开发和验证基于机器学习的机器学习模型的死亡风险在第一集精神病的发展和验证
Johannes Lieslehto1,2, Jari Tiihonen1,3,4, Markku Lähteenvuo1
1Department of Forensic Psychiatry, Niuvanniemi Hospital, University of Eastern Finland, Kuopio.
JAMA network open
|March 18, 2024
概括
机器学习准确地预测了第一发精神病 (FEP) 的死亡风险,识别了可能受益于特定药物治疗的个人,如长效注射剂和情绪稳定剂.
科学领域:
- 精神病学是一个精神病学.
- 机器学习 机器学习
- 公共卫生 公共卫生
背景情况:
- 第一发精神病 (FEP) 缺乏验证的工具来评估死亡风险,这阻碍了个性化干预.
- 早期识别高风险个体对于改善FEP的结果至关重要.
研究的目的:
- 评估机器学习 (ML) 在预测FEP死亡风险方面的可行性.
- 确定基于ML的风险预测是否可以指导药物治疗选择.
主要方法:
- 一项使用全国范围的瑞典和芬兰队列数据 (2006-2021) 的预后研究.
- 机器学习模型使用51个全国性注册表变量进行了开发和验证.
- 模型性能使用接收器操作特征曲线 (AUROC) 下的面积来评估.
主要成果:
- 对于2年死亡率预测,ML模型在瑞典验证样本中实现了0.70的AUROC,在芬兰样本中达到0.67.
- 被确定患有高死亡风险的个体表现出显著增加的长期死亡率 (HR 3.77-3.72).
- 长效抗精神病药物和情绪稳定剂与高风险个体的死亡率降低有关.
结论:
- 机器学习模型可以有效地预测第一发精神病的死亡风险.
- 这些预测可以为个性化治疗策略提供信息,从而有可能降低死亡率.
- 这项研究强调了ML提高FEP患者护理的潜力.
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