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使用机器学习模型识别自闭症谱系障碍照顾者的育儿压力的主要预测因素

Hangnyoung Choi^1,2, Jae Han Kim³, Hwiyoung Kim^4,5

¹Department of Child and Adolescent Psychiatry, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.

Frontiers in neuroscience

|September 14, 2023

概括

机器学习模型在自闭症谱系障碍 (ASD) 患者的照顾者中确定了育儿压力的关键预测因素. 儿童的侵略性行为和照顾者抑郁症是重要的因素,使家庭能够进行早期干预.

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.
机器学习在医疗保健中的应用

背景情况:

之前对自闭症谱系障碍 (ASD) 护理人员的育儿压力预测因素的研究,由于传统的统计方法,产生了不一致的结果.
可解释的机器学习模型提供了一种新的方法来识别这一群体中对育儿压力的重大预测因素.

研究的目的:

开发和利用可解释的机器学习模型,以确定ASD个体的照顾者中育儿压力的主要预测因素.
提高识别高风险照顾者及时干预的准确性和一致性.

主要方法:

在2016年3月至2020年10月期间,从496名参与者 (护理人员和ASD患者) 收集了数据.
功能包括照顾者精神病理学 (MMPI-2),ASD核心症状 (SRS-2) 和儿童行为问题 (CBCL).
机器学习分类器 (XGBoost,RF,物流回归,SVM) 用于预测育儿压力,使用AUC和其他指标评估绩效. 沙普利添加式扩展 (SHAP) 用于预测器识别.

主要成果:

表现最好的模型在总育儿压力 (RF模型) 中获得了0.862的AUC.
总的育儿压力的关键预测因素包括ASD患者的侵略行为和焦虑/抑郁症状,以及照顾者抑郁,社会内向和精神病.
特定的预测因素也被确定为子尺度:父母的痛苦,父母-孩子的功能障碍互动,和困难的孩子.

结论:

可解释的机器学习模型有效地确定了自闭症患者的照顾者对育儿压力的主要预测因素.
这些发现可以帮助临床医生识别患有严重育儿压力高风险的护理人员.
基于已识别的预测因子的及时干预可以改善ASD患者的治疗结果.

关键词:

人工智能的人工智能是人工智能.自闭症谱系障碍自闭症谱系障碍机器学习是机器学习.育儿压力育儿压力是父母的压力.预测器预测器预测器

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