|Title||A call for open data to develop mental health digital biomarkers.|
|Publication Type||Journal Article|
|Year of Publication||2022|
|Authors||Adler DA, Wang F, Mohr DC, Estrin D, Livesey C, Choudhury T|
|Date Published||2022 Mar 03|
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.
|Alternate Journal||BJPsych Open|
|PubMed Central ID||PMC8935940|
|Grant List||R01 MH111610 / MH / NIMH NIH HHS / United States |
RF1 AG072449 / AG / NIA NIH HHS / United States
A call for open data to develop mental health digital biomarkers.
Submitted by chz4003 on April 11, 2023 - 12:37pm
Institute of Artificial Intelligence for Digital Health