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Sökning: WFRF:(Ruwaard Jeroen)

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1.
  • Karyotaki, Eirini, et al. (författare)
  • Do guided internet-based interventions result in clinically relevant changes for patients with depression? : An individual participant data meta-analysis
  • 2018
  • Ingår i: Clinical Psychology Review. - : Elsevier. - 0272-7358 .- 1873-7811. ; 63, s. 80-92
  • Forskningsöversikt (refereegranskat)abstract
    • Little is known about clinically relevant changes in guided Internet-based interventions for depression. Moreover, methodological and power limitations preclude the identification of patients' groups that may benefit more from these interventions. This study aimed to investigate response rates, remission rates, and their moderators in randomized controlled trials (RCTs) comparing the effect of guided Internet-based interventions for adult depression to control groups using an individual patient data meta-analysis approach. Literature searches in PubMed, Embase, PsycINFO and Cochrane Library resulted in 13,384 abstracts from database inception to January 1, 2016. Twenty-four RCTs (4889 participants) comparing a guided Internet-based intervention with a control group contributed data to the analysis. Missing data were multiply imputed. To examine treatment outcome on response and remission, mixed-effects models with participants nested within studies were used. Response and remission rates were calculated using the Reliable Change Index. The intervention group obtained significantly higher response rates (OR = 2.49, 95% CI 2.17-2.85) and remission rates compared to controls (OR = 2.41, 95% CI 2.07-2.79). The moderator analysis indicated that older participants (OR = 1.01) and native-born participants (1.66) were more likely to respond to treatment compared to younger participants and ethnic minorities respectively. Age (OR = 1.01) and ethnicity (1.73) also moderated the effects of treatment on remission.Moreover, adults with more severe depressive symptoms at baseline were more likely to remit after receiving intemet-based treatment (OR = 1.19). Guided Internet-based interventions lead to substantial positive treatment effects on treatment response and remission at post-treatment. Thus, such interventions may complement existing services for depression and potentially reduce the gap between the need and provision of evidence-based treatments.
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2.
  • van Breda, Ward, et al. (författare)
  • A feature representation learning method for temporal datasets
  • 2016
  • Ingår i: PROCEEDINGS OF 2016 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI). - : IEEE. - 9781509042401
  • Konferensbidrag (refereegranskat)abstract
    • Predictive modeling of future health states can greatly contribute to more effective health care. Healthcare professionals can for example act in a more proactive way or predictions can drive more automated ways of therapy. However, the task is very challenging. Future developments likely depend on observations in the (recent) past, but how can we capture this history in features to generate accurate predictive models? And what length of history should we consider? We propose a framework that is able to generate patient tailored features from observations of the recent history that maximize predictive performance. For a case study in the domain of depression we find that using this method new data representations can be generated that increase the predictive performance significantly.
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