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Automatic assessment of mental health using phone metadata
Author(s) -
Singh Vivek K.,
Long Teng
Publication year - 2018
Publication title -
proceedings of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.193
H-Index - 14
ISSN - 2373-9231
DOI - 10.1002/pra2.2018.14505501049
Subject(s) - mental health , metadata , mobile phone , health informatics , computer science , informatics , mhealth , analytics , phone , psychological intervention , data science , health care , applied psychology , psychology , world wide web , nursing , medicine , psychiatry , public health , engineering , telecommunications , linguistics , philosophy , economic growth , electrical engineering , economics
An accurate and timely assessment of mental health of individuals is an important building block toward healthy well‐functioning societies. Traditional methods for assessing mental health and wellbeing are surveys administered by health‐care professionals. Such surveys are labor intensive, costly, and typically intermittent. With the growth in mobile sensing, health informatics, and data science, this work argues a case for using mobile phone meta‐data and data analytics to automatically infer an individual's mental health. Specifically, based on a clinically utilized health measure as ground truth (MHI‐5) and ten‐week phone usage metadata corpus for 45 participants we report that automated machine learning algorithms utilizing phone based features can achieve reasonable accuracy (around 80%) at automatically classifying the level of mental health of an individual. Results could pave way for cheaper, automated, health assessments with timely escalations to mental health professionals and interventions when required.