Department of Health Sciences, University of York, UK; Hull York Medical School, University of York, UK.
Department of Health Sciences, University of York, UK.
J Affect Disord. 2019 Mar 1;246:857-860. doi: 10.1016/j.jad.2018.12.095. Epub 2018 Dec 25.
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 values were somewhat lower for the machine learning approach (45% vs. 35%).
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.
Overall, our findings support the potential application of machine learning in personalised mental healthcare.
抑郁症会导致严重的身体和心理社会发病。预测抑郁症状的持续存在可以进行有针对性的预防，减轻抑郁症的负担。机器学习是一个快速发展的领域，这种方法提供了强大的预测能力。我们调查了机器学习方法在预测老年人抑郁症状持续存在的能力。
使用 284 名患者的基线人口统计学和心理计量学数据，使用机器学习方法（“极端梯度增强”）预测老年人在 12 个月后出现持续性抑郁症状的可能性。预测性能与传统统计方法（逻辑回归）进行了比较。数据来自 CASPER（协作护理和积极监测有阈下抑郁的老年人）试验的“常规治疗”臂。
与逻辑回归相比，机器学习的预测性能更好（平均 AUC 为 0.72 对 0.67，p < 0.0001）。使用机器学习，预测在 12 个月时 PHQ-9 评分高于阈值的患者中，平均有 89%的患者实际上确实如此，而逻辑回归为 78%。然而，机器学习方法的平均阴性预测值略低（45%对 35%）。
样本量相对较小可能限制了算法的预测能力。此外，PHQ-9 评分被用作持续性抑郁症状的指标，虽然经过了很好的验证，但临床访谈会更理想。
总的来说，我们的发现支持机器学习在个性化心理健康护理中的潜在应用。
