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使用机器学习和神经心理测量数据检测儿童和青少年精神病中的正式思维障碍

Przemysław T Zakowicz¹, Maksymilian A Brzezicki², Charalampos Levidiotis³

¹Collegium Medicum, University of Zielona Gora, Zielona Góra, Poland.

Frontiers in psychiatry

|April 1, 2025

概括

神经心理测试和机器学习可以在早期发病的精神病中识别形式思维障碍 (FTD). 这种方法显示了早期诊断和干预的前景,改善了患者的治疗结果.

科学领域:

神经科学是一个神经科学.
精神病学是一个精神病学.
计算医学是一种计算医学.

背景情况:

正式思维障碍 (FTD) 是早期发病精神病的一个关键症状,影响患者的预后.
目前FTD诊断依赖于主观和时间密集的临床评估.
需要客观的诊断工具来改善早期检测和治疗.

研究的目的:

探索神经心理测试和机器学习的使用,以区分FTD在早期发病的精神病.
用认知任务性能和药物数据评估机器学习模型的诊断准确性.
确定与FTD相关的关键神经心理和临床特征.

主要方法:

评估了27名患有早期发病精神病的人群.
神经心理测试包括爱荷华州博任务 (IGT) 和简单反应时间 (SRT).
机器学习模型 (LR,SVM,RF,XGBoost) 根据认知数据和抗精神病药物负载 (罗马等效剂) 来分类参与者.

主要成果:

后勤回归 (LR) 实现了最高的性能,ROC AUC为0.850.
关键预测因素包括IGT卡的选择,SRT反应时间的变化和药物剂量.
最好的模型准确地分类了27名参与者中的24名.

结论:

神经心理测试与机器学习相结合,提供了一种可行的方法来识别早期发病精神病的FTD.
早期FTD检测可以实现及时干预,并可能改善临床结果.
在更大,多样化的队列中进一步验证是必要的,以确认这些发现并探索神经认知基础.

关键词:

儿童和青少年精神病症.认知精神病学是一种认知精神病学.执行部门赤字执行部门赤字正式思维障碍是一种思维障碍.机器学习是机器学习.

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