The article Predicting remission after internet-delivered psychotherapy in patients with depression using machine learning and multi-modal data is now published in Translational Psychiatry and ready to be read and spread.
The study applied supervised machine learning with multi-modal data to predict remission of major depressive disorder after internet-delivered psychotherapy. Predictor types were demographic, clinical, process, and genetic and outcome was remission status post ICBT. Transparency analysis showed model usage of all predictor types at both the group and individual patient level. Future iterations of the derived and validated model may inform tailored intervention before initiating ICBT for MDD. The multi-modal approach to predict remission in these patients holds promise and warrants further investigation to establish clinical utility.
The authors are John Wallert, Julia Boberg, Viktor Kaldo, David Mataix-Cols, Oskar Flygare, James Crowley, Matthew Halvorsen, Fehmi Ben Abdesslem, Magnus Boman, Evelyn Andersson, Nils Hentati Isacsson, Ekaterina Ivanova and Christian Rück.
Rücklab - Psychiatry research at Karolinska Institutet 