机器学习用于预测社会护理中的儿童心理健康问题
Ryan Crowley1, Katherine Parkin2,3,4, Emma Rocheteau5
1New York University Grossman School of Medicine, New York, US.
BJPsych open
|April 11, 2025
概括
机器学习模型在预测社会护理儿童的童年心理健康问题方面表现有前途. 连接不同的数据源可以提高预测准确度,帮助早期干预努力,以改善心理社会发展.
科学领域:
- 儿童和青少年精神病学 儿童和青少年精神病学
- 医疗保健中的机器学习
- 社会护理信息学 社会护理信息学
背景情况:
- 在英国,儿童心理健康问题的比例不断上升,需要及早识别.
- 早期干预对心理社会发展至关重要,尤其是在社会护理中的儿童.
- 临床预测工具为改善早期识别和干预提供了潜在的途径.
研究的目的:
- 描述接受社会护理服务的儿童的一个新型队列.
- 开发和评估用于预测儿童心理健康问题的机器学习模型.
- 评估开发的预测模型的性能,可解释性和公平性.
主要方法:
- 使用了来自英国威尔士的链接,非识别的健康,社会护理和教育数据.
- 创建了一个由26,820名儿童组成的队列,获得社会护理服务.
- 开发和评估了多种机器学习模型,用于预测心理健康问题.
主要成果:
- 确定了关键的风险因素:年龄,物质滥用,以及作为一个照顾孩子.
- 性能最好的梯度增强模型实现了AUC为0.75 (95%CI为0.73-0.78).
- 算法公平性评估揭示了预测模型中的潜在偏差.
结论:
- 在这个群体中,机器学习在预测儿童心理健康问题的方面表现出有希望的表现.
- 整合多样化,常规收集的数据集可以提高社会护理环境中的预测准确性.
- 将多种数据源连接起来,可以提供更广泛的临床,社会和环境风险因素,以改善预测.
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