Portrait de Samira Abbasgholizadeh-Rahimi

Samira Abbasgholizadeh-Rahimi

samira.rahimi@mila.quebec
Professeure adjointe, McGill University, Département de génie électrique et informatique
Sujets de recherche
Apprentissage automatique médical
Graphes de connaissances
Traitement du langage naturel
Site web
LinkedIn
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Biographie

Samira Abbasgholizadeh-Rahimi (B. Ing., Ph. D.) est titulaire de la Chaire de recherche du Canada sur les soins de santé primaires numériques avancés et professeure adjointe au Département de médecine familiale de l'Université McGill et à Mila – Institut québécois d'intelligence artificielle.

Elle est également scientifique affiliée à l'Institut Lady Davis de recherches médicales de l'Hôpital général juif, présidente élue de la Société canadienne de recherche opérationnelle et directrice d'Intelligence artificielle en médecine familiale (AIFM).

Bénéficiant de sa formation interdisciplinaire, ses travaux portent sur le développement et la mise en œuvre de technologies de santé numérique avancées, telles que les outils d'aide à la décision basés sur l'IA, dans les soins de santé primaires. Ses recherches sont consacrées à l'amélioration de la prévention et de la gestion des maladies chroniques, dont les maladies cardiovasculaires, avec un accent particulier sur les populations vulnérables.

Les travaux qu’elle a menés en tant que chercheuse principale ont été financés par le Fonds de recherche du Québec - Santé (FRQS), le Conseil de recherches en sciences naturelles et en génie (CRSNG), Roche Canada, la Fondation Brocher (Suisse) et la Stratégie de recherche axée sur le patient (SRAP) - Instituts de recherche en santé du Canada (IRSC).

Elle a reçu de nombreux prix, notamment le prix New Investigator Primary Care Research 2022 du North American Primary Care Research Group (NAPCRG), qui récompense les contributions exceptionnelles de nouveaux chercheurs dans le domaine de la recherche sur les soins primaires.

Étudiants actuels

Sydney Keyamo
Maîtrise recherche - McGill
Shayan nejadshamsi
Stagiaire de recherche - McGill
Haowei Qiu
Maîtrise professionnelle - McGill
Gauri Sharma
Maîtrise recherche - McGill
Superviseur⋅e principal⋅e :
AJung Moon
Github
Google Scholar
Chetan Shirvankar
Doctorat - McGill
chetan.shirvankar@mila.quebec

