Toward Digital Phenotypes of Early Childhood Mental Health via Unsupervised and Supervised Machine Learning

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.

I. INTRODUCTION

Mental health disorders in early childhood are prevalent, disruptive, and can negatively impact development if left unaddressed [1]–[3]. Children cannot reliably report their abstract emotions [4], and guardian reports can be biased, yielding challenges in detection of childhood mental health conditions [5], [6]. There is a persisting need for objective measures to supplement current childhood mental health assessments, increasing reliability and sensitivity. Our prior work lays the groundwork for these efforts by introducing a open-access digital phenotyping application, ChAMP, and a corresponding battery of behavioral tasks for assessing childhood mental health [7]–[15] (see Fig. 1). While our preliminary work provides compelling evidence data from individual tasks can be used to detect children with internalizing disorders (e.g., anxiety), results must be replicated in a larger cohort and extended to consider data from the entire battery. Moreover, in line with the Research Domain Criteria (RDoC), there is need to explore battery data for latent groupings that may be indicative of internalizing or externalizing impairment, rather than specific diagnoses.