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Aug 31, 2026Open Access
Antidepressant-associated cycle acceleration (AICA), encompassing antidepressant-related switching, rapid-cycling induction or acceleration, and mixed-state emergence, is an important treatment-safety concern in bipolar disorder (BD). Because these outcomes may be difficult to distinguish from spontaneous illness progression, pre-initiation risk stratification is clinically relevant. This proof-of-concept simulation study evaluates whether a multivariate machine-learning framework can recover pr...
Aug 31, 2026Open Access
Selective serotonin reuptake inhibitor (SSRI)-associated mood destabilisation, encompassing hypomania, mania, and mixed-state induction, is an important pharmacological safety concern in bipolar disorder. Current clinical decision-making relies largely on static baseline risk factors and therefore may not capture the temporal evolution of symptoms, medication exposure, adherence, sleep, and physiological measures across follow-up visits. This methodological proof-of-concept study evaluates wheth...
Aug 27, 2026Open Access
Treatment-resistant bipolar depression (TRD-BD), defined in this simulation as failure to achieve remission after at least two adequate pharmacological trials for the current bipolar depressive episode, including at least one antidepressant trial delivered with mood-stabilising treatment, is managed through a sequence of uncertain decisions: continue, switch antidepressant class, or augment with a mood stabiliser or atypical antipsychotic. Current practice relies on consensus bipolar-disorder gu...
Aug 27, 2026Open Access
Depressive episodes in bipolar disorder are preceded by a prodromal phase in which subtle physiological and behavioural changes reduced daytime activity, fragmented and lengthened sleep, blunted circadian rhythm, diminished heart rate variability, and social withdrawal emerge before the patient or clinician recognises a clinical episode. Digital phenotyping, the continuous passive collection of behavioural and physiological data from wearable sensors and smartphones, offers an unprecedented wind...
Aug 27, 2026Open Access
Mood episode recurrence in bipolar disorder is shaped by the structure of a patient’s episode history: the temporal succession of episodes, pharmacological transitions between episodes, and social-rhythm disruptions that may destabilise the circadian and social-zeitgeber system. Existing recurrence models generally flatten this history or merge all connections into one homogeneous graph, thereby discarding relation-type information. We therefore conducted a proof-of-concept simulation stud...
Aug 26, 2026Open Access
Antidepressant non-response in bipolar depression affects an estimated 40% - 55% of treated patients and is a major contributor to treatment chronification and pharmacological escalation. Although clinical, pharmacogenomic, and circadian correlates have been proposed, their combined predictive value remains uncertain. This study is a simulation-based proof of concept using a fully synthetic cohort and simulated outcomes; it does not constitute a validated clinical prediction. Because the full mo...
Aug 26, 2026Open Access
Antidepressant discontinuation in bipolar disorder is a clinically important contributor to treatment failure. Side-effect burden, therapeutic alliance, and prescription-adherence behaviour may jointly influence unplanned cessation. This proof-of-concept study evaluates whether those domains can support discontinuation-risk modelling in a fully synthetic cohort; it does not analyse real patient records or provide clinical validation. We generated a synthetic cohort of N = 900 antidepressant-expo...
Aug 24, 2026Open Access
Treatment-emergent mania (TEM), including antidepressant-associated hypomanic or manic switching and cycle acceleration, is an important safety concern in bipolar II disorder. This study is a synthetic-data proof of concept: all patients, digital phenotyping streams, episode histories, pharmacological variables, and TEM outcomes were simulated. The findings, therefore, evaluate methodological feasibility rather than clinical effectiveness. We developed a multimodal modelling framework combining ...
Jun 29, 2026Open Access
This study examines how AI-personalized video advertising on social media influences consumer purchase behavior. Using AI and machine learning, personalized ads are designed to match individual user preferences, making them more engaging than traditional advertisements. The research used a mixed-methods approach, combining questionnaires and open-ended responses from 400 participants who viewed both personalized and non-personalized video ads. The results showed that AI-personalized advertisemen...
May 22, 2026Open Access
Artificial intelligence (AI) is increasingly being integrated into various areas of medical practice. Although it has proven effective in certain specialties, medical schools have yet to incorporate AI into their curricula. This is largely due to the lack of strong evidence supporting its educational benefits. The aim of our study was to assess the perception, use, and satisfaction of professors at the Faculty of Medicine and Pharmacy of Casablanca regarding the integration of AI-based tools int...
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