过基于患者数据预测结果, 为个性化MDD治疗提供了一个有希望的途径.
- 基于ML的MDD决策支持系统的临床适用性尚未得到证实.
研究的目的:
- 评估已公布的ML方法在欧盟的临床环境中预测MDD治疗结果的适用性.
- 评估重点是质量,道德,社会和法律标准.
主要方法:
- 在PubMed和谷歌学者的文献搜索 (2016年1月至2024年12月) 寻找ML在MDD治疗结果预测中的应用.
- 基于验证,性能和符合欧盟伦理,社会和法律标准的ML模型适用性评估.
主要成果:
- 随机森林 (RF) 和支持矢量机 (SVM) 是最常见的ML方法.
- 使用多个患者数据类别的模型显示出比单个类别的模型更高的预测准确性.
- 外部验证是有限的,由于早期发展阶段,遵守社会,道德和法律标准是具有挑战性的.
结论:
- 目前用于预测MDD治疗结果的ML方法缺乏证明的通用性.
- 符合监管的挑战 (社会,道德,法律) 阻碍了欧盟的临床适用性.
- 需要进一步开发和验证ML工具在临床环境中有用.
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