通过真实世界数据和健康的社会决定因素识别青少年抑郁和焦虑:机器学习模型的开发和验证。

IF 5.8 2区 医学 Q1 PSYCHIATRY
Jmir Mental Health Pub Date : 2025-02-12 DOI:10.2196/66665
Mamoun T Mardini, Georges E Khalil, Chen Bai, Aparna Menon DivaKaran, Jessica M Ray
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引用次数: 0

摘要

背景:青少年心理健康状况如抑郁和焦虑的患病率显著增加。尽管机器学习(ML)具有潜力,但缺乏使用真实世界数据(RWD)来增强对这些疾病的早期检测和干预的模型。目的:本研究旨在利用RWD和健康社会决定因素(SDoH)的ML技术识别青少年的抑郁和焦虑。方法:我们分析了10-17岁青少年的RWD,考虑了各种因素,如人口统计学、既往诊断、处方药物、医疗程序和焦虑或抑郁发作前的实验室测量记录。临床数据在块水平上与SDoH相关联。开发了三个独立的模型来预测焦虑、抑郁和两种情况。我们选择的机器学习模型是极端梯度增强(XGBoost),我们使用嵌套交叉验证技术评估其性能。为了解释模型预测,我们使用了Shapley加性解释方法。结果:我们的队列包括52,054名青少年,其中12,572名患有焦虑症,7812名患有抑郁症,14,019名患有任何一种情况。模型获得的曲线下面积值为焦虑为0.80,抑郁为0.81,两者结合为0.78。排除SDoH数据对模型性能的影响最小。Shapley加性解释分析发现性别、种族、受教育程度和各种医疗因素是焦虑和抑郁的关键预测因素。结论:本研究强调了ML在使用RWD的青少年中早期识别抑郁和焦虑的潜力。通过利用RWD,卫生保健提供者可以更准确地识别有风险的青少年并更早地进行干预,从而有可能改善心理健康结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Identifying Adolescent Depression and Anxiety Through Real-World Data and Social Determinants of Health: Machine Learning Model Development and Validation.

Identifying Adolescent Depression and Anxiety Through Real-World Data and Social Determinants of Health: Machine Learning Model Development and Validation.

Identifying Adolescent Depression and Anxiety Through Real-World Data and Social Determinants of Health: Machine Learning Model Development and Validation.

Identifying Adolescent Depression and Anxiety Through Real-World Data and Social Determinants of Health: Machine Learning Model Development and Validation.

Background: The prevalence of adolescent mental health conditions such as depression and anxiety has significantly increased. Despite the potential of machine learning (ML), there is a shortage of models that use real-world data (RWD) to enhance early detection and intervention for these conditions.

Objective: This study aimed to identify depression and anxiety in adolescents using ML techniques on RWD and social determinants of health (SDoH).

Methods: We analyzed RWD of adolescents aged 10-17 years, considering various factors such as demographics, prior diagnoses, prescribed medications, medical procedures, and laboratory measurements recorded before the onset of anxiety or depression. Clinical data were linked with SDoH at the block-level. Three separate models were developed to predict anxiety, depression, and both conditions. Our ML model of choice was Extreme Gradient Boosting (XGBoost) and we evaluated its performance using the nested cross-validation technique. To interpret the model predictions, we used the Shapley additive explanation method.

Results: Our cohort included 52,054 adolescents, identifying 12,572 with anxiety, 7812 with depression, and 14,019 with either condition. The models achieved area under the curve values of 0.80 for anxiety, 0.81 for depression, and 0.78 for both combined. Excluding SDoH data had a minimal impact on model performance. Shapley additive explanation analysis identified gender, race, educational attainment, and various medical factors as key predictors of anxiety and depression.

Conclusions: This study highlights the potential of ML in early identification of depression and anxiety in adolescents using RWD. By leveraging RWD, health care providers may more precisely identify at-risk adolescents and intervene earlier, potentially leading to improved mental health outcomes.

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来源期刊
Jmir Mental Health
Jmir Mental Health Medicine-Psychiatry and Mental Health
CiteScore
10.80
自引率
3.80%
发文量
104
审稿时长
16 weeks
期刊介绍: JMIR Mental Health (JMH, ISSN 2368-7959) is a PubMed-indexed, peer-reviewed sister journal of JMIR, the leading eHealth journal (Impact Factor 2016: 5.175). JMIR Mental Health focusses on digital health and Internet interventions, technologies and electronic innovations (software and hardware) for mental health, addictions, online counselling and behaviour change. This includes formative evaluation and system descriptions, theoretical papers, review papers, viewpoint/vision papers, and rigorous evaluations.
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