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

TY - JOUR

T1 - Predicting persistent depressive symptoms in older adults

T2 - a machine learning approach to personalised mental healthcare

AU - Hatton, Chris

AU - Paton, Lewis William

AU - McMillan, Dean

AU - Cussens, James

AU - Gilbody, Simon

AU - Tiffin, Paul Alexander

N1 - © 2018 Elsevier B.V. All rights reserved. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy.

PY - 2019/3/1

Y1 - 2019/3/1

N2 - Background 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. Method 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. Results 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 values were somewhat lower for the machine learning approach (45% vs. 35%). Limitations A relatively small sample size potentially limited the predictive power of the algorithm. In addition, PHQ-9 scores were used as an indicator of persistent depressive symptoms, and whilst well validated, a clinical interview would have been preferable. Conclusions : Overall, our findings support the potential application of machine learning in personalised mental healthcare. Keywords DepressionMachine learningOld age psychiatry

AB - Background 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. Method 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. Results 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 values were somewhat lower for the machine learning approach (45% vs. 35%). Limitations A relatively small sample size potentially limited the predictive power of the algorithm. In addition, PHQ-9 scores were used as an indicator of persistent depressive symptoms, and whilst well validated, a clinical interview would have been preferable. Conclusions : Overall, our findings support the potential application of machine learning in personalised mental healthcare. Keywords DepressionMachine learningOld age psychiatry

KW - Depression

KW - Machine Learning

KW - Old age mental health

U2 - 10.1016/j.jad.2018.12.095

DO - 10.1016/j.jad.2018.12.095

M3 - Article

SN - 0165-0327

VL - 246

SP - 857

EP - 860

JO - Journal of affective disorders

JF - Journal of affective disorders

ER -