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机器学习在预测中风后抑郁症方面的准确性:系统性审查和元分析

Husile Husile¹, Qinglin Bao², Sarula Sarula²

¹Inner Mongolia Medical University, Hohhot Inner Mongolia, China.

Brain and behavior

|May 26, 2025

概括

机器学习模型对预测中风后抑郁症充满希望,但过度匹配的风险需要仔细考虑. 这些模型可以帮助早期识别和预防这种常见的中风并发症的预防策略.

科学领域:

神经学神经学
数据科学数据科学数据科学
精神病学是一个精神病学.

背景情况:

脑卒中后抑郁症 (PSD) 显著影响患者的生活质量.
早期发现PSD对于有效预防至关重要.
对于PSD的机器学习 (ML) 的预测准确性仍在争论中.

研究的目的:

系统地评估ML模型在预测PSD方面的有效性.
为PSD预测进行现有ML研究的元分析.

主要方法:

在主要数据库 (PubMed,Embase,Cochrane,Web of Science) 进行系统的文献搜索,截至2023年11月20日.
包括28项研究,包括85,223名患者.
使用预测模型风险偏差评估工具 (PROBAST) 进行质量评估.

主要成果:

分析包括了28项研究,涉及85,223名患者.
验证集中的c指数低于培训集中的c指数,这表明潜在的过拟合.
尽管有潜在的过拟合,但ML模型在预测PSD方面表现出可取的准确性.
超回归显示,随着随访时间的延长,模型性能 (c指数) 没有下降.

结论:

机器学习模型是预测中风后抑郁症的有效工具.
这些模型可以预测PSD风险在不同的时间点.
基于ML的预测为个性化PSD预防提供了有价值的工具.

关键词:

脑卒中后的抑郁症是什么预测模型预测模型系统性审查系统性审查

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