Toward Digital Phenotypes of Early Childhood Mental Health via Unsupervised and Supervised Machine Learning
Toward Digital Phenotypes of Early Childhood Mental Health via Unsupervised and Supervised Machine Learning
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通过无监督和监督机器学习实现幼儿心理健康的数字表型
DOI：
10.1109/embc40787.2023.10340806
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
McGinnis, Ryan S.
中科院分区：
文献类型：
--
作者：
Loftness, Bryn C.;Rizzo, Donna M.;Halvorson-Phelan, Julia;O’Leary, Aisling;Prytherch, Shania;Bradshaw, Carter;Brown, Anna Jane;Cheney, Nick;McGinnis, Ellen W.;McGinnis, Ryan S.
儿童心理健康障碍，如焦虑，抑郁和ADHD是常见的，并且往往在青春期或成年期未被发现。这可能会对长期福祉和生活质量产生不利影响。目前对学龄前儿童的父母报告评估往往是有偏见的，因此需要客观的心理健康筛查工具。利用数字化工具来识别儿童精神障碍的行为特征，可以在最有可能产生长期影响的时候增加干预。我们提供了来自84名参与者（4-8岁，50%被诊断为焦虑，抑郁和/或ADHD）的数据，这些数据是在使用ChAMP系统进行的一系列情绪诱导任务中收集的。无监督Kohonen自组织地图（SOM）从运动和音频功能构建表明，年龄并不倾向于解释集群一致的特定任务和跨任务SOM内的性别。症状流行率和诊断状态也显示出一些聚集性证据。案例研究表明，高损伤（>第80百分位数症状计数）和诊断亚型（ADHD-组合）可能是大多数行为不同的儿童的原因。基于相同的数据集，我们还提出了诊断二进制分类的监督建模结果。我们的顶级模型产生了中等但有希望的结果（ROC AUC .6-.82，TPR .36-.71，准确度.62-.86），与我们之前针对孤立行为任务的努力相当。增强功能，调整模型参数，并纳入额外的可穿戴传感器数据，将继续推动儿童心理健康数字表型发现的快速发展。临床相关性-这项工作推进了可穿戴设备用于检测儿童心理健康障碍的使用。
Childhood mental health disorders such as anxiety, depression, and ADHD are commonly-occurring and often go undetected into adolescence or adulthood. This can lead to detrimental impacts on long-term wellbeing and quality of life. Current parent-report assessments for pre-school aged children are often biased, and thus increase the need for objective mental health screening tools. Leveraging digital tools to identify the behavioral signature of childhood mental disorders may enable increased intervention at the time with the highest chance of long-term impact. We present data from 84 participants (4-8 years old, 50% diagnosed with anxiety, depression, and/or ADHD) collected during a battery of mood induction tasks using the ChAMP System. Unsupervised Kohonen Self-Organizing Maps (SOM) constructed from movement and audio features indicate that age did not tend to explain clusters as consistently as gender within task-specific and cross-task SOMs. Symptom prevalence and diagnostic status also showed some evidence of clustering. Case studies suggest that high impairment (>80th percentile symptom counts) and diagnostic subtypes (ADHD-Combined) may account for most behaviorally distinct children. Based on this same dataset, we also present results from supervised modeling for the binary classification of diagnoses. Our top performing models yield moderate but promising results (ROC AUC .6-.82, TPR .36-.71, Accuracy .62-.86) on par with our previous efforts for isolated behavioral tasks. Enhancing features, tuning model parameters, and incorporating additional wearable sensor data will continue to enable the rapid progression towards the discovery of digital phenotypes of childhood mental health.Clinical Relevance— This work advances the use of wearables for detecting childhood mental health disorders.
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DOI：
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发表时间：
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期刊：
2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC
影响因子：
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作者：
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