Use of Random Forest to Predict Adherence in an Online Intervention for Depression Using Baseline and Early Usage Data: Model Development and Validation on Retrospective Routine Care Log Data.

Background: Online interventions, such as the iFightDepression (iFD) tool, are increasingly recognized as effective alternatives to traditional face-to-face psychotherapy or pharmacotherapy for treating depression. However, particularly when used outside of study settings, low adherence rates and the resulting diminished benefits of the intervention can limit their effectiveness. Understanding the factors that predict adherence would allow for early, tailored interventions for individuals at risk of nonadherence, thereby enhancing user engagement and optimizing therapeutic outcomes.

[Addiction in psychiatric ambulatory service].

Addictive disorders are of high prevalence and often associated with other psychiatric and somatic diseases. Diagnostic procedures must be targeted to this topic and should also cover the exclusion of comorbidities, such as OCD, psychotic, mood disorders and ADHD. Severe cases, often complicated by multiple dependencies of substances and advanced stages of the disease can require the involvement of a variety of caregivers, who are requested to sufficiently cooperate and interact to guarantee an optimal outcome of treatment.