可解释的机器学习模型可以预测第一发精神分裂症患者的攻击性行为
Journal of psychiatric research
|October 4, 2025
概括
机器学习模型可以预测第一发精神分裂症患者的攻击性行为. 随机生存森林模型显示高准确度,确定冲动性和收入作为关键风险因素.
科学领域:
- 精神病学是一个精神病学.
- 机器学习 机器学习
- 行为科学 行为科学
背景情况:
- 精神分裂症是一种复杂的精神障碍.
- 攻击性行为是第一个情节精神分裂症患者的一个重大问题.
- 预测侵略性行为对于及时干预至关重要.
研究的目的:
- 预测第一发精神分裂症患者的攻击性行为的风险.
- 为此预测评估时间到事件机器学习模型的性能.
- 为了确定这一群体中攻击性行为的关键预测因素.
主要方法:
- 一个队列研究设计被采用了216名第一发精神分裂症患者.
- 对患者进行了24个月的跟踪,以评估攻击性行为.
- 开发了三种生存模型,并使用C指数,时间依赖AUC和AUPRC等指标进行评估. 对于特征的重要性,使用了沙普利添加式解释 (SHAP).
主要成果:
- 在随访期间,在16.67%的患者中观察到侵略性行为.
- 随机生存森林模型表现最好 (C指数=0.79,时间依赖的AUC=0.91,AUPRC=0.59).
- 高冲动性,更高的平均月收入,更大的家庭规模,失业率和较低的EPQ-L得分被确定为重要的预测因素.
结论:
- 随机生存森林模型在预测第一发精神分裂症的攻击性行为方面是有效的.
- 冲动性和平均月收入是影响侵略行为的最重要的因素.
- 这些发现可以有助于为处于危险的个人制定有针对性的干预措施.
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