Predicting persistent depressive symptoms in older adults: A machine learning approach to personalised mental healthcare

Journal of Affective Disorders
Christopher M HattonPaul A Tiffin

Abstract

Depression causes significant physical and psychosocial morbidity. Predicting persistence of depressive symptoms could permit targeted prevention, and lessen the burden of depression. Machine learning is a rapidly expanding field, and such approaches offer powerful predictive abilities. We investigated the utility of a machine learning approach to predict the persistence of depressive symptoms in older adults. Baseline demographic and psychometric data from 284 patients were used to predict the likelihood of older adults having persistent depressive symptoms after 12 months, using a machine learning approach ('extreme gradient boosting'). Predictive performance was compared to a conventional statistical approach (logistic regression). Data were drawn from the 'treatment-as-usual' arm of the CASPER (CollAborative care and active surveillance for Screen-Positive EldeRs with subthreshold depression) trial. Predictive performance was superior using machine learning compared to logistic regression (mean AUC 0.72 vs. 0.67, p < 0.0001). Using machine learning, an average of 89% of those predicted to have PHQ-9 scores above threshold at 12 months actually did, compared to 78% using logistic regression. However, mean negative predictive...Continue Reading

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Citations

Mar 3, 2020·Psychotherapy Research : Journal of the Society for Psychotherapy Research·Ben LorimerJames Lawrence
Oct 24, 2020·Frontiers in Human Neuroscience·Ortal SlobodinItai Berger
Feb 11, 2021·Homeopathy : the Journal of the Faculty of Homeopathy·Bipin JethaniRaj Kumar Manchanda
May 5, 2021·Age and Ageing·Carla Ferreira do NascimentoAlexandre Dias Porto Chiavegatto Filho

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