NIH R01 · 2024
PROJECT SUMMARY/ABSTRACT Digital interventions offer a highly scalable and relatively cost- and time-efficient approach to the delivery of accessible mental health services. However, evidence for efficacy comes from nomothetic group averages, overlooking the fact that a treatment that is effective for one patient may be less effective or even harmful for another. Further, guidance on matching individuals to their optimal intervention is lacking. These decisions are primarily based on clinical judgment or “trial and error,” which results in many patients receiving ineffective treatment or requiring multiple courses of treatment before achieving remission. Machine learning (ML) algorithms…
From the public funding record at NIH RePORTER. Describes the funded project, not the reviews below.