Ramzi Halabi

Novel Abstract Screening Algorithm Using Delphi-Inspired Large Language Model Consensus for Systematic Reviews in Psychiatry: Nouvel algorithme de sélection des résumés utilisant un consensus issu d’un grand modèle de langage inspiré de la méthode Delphi pour les revues systématiques en psychiatrie

Mirkamal Tolend, Ramzi Halabi, Kousai Ghaouari, Yvonne C Y Lau et autres

BackgroundLarge language models (LLMs) may reduce the burden associated with performing systematic reviews by prescreening abstracts from a literature search for eligibility for inclusion in full-text review.MethodsWe developed an iterative, LLM-based workflow for screening abstracts: after manual specification of eligibility criteria and …

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Trajectories of Suicidal Risk Impact Mood Regulation Differently in Patients With a Diagnosis of Bipolar Disorder

Abigail Ortiz, Ramzi Halabi, Daniel Blumberger, Christina Gonzalez‐Torres et autres

BACKGROUND: Bipolar disorder (BD) carries a suicide risk 20 times higher than the general population, with up to 60% of patients attempting suicide. Current interventions have failed to reduce its incidence; static factors have shown limited predictive utility. Emerging evidence suggests dynamic …

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A systematic exploration of digital biomarkers for the detection of depressive episodes in bipolar disorder

Ramzi Halabi, Benoit H. Mulsant, Mirkamal Tolend, Daniel M. Blumberger et autres

Digital phenotyping promises to transform psychiatry by using multimodal, densely sampled data. However, its potential is hindered by the lack of focus on identifying and validating digital biomarkers that accurately reflect mental states before evaluating their impact on outcomes. This longitudinal study …

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89. Time Series Forecasting of (Hypo)Manic Episodes Using Electronic Monitoring – Feasibility of a Personalized Deep Learning Model Workflow

Mirkamal Tolend, Ramzi Halabi, Arend Hintze, Abigail Ortiz

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15. Selection and Characterization of Digital Biomarkers of Depressive Episodes in Bipolar Disorder Using Explainable Machine Learning

Abigail Ortiz, Ramzi Halabi, Mirkamal Tolend, Arend Hintze et autres

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Day-to-day variability in activity levels detects transitions to depressive symptoms in bipolar disorder earlier than changes in sleep and mood

Abigail Ortiz, Ramzi Halabi, Martin Alda, Alexandra DeShaw et autres

Abstract Anticipating clinical transitions in bipolar disorder (BD) is essential for the development of clinically actionable predictions. Our aim was to determine what is the earliest indicator of the onset of depressive symptoms in BD. We hypothesized that changes in activity would …

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Day-to-day variability in sleep and activity predict the onset of a hypomanic episode in patients with bipolar disorder

Abigail Ortiz, Ramzi Halabi, Martin Alda, Alexandra DeShaw et autres

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Mood regulation in euthymic patients with a history of antidepressant‐induced mania

Ramzi Halabi, Khairatun Yusuff, Clara Park, Alexandra DeShaw et autres

INTRODUCTION: The use of antidepressants in bipolar disorder (BD) remains contentious, in part due to the risk of antidepressant-induced mania (AIM). However, there is no information on the architecture of mood regulation in patients who have experienced AIM. We compared the architecture …

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Comparative Assessment of Multimodal Sensor Data Quality Collected Using Android and iOS Smartphones in Real-World Settings

Ramzi Halabi, Rahavi Selvarajan, Zixiong Lin, Calvin Herd et autres

Healthcare researchers are increasingly utilizing smartphone sensor data as a scalable and cost-effective approach to studying individualized health-related behaviors in real-world settings. However, to develop reliable and robust digital behavioral signatures that may help in the early prediction of the individualized disease …

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Comparative assessment of multimodal sensor data quality collected using Android and iOS smartphones in real-world settings

Ramzi Halabi, Rahavi Selvarajan, Zixiong Lin, Calvin Herd et autres

Abstract Healthcare researchers are increasingly utilizing smartphone sensor data as a scalable and cost-effective approach to studying individualized health-related behaviors in real-world settings. However, to develop reliable and robust digital behavioral signatures that may help predict disease trajectory early and future prognosis, …

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A Novel Unsupervised Machine Learning Approach to Assess Postural Dynamics in Euthymic Bipolar Disorder

Ramzi Halabi, Christina Gonzalez‐Torres, Stephane MacLean, Muhammad Ishrat Husain et autres

Bipolar disorder (BD) is a mood disorder with different phases alternating between euthymia, manic or hypomanic episodes, and depressive episodes. While motor abnormalities are commonly seen during depressive or manic episodes, not much attention has been paid to postural abnormalities during periods …

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Not missing at random: Missing data are associated with clinical status and trajectories in an electronic monitoring longitudinal study of bipolar disorder

Ramzi Halabi, Benoit H. Mulsant, Martin Alda, Alexandra DeShaw et autres

There is limited information on the association between participants' clinical status or trajectories and missing data in electronic monitoring studies of bipolar disorder (BD). We collected self-ratings scales and sensor data in 145 adults with BD. Using a new metric, Missing Data …

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