根据使用机器学习的纵向症状轨迹,根据第一个情节精神病的亚型,使用机器学习
Yanan Liu1, Sara Jalali2, Ridha Joober2,3
1Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
Npj mental health research
|May 15, 2025
概括
在第一次精神病发作 (FEP) 后识别不同的患者群体可以个性化治疗. 这项研究发现了三个症状轨迹,使得使用基线数据可以预测患者组成员的情况.
科学领域:
- 精神病学和临床心理学
- 计算神经科学是一种神经科学.
- 医学数据科学 医学数据科学
背景情况:
- 精神病第一次发作 (FEP) 后的临床过程非常可变.
- 个性化治疗策略需要了解和预测FEP患者的纵向症状轨迹.
研究的目的:
- 根据FEP后的纵向阳性和阴性症状,确定不同的患者子组.
- 使用基线临床数据预测FEP患者的集群成员资格.
主要方法:
- 应用K-平均集群对411名FEP患者的纵向症状数据.
- 脊柱逻辑回归被用来根据基线特征预测集群成员.
主要成果:
- 三个不同的群体出现了:低症状 (LS),低积极/持久负面 (LPPN) 和持久积极和负面症状 (PPNS).
- 预测集群成员的AUC为0.74.
- 对LS的预测因素包括较低的冷漠和情感平坦;对PPNS的预测因素包括幻觉严重程度和积极思维障碍.
结论:
- 在FEP之后存在不同的症状轨迹,其特点是不同的症状概况和抗精神病剂量要求.
- 基线临床特征可以预测这些轨迹,为FEP的个性化干预铺平道路.
- 了解这些异质途径对于定制治疗以满足个体患者需求至关重要.
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