Predicting remission after internet-delivered psychotherapy in patients with depression using machine learning and multi-modal data.
Predicting remission after internet-delivered psychotherapy in patients with depression using machine learning and multi-modal data.
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DOI：
10.1038/s41398-022-02133-3
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发表时间：
2022-09-01
影响因子：
6.8
通讯作者：
Ruck, Christian
中科院分区：
文献类型：
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作者：
Wallert, John;Boberg, Julia;Kaldo, Viktor;Mataix-Cols, David;Flygare, Oskar;Crowley, James J.;Halvorsen, Matthew;Ben Abdesslem, Fehmi;Boman, Magnus;Andersson, Evelyn;Isacsson, Nils Hentati;Ivanova, Ekaterina;Ruck, Christian
该研究应用多模态数据的监督机器学习来预测心理治疗后重性抑郁症（MDD）的缓解。纳入了在斯德哥尔摩互联网精神病学诊所诊断为轻度至中度MDD并接受指导性基于互联网的认知行为疗法（ICBT）治疗的基因型成人患者（n = 894，65.5%为女性，年龄18-75岁）（2008-2016年）。预测因子类型为人口统计学、临床、过程（例如，完成在线问卷的时间）和遗传（多基因风险评分）。结局为ICBT后的缓解状态（MADRS-S的临界值≤10）。根据ICBT开始日期，数据分为训练（60%）和验证（40%）。预测选择采用人类的专业知识，然后递归特征消除。通过交叉验证对模型推导进行了内部验证。最终的随机森林模型在保持验证集上针对（i）null，（ii）logit，（iii）XGBoost和（iv）混合元集成模型进行外部验证。特征选择保留了代表所有四种预测因子类型的45个预测因子。通过看不见的验证数据，最终的随机森林模型在分类ICBT缓解后相当准确（准确度0.656 [0.604，0.705]，P vs零模型= 0.004; AUC 0.687 [0.631，0.743]），略优于logit（bootstrap D = 1.730，P = 0.084），但不优于XGBoost（D = 0.463，P = 0.643）。透明度分析显示，模型使用的所有预测类型在组和个体患者水平。一个新的，多模态分类器预测抑郁症缓解状态后，ICBT治疗常规精神科护理推导和经验验证。预测缓解的多模式方法可以为定制治疗提供信息，值得进一步研究以获得临床实用性。
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.
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影响因子：
10.3
作者：
Edmonds, M.;Hadjistavropoulos, H. D.;Titov, N.
通讯作者：
Titov, N.
影响因子：
39.2
作者：
Collins, Gary S.;Reitsma, Johannes B.;Moons, Karel G. M.
通讯作者：
Moons, Karel G. M.
影响因子：
5.9
作者：
Forsell, Erik;Isacsson, Nils;Kaldo, Viktor
通讯作者：
Kaldo, Viktor
影响因子：
39.2
作者：
Moons, Karel G. M.;Wolff, Robert F.;Mallett, Sue
通讯作者：
Mallett, Sue
DOI：
10.1038/s41386-020-0767-z
发表时间：
2021-01
期刊：
Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology
影响因子：
--
作者：
Koppe G;Meyer-Lindenberg A;Durstewitz D
通讯作者：
Durstewitz D