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Predicting remission after internet-delivered psychotherapy in patients with depression using machine learning and multi-modal data
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- Wallert, John (author)
- Karolinska Institutet, Sweden;Stockholm Healthcare Services, Sweden,Karolinska Inst, Ctr Psychiat Res, Dept Clin Neurosci, Huddinge, Sweden.;Stockholm HealthCare Serv, Huddinge, Sweden.
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- Boberg, Julia (author)
- Karolinska Institutet, Sweden;Stockholm Healthcare Services, Sweden,Karolinska Inst, Ctr Psychiat Res, Dept Clin Neurosci, Huddinge, Sweden.;Stockholm HealthCare Serv, Huddinge, Sweden.
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- Kaldo, Viktor, Professor (author)
- Linnéuniversitetet,Institutionen för psykologi (PSY),Karolinska Institutet, Sweden;Stockholm Healthcare Services, Sweden,DISA ; DISA-IDP,Karolinska Institute, Sweden; Linnaeus University, Sweden,Karolinska Inst, Ctr Psychiat Res, Dept Clin Neurosci, Huddinge, Sweden.;Stockholm HealthCare Serv, Huddinge, Sweden.;Linnaeus Univ, Fac Hlth & Life Sci, Dept Psychol, Växjö, Sweden.
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- Karolinska Institutet, Sweden;Stockholm Healthcare Services, Sweden Karolinska Inst, Ctr Psychiat Res, Dept Clin Neurosci, Huddinge, Sweden;Stockholm HealthCare Serv, Huddinge, Sweden. (creator_code:org_t)
- 2022-09-01
- 2022
- English.
- In: Translational Psychiatry. - : Springer Nature. - 2158-3188. ; 12:1
- Journal article (peer-reviewed)
Abstract
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- This study applied supervised machine learning with multi-modal data to predict remission of major depressive disorder {MDD) after psychotherapy. Genotyped adult patients (n = 894, 65.5% women, age 18-75 years) diagnosed with mild-to-moderate MDD and treated with guided Internet-based Cognitive Behaviour Therapy (ICBT) at the Internet Psychiatry Clinic in Stockholm were included (2008-2016). Predictor types were demographic, clinical, process (e.g., time to complete online questionnaires), and genetic (polygenic risk scores). Outcome was remission status post ICBT (cut-off <= 10 on MADRS-S). Data were split into train (60%) and validation (40%) given ICBT start date. Predictor selection employed human expertise followed by recursive feature elimination. Model derivation was internally validated through cross-validation. The final random forest model was externally validated against a (i) null, (ii) logit, (iii) XGBoost, and {iv) blended meta-ensemble model on the hold-out validation set. Feature selection retained 45 predictors representing all four predictor types. With unseen validation data, the final random forest model proved reasonably accurate at classifying post ICBT remission (Accuracy 0.656 [0.604, 0.705], P vs null model = 0.004; AUC 0.687 [0.631, 0.743]), slightly better vs logit (bootstrap D = 1.730, P = 0.084) but not vs XGBoost (D = 0.463, P = 0.643). Transparency analysis showed model usage of all predictor types at both the group and individual patient level. A new, multi-modal classifier for predicting MDD remission status after ICBT treatment in routine psychiatric care was derived and empirically validated. The multi-modal approach to predicting remission may inform tailored treatment, and deserves further investigation to attain clinical usefulness.
Subject headings
- SAMHÄLLSVETENSKAP -- Psykologi -- Tillämpad psykologi (hsv//swe)
- SOCIAL SCIENCES -- Psychology -- Applied Psychology (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Neurologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Neurology (hsv//eng)
Keyword
- Psykologi
- Psychology
- Hälsoinformatik
- Health Informatics
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- ref (subject category)
- art (subject category)
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