精神病中的预后预测:探索机器学习模型的互补作用
Violet van Dee1,2, Seyed M Kia3,4, Caterina Fregosi5
1Psychiatry, University Medical Centre Utrecht Brain Centre, Utrecht, The Netherlands violetvandee@hotmail.com.
BMJ mental health
|June 26, 2025
概括
机器学习模型 (MLMs) 显示出与精神病学家对精神分裂症结果的预测准确度相似. 虽然MLM可以在复杂的病例中提供帮助,但其目前的局限性阻碍了广泛的临床整合.
科学领域:
- 精神病学是一个精神病学.
- 计算精神病学是一种计算精神病学.
- 机器学习在医疗保健中的应用
背景情况:
- 预测精神分裂症谱系障碍轨迹是具有挑战性的,因为患者的变化.
- 机器学习 (ML) 模型对结果预测有希望,但尚未进入临床实践.
- 将ML模型 (MLMs) 与精神病学家的预测相结合,是提高临床实用性的关键.
研究的目的:
- 比较精神病医生和MLMs的预后准确性,以短期缓解第一次精神病发作.
- 调查MLM是否可以增强精神病医生的预测能力.
主要方法:
- 24名精神科医生根据OPTiMiSE试验中的患者简历预测了缓解概率.
- 与精神病学家共享MLM预测,以允许修订估计.
- 精神病医生和MLM之间的比较预测准确度和评价者之间的协议.
主要成果:
- MLM的预测准确性很低,但与精神病医生相比,症状缓解 (0.50对0.52) 和功能缓解 (0.72对0.79) 是相对的.
- 评估者之间的一致性很低,在精神病医生和MLM之间是可比的.
- 多媒体营销并没有提高整体预测准确度,但显示了难以预测的病例的潜力;然而,精神病医生很难确定何时信任多媒体营销的输出.
结论:
- 多媒体营销有潜力成为精神病学决策中的补充工具,特别是在具有挑战性的案件中.
- 目前的MLM预测准确度和识别依赖线索的局限性限制了它们的有效性.
- 需要进一步发展,以提高MLM的实用性和促进临床整合,确保精神病医生保留决策自主权.
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