机器学习自杀风险模型在美洲印第安人群中的表现
Emily E Haroz1,2, Paul Rebman2, Novalene Goklish1
1Center for Indigenous Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
JAMA network open
|October 14, 2024
概括
现有的机器学习模型显示,在美国印第安人和阿拉斯加土著人口中识别自杀风险是有前途的,其表现优于目前的查方法. 为了在这些服务不足的社区获得最佳的准确性,需要进一步的研究和重新校准.
科学领域:
- 公共卫生 公共卫生
- 医疗保健服务研究 医疗服务研究
- 医疗保健中的机器学习
背景情况:
- 美国印第安人和阿拉斯加土著人口经历了与自杀有关的重大不平等.
- 现有的自杀风险识别工具对于这些特定人群来说很少.
- 机器学习模型为改善自杀风险预测提供了潜力.
研究的目的:
- 评估已建立的机器学习模型的准确性,以识别自杀风险.
- 评估大多数美洲印第安人患者群体中的模型性能.
- 将机器学习模型与组合增强选指标进行比较.
主要方法:
- 来自西南印度卫生服务单位的电子健康记录数据 (2017-2021) 的二次分析.
- 心理健康研究网络 (MHRN) 和范德比尔特大学 (VU) 模型的应用和比较.
- 接收器操作特征曲线下的区域 (AUROC) 用于将模型性能与90天的自杀企图/死亡结果进行比较.
主要成果:
- MHRN模型实现了0.81的AUROC,优于VU模型 (AUROC 0.68) 和增强查 (AUROC 0.66).
- 最初的模型校准很差,但在重新校准后得到了改进.
- 研究人口主要是美国印第安人 (84.7%),自杀未遂率为1.9%,90天内自杀死亡率为0.2%.
结论:
- 现有的自杀风险识别机器学习模型在应用于美洲印第安人和阿拉斯加土著人口时显示出潜力.
- 这些模型的表现优于选结果,过去的尝试和最近的创意的综合指标.
- 再校准对于提高模型准确性和临床实用性在这个人口群中至关重要.
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