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A machine learning ensemble to predict treatment outcomes following an Internet intervention for depression.

ABSTRACT: BACKGROUND:Some Internet interventions are regarded as effective treatments for adult depression, but less is known about who responds to this form of treatment. METHOD:An elastic net and random forest were trained to predict depression symptoms and related disability after an 8-week course of an Internet intervention, Deprexis, involving adults (N = 283) from across the USA. Candidate predictors included psychopathology, demographics, treatment expectancies, treatment usage, and environmental context obtained from population databases. Model performance was evaluated using predictive R2$\lpar R_{{\rm pred}}^2\rpar\comma $ the expected variance explained in a new sample, estimated by 10 repetitions of 10-fold cross-validation. RESULTS:An ensemble model was created by averaging the predictions of the elastic net and random forest. Model performance was compared with a benchmark linear autoregressive model that predicted each outcome using only its baseline. The ensemble predicted more variance in post-treatment depression (8.0% gain, 95% CI 0.8-15; total $R_{{\rm pred}}^2 \; $= 0.25), disability (5.0% gain, 95% CI -0.3 to 10; total $R_{{\rm pred}}^2 \; $= 0.25), and well-being (11.6% gain, 95% CI 4.9-19; total $R_{{\rm pred}}^2 \; $= 0.29) than the benchmark model. Important predictors included comorbid psychopathology, particularly total psychopathology and dysthymia, low symptom-related disability, treatment credibility, lower access to therapists, and time spent using certain Deprexis modules. CONCLUSION:A number of variables predict symptom improvement following an Internet intervention, but each of these variables makes relatively small contributions. Machine learning ensembles may be a promising statistical approach for identifying the cumulative contribution of many weak predictors to psychosocial depression treatment response.

SUBMITTER: Pearson R

PROVIDER: S-EPMC6763538 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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A machine learning ensemble to predict treatment outcomes following an Internet intervention for depression.

Pearson Rahel R Pisner Derek D Meyer Björn B Shumake Jason J Beevers Christopher G CG

Psychological medicine 20181105 14

<h4>Background</h4>Some Internet interventions are regarded as effective treatments for adult depression, but less is known about who responds to this form of treatment.<h4>Method</h4>An elastic net and random forest were trained to predict depression symptoms and related disability after an 8-week course of an Internet intervention, Deprexis, involving adults (N = 283) from across the USA. Candidate predictors included psychopathology, demographics, treatment expectancies, treatment usage, and ...[more]

PMID: 30392475

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Project description:BackgroundProblematic and pathological gambling have been linked to depression. Despite a high demand for treatment and negative financial consequences, only a small fraction of problematic and pathological gamblers seek professional help. The existing treatment gap could be narrowed by providing low-threshold, anonymous internet-based interventions. The aim of the present study was to examine the acceptance and efficacy of an online-intervention for depression ("Deprexis") in a sample of problematic and pathological slot-machine gamblers. We hypothesized that the intervention group would show a greater reduction in both depressive and gambling-related symptoms compared to a wait-list control group.MethodA total of 140 individuals with self-reported gambling and mood problems were randomly allocated either to the intervention group or to a wait-list control group. After 8 weeks, all participants were invited for re-assessment. The Patient Health Questionnaire - 9 (PHQ-9) served as the primary outcome assessment. Problematic gambling was measured with the Pathological Gambling Adaptation of Yale-Brown Obsessive Compulsive Scale (PG-YBOCS) and the South Oaks Gambling Screen (SOGS). The trial is registered with the German Registry for Clinical Studies (DRKS00013888).ResultsITT analyses showed that the intervention led to a significant reduction in depressive symptoms as well as gambling-related symptoms compared to the control group, with moderate to strong effect sizes. PP analyses failed to yield significant results due to high rates of non-completion and limited statistical power. Moderator analyses indicated that Deprexis was particularly beneficial in reducing problematic gambling for those scoring high on baseline gambling-related symptoms and for those who gamble due to loneliness.DiscussionResults of the present study suggest that Deprexis might be a useful adjunct to traditional interventions for the treatment of problematic gambling. The potential of internet-based interventions that are more targeted at issues specific to gambling should be evaluated in future studies.Trial registrationGerman Registry for Clinical Studies DRKS00013888.

Project description:Background: Recent evidence suggests that integration of multi-modal data improves performance in machine learning prediction of depression treatment outcomes. Here, we compared the predictive performance of three machine learning classifiers using differing combinations of sociodemographic characteristics, baseline clinical self-reports, cognitive tests, and structural magnetic resonance imaging (MRI) features to predict treatment outcomes in late-life depression (LLD). Methods: Data were combined from two clinical trials conducted with depressed adults aged 60 and older, including response to escitalopram (N = 32, NCT01902004) and Tai Chi (N = 35, NCT02460666). Remission was defined as a score of 6 or less on the 24-item Hamilton Rating Scale for Depression (HAMD) at the end of 24 weeks of treatment. Features subsets were constructed from baseline sociodemographic and clinical features, gray matter volumes (GMVs), or both. Three classification algorithms were compared: (1) Support Vector Machine-Radial Bias Function (SVMRBF), (2) Random Forest (RF), and (3) Logistic Regression (LR). A repeated 5-fold cross-validation approach with a wrapper-based feature selection method was used for model fitting. Model performance metrics included Area under the ROC Curve (AUC) and Matthews correlation coefficient (MCC). Cross-validated performance significance was tested by permutation analysis. Classifiers were compared by Cochran's Q and post-hoc pairwise comparisons using McNemar's Chi-Square test with Bonferroni correction. Results: For the RF and SVMRBF algorithms, the combined feature set outperformed the clinical and GMV feature sets with a final cross-validated AUC of 0.83 ± 0.11 and 0.80 ± 0.11, respectively. Both classifiers passed permutation analysis. The LR algorithm performed best using GMV features alone (AUC 0.79 ± 0.14) but failed to pass permutation analysis using any feature set. Performance of the three classifiers differed significantly for all three features sets. Important predictive features of treatment response included anterior and posterior cingulate volumes, depression characteristics, and self-reported health-related quality scores. Conclusion: This preliminary exploration into the use of ML and multi-modal data to identify predictors of general treatment response in LLD indicates that integration of clinical and structural MRI features significantly increases predictive capability. Identified features are among those previously implicated in geriatric depression, encouraging future work in this arena.

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A machine learning ensemble to predict treatment outcomes following an Internet intervention for depression.

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A machine learning ensemble to predict treatment outcomes following an Internet intervention for depression.

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