A call for open data to develop mental health digital biomarkers

Daniel A. Adler*, Fei Wang, David C. Mohr, Deborah Estrin, Cecilia Livesey, Tanzeem Choudhury

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

1 Scopus citations

Abstract

Digital biomarkers of mental health, created using data extracted from everyday technologies including smartphones, wearable devices, social media and computer interactions, have the opportunity to revolutionise mental health diagnosis and treatment by providing near-continuous unobtrusive and remote measures of behaviours associated with mental health symptoms. Machine learning models process data traces from these technologies to identify digital biomarkers. In this editorial, we caution clinicians against using digital biomarkers in practice until models are assessed for equitable predictions ('model equity') across demographically diverse patients at scale, behaviours over time, and data types extracted from different devices and platforms. We posit that it will be difficult for any individual clinic or large-scale study to assess and ensure model equity and alternatively call for the creation of a repository of open de-identified data for digital biomarker development.

Original languageEnglish (US)
Article numbere58
JournalBJPsych Open
Volume8
Issue number2
DOIs
StatePublished - Mar 3 2022

Keywords

  • Digital technology
  • machine learning
  • man-machine systems
  • mental health
  • remote consultation

ASJC Scopus subject areas

  • Psychiatry and Mental health

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