Publications

EDAI Framework for Integrating Equity, Diversity, and Inclusion Throughout the Lifecycle of AI to Improve Health and Oral Health Care: Qualitative Study
Samira Abbasgholizadeh-Rahimi
Richa Shrivastava
Anita Brown-Johnson
Pascale Caidor
Claire Davies
Amal Idrissi Janati
Pascaline Kengne Talla
Sreenath Madathil
Bettina M Willie
Elham Emami
2024-11-15
Journal of Medical Internet Research (publié)
doi.org
Outcomes of guidelines from health technology assessment organizations in community-based primary care: a systematic mixed studies review
Ashkan Baradaran
Raymond Tolentino
Roland Grad
Isabelle Ganache
Genevieve Gore
Samira Abbasgholizadeh-Rahimi
Pierre Pluye
2024-11-14
International Journal of Technology Assessment in Health Care (publié)
doi.org
Perspectives on virtual interviews and emerging technologies integration in family medicine residency programs: a cross-sectional survey study
Raymond Tolentino
Charo Rodriguez
Fanny Hersson-Edery
Julie Lane
Samira Abbasgholizadeh-Rahimi
2024-09-09
BMC Medical Education (publié)
doi.org
Existing Digital Health Technology Index Summary Report for Older Adults Living with Neurocognitive Disorders (Mild and Major) and Their Informal Caregivers: An Environmental Scan
Ambily Jose
Maxime Sasseville
Ellen Gorus
Anik Giguère
Anne Bourbonnais
Samira Abbasgholizadeh-Rahimi
Clémence Balley
Ronald Buyl
Marie-Pierre Gagnon
Digital health has added numerous promising solutions to enhance the health and wellness of people with neurocognitive disorders (NCDs) and … (voir plus)their informal caregivers. (1) Background: It is important to obtain a comprehensive view of currently available technologies, their outcomes, and conditions of success to inform recommendations regarding digital health solutions for people with NCDs and their caregivers. This environmental scan was performed to identify the features of existing digital health solutions relevant to the targeted population. This work reviews currently available digital health solutions and their related characteristics to develop a decision support tool for older adults living with mild or major neurocognitive disorders and their informal caregivers. This knowledge will aid the development of a decision support tool to assist older adults and their informal caregivers in their search for adequate digital health solutions according to their needs and preferences based on trustable information. (2) Methods: We conducted an environmental scan to identify digital health solutions from a systematic review and targeted searches in the grey literature covering the regions of Canada and Europe. Technological tools were scanned based on a preformatted extraction grid. We assessed their relevance based on selected attributes and summarized the findings. (3) Results: We identified 100 available digital health solutions. The majority (56%) were not specific to NCDs. Only 28% provided scientific evidence of their effectiveness. Remote patient care, movement tracking, and cognitive exercises were the most common purposes of digital health solutions. Most solutions were presented as decision aid tools, pill dispensers, apps, web, or a combination of these platforms. (4) Conclusions: This environmental scan allowed for identifying current digital health solutions for older adults with mild or major neurocognitive disorders and their informal caregivers. Findings from the environmental scan highlight the need for additional approaches to strengthen digital health interventions for the well-being of older adults with mild and major NCDs and their informal and formal healthcare providers.
2024-06-22
Geriatrics (publié)
doi.org
Performance of generative pre-trained transformers (GPTs) in Certification Examination of the College of Family Physicians of Canada
Mehdi Mousavi
Shabnam Shafiee
Jason M Harley
Jackie Cheung
Samira Abbasgholizadeh-Rahimi
Introduction The application of large language models such as generative pre-trained transformers (GPTs) has been promising in medical educa… (voir plus)tion, and its performance has been tested for different medical exams. This study aims to assess the performance of GPTs in responding to a set of sample questions of short-answer management problems (SAMPs) from the certification exam of the College of Family Physicians of Canada (CFPC). Method Between August 8th and 25th, 2023, we used GPT-3.5 and GPT-4 in five rounds to answer a sample of 77 SAMPs questions from the CFPC website. Two independent certified family physician reviewers scored AI-generated responses twice: first, according to the CFPC answer key (ie, CFPC score), and second, based on their knowledge and other references (ie, Reviews’ score). An ordinal logistic generalised estimating equations (GEE) model was applied to analyse repeated measures across the five rounds. Result According to the CFPC answer key, 607 (73.6%) lines of answers by GPT-3.5 and 691 (81%) by GPT-4 were deemed accurate. Reviewer’s scoring suggested that about 84% of the lines of answers provided by GPT-3.5 and 93% of GPT-4 were correct. The GEE analysis confirmed that over five rounds, the likelihood of achieving a higher CFPC Score Percentage for GPT-4 was 2.31 times more than GPT-3.5 (OR: 2.31; 95% CI: 1.53 to 3.47; p0.001). Similarly, the Reviewers’ Score percentage for responses provided by GPT-4 over 5 rounds were 2.23 times more likely to exceed th
2024-05-01
Family Medicine and Community Health (publié)
doi.org
Implications of conscious AI in primary healthcare
Dorsai Ranjbari
Samira Abbasgholizadeh-Rahimi
2024-03-01
Family Medicine and Community Health (publié)
doi.org
Socially Assistive Robots for patients with Alzheimer's Disease: A scoping review.
Vania Karami
Mark J. Yaffe
Genevieve Gore
AJung Moon
Samira Abbasgholizadeh-Rahimi
2024-03-01
Archives of gerontology and geriatrics (Print) (publié)
doi.org
Use of Artificial Intelligence in the Identification and Management of Frailty: A Scoping Review Protocol
Sathya Karunananthan
Arya Rahgozar
Ramtin Hakimjavadi
Hui Yan
Kunal A Dalsania
Howard Bergman
Bishwajit Ghose
Jim LaPlante
Tess McCutcheon
Daniel I McIsaac
Samira Abbasgholizadeh-Rahimi
Nadia Sourial
Manpreet Thandi
Sabrina T Wong
Clare Liddy
2023-12-28
BMJ Open (publié)
doi.org
Qualitative Code Suggestion: A Human-Centric Approach to Qualitative Coding
Cesare Spinoso-Di Piano
Samira Abbasgholizadeh-Rahimi
Jackie Cheung
Qualitative coding is a content analysis method in which researchers read through a text corpus and assign descriptive labels or qualitative… (voir plus) codes to passages. It is an arduous and manual process which human-computer interaction (HCI) studies have shown could greatly benefit from NLP techniques to assist qualitative coders. Yet, previous attempts at leveraging language technologies have set up qualitative coding as a fully automatable classification problem. In this work, we take a more assistive approach by defining the task of qualitative code suggestion (QCS) in which a ranked list of previously assigned qualitative codes is suggested from an identified passage. In addition to being user-motivated, QCS integrates previously ignored properties of qualitative coding such as the sequence in which passages are annotated, the importance of rare codes and the differences in annotation styles between coders. We investigate the QCS task by releasing the first publicly available qualitative coding dataset, CVDQuoding, consisting of interviews conducted with women at risk of cardiovascular disease. In addition, we conduct a human evaluation which shows that our systems consistently make relevant code suggestions.
2023-12-01
Findings of the Association for Computational Linguistics: EMNLP 2023 (publié)
doi.org
openreview.net
Integrating Equity, Diversity, and Inclusion Throughout the Lifecycle of Artificial Intelligence for Better Health and Oral Health Care: A Workshop Summary.
Elham Emami
Samira Abbasgholizadeh-Rahimi
Milka Nyariro
Professors Elham Emami and Samira Rahimi organized and co-led an international interdisciplinary workshop in June 2023 at McGill University,… (voir plus) built upon an intersectoral approach addressing equity, diversity and inclusion within the field of AI.
2023-11-01
Journal (published)
pubmed.ncbi.nlm.nih.gov
Integrating Equity, Diversity, and Inