机器学习模型的准确性和可传输性,用于与纵向临床记录进行青少年自杀预测
Chengxi Zang1,2, Yu Hou1,2, Daoming Lyu1,2
1Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, Cornell, USA.
Translational psychiatry
|July 31, 2024
概括
机器学习模型在预测青少年自杀企图方面表现有前途. 当它们应用于新数据 (可移植性) 时,它们的性能会有所不同,这会影响临床使用,更简单的模型通常表现得和复杂的模型一样好.
科学领域:
- 临床信息学 临床信息学
- 计算精神病学是一种计算精神病学.
- 青少年健康 青少年健康
背景情况:
- 机器学习 (ML) 模型显示了使用现实数据预测青少年自杀企图的潜力.
- 这些ML模型在不同数据集和临床环境中的可传输性仍然在很大程度上未经检查,这阻碍了广泛的临床采用.
研究的目的:
- 开发和比较基于ML的自杀预测模型的交叉数据性能,这些模型在各种现实数据集上进行训练.
- 评估ML模型在不同的医疗保健环境 (住院,门诊) 和数据类型 (索赔,EHR) 中的可移植性.
主要方法:
- 开发了ML自杀预测模型,使用来自康涅狄格州全付款人索赔数据库,医院住院患者出院数据库和堪萨斯州卫生信息网络的电子健康记录的数据.
- 在源数据集上评估模型性能,然后将其应用 (运输) 到目标数据集,比较结果.
- 包括285,320名患者,其中3389名 (1.2%) 被确定为自杀企图,其中66%是女性.
主要成果:
- 复杂的深度长期短期记忆神经网络模型在本地或传输性能方面没有超过更简单的规范化后勤回归模型.
- 运输模型的性能各不相同,其中一些显示精度下降,而另一些则与源性能相比有所改善.
- 运输模型,虽然通常受局部性能限制,但在目标数据集中确定了额外的自杀企图案例.
结论:
- 发现了青少年自杀预测模型的复杂可运输性模式.
- 更简单的ML模型可以实现令人满意的运输性能,这表明更广泛的临床适用性.
- 需要对模型通用性的进一步研究,以促进强大的自杀预测工具的开发.
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