期刊
PSYCHIATRY RESEARCH
卷 295, 期 -, 页码 -出版社
ELSEVIER IRELAND LTD
DOI: 10.1016/j.psychres.2020.113618
关键词
Digital therapeutics; Digital intervention; Personalized; Artificial intelligence; Machine learning; Depression; anxiety
类别
资金
- NIDA NIH HHS [P30 DA029926, T32 DA037202] Funding Source: Medline
- NIMH NIH HHS [R01 MH123482] Funding Source: Medline
While digital psychiatric interventions can reduce treatment barriers, not everyone benefits from them. Research shows that machine learning methods can accurately predict an individual's responsiveness to digital treatments to help personalize decision-making about the direction of treatment.
While digital psychiatric interventions reduce treatment barriers, not all persons benefit from this type of treatment. Research is needed to preemptively identify who is likely to benefit from these digital treatments in order to redirect those people to a higher level of care. The current manuscript used an ensemble of machine learning methods to predict changes in major depressive and generalized anxiety disorder symptoms from pre to 9-month follow-up in a randomized controlled trial of a transdiagnostic digital intervention based on participants' (N=632) pre-treatment data. The results suggested that baseline characteristics could accurately predict changes in depressive symptoms in both treatment groups (r=0.482, 95% CI[0.394, 0.561]; r=0.477, 95% CI [0.385, 0.560]) and anxiety symptoms in both treatment groups (r=0.569, 95% CI[0.491, 0.638]; r=0.548, 95% CI[0.464, 0.622]). These results suggest that machine learning models are capable of preemptively predicting a person's responsiveness to digital treatments, which would enable personalized decision-making about which persons should be directed towards standalone digital interventions or towards blended stepped-care.
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