| 1. |
- Boman, Magnus, et al.
(författare)
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Learning machines in Internet-delivered psychological treatment
- 2019
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Ingår i: Progress in Artificial Intelligence. - : Springer Verlag. - 2192-6352 .- 2192-6360. ; 8:4, s. 475-485
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Tidskriftsartikel (refereegranskat)abstract
- A learning machine, in the form of a gating network that governs a finite number of different machine learning methods, is described at the conceptual level with examples of concrete prediction subtasks. A historical data set with data from over 5000 patients in Internet-based psychological treatment will be used to equip healthcare staff with decision support for questions pertaining to ongoing and future cases in clinical care for depression, social anxiety, and panic disorder. The organizational knowledge graph is used to inform the weight adjustment of the gating network and for routing subtasks to the different methods employed locally for prediction. The result is an operational model for assisting therapists in their clinical work, about to be subjected to validation in a clinical trial.
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| 2. |
- Forsell, Erik, et al.
(författare)
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Predicting Treatment Failure in Regular Care Internet-Delivered Cognitive Behavior Therapy for Depression and Anxiety Using Only Weekly Symptom Measures
- 2020
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Ingår i: Journal of Consulting and Clinical Psychology. - : American Psychological Association (APA). - 0022-006X .- 1939-2117. ; 88:4, s. 311-321
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Therapist guided Internet-Delivered Cognitive Behavior Therapy (ICBT) is effective, but as in traditional CBT, not all patients improve, and clinicians generally fail to identify them early enough. We predict treatment failure in 12-week regular care ICBT for Depression, Panic disorder and Social anxiety disorder, using only patients' weekly symptom ratings to identify when the accuracy of predictions exceed 2 benchmarks: (a) chance, and (b) empirically derived clinician preferences for actionable predictions. Method: Screening, pretreatment and weekly symptom ratings from 4310 regular care ICBT-patients from the Internet Psychiatry Clinic in Stockholm, Sweden was analyzed in a series of regression models each adding 1 more week of data. Final score was predicted in a holdout test sample, which was then categorized into Success or Failure (failure defined as the absence of both remitter and responder status). Classification analyses with Balanced Accuracy and 95% Confidence intervals was then compared to predefined benchmarks. Results: Benchmark 1 (better than chance) was reached 1 week into all treatments. Social anxiety disorder reached Benchmark 2 (>65%) at week 5, whereas Depression and Panic Disorder reached it at week 6. Conclusions: For depression, social anxiety and panic disorder, prediction with only patient-rated symptom scores can detect treatment failure 6 weeks into ICBT, with enough accuracy for a clinician to take action. Early identification of failing treatment attempts may be a viable way to increase the overall success rate of existing psychological treatments by providing extra clinical resources to at-risk patients, within a so-called Adaptive Treatment Strategy.
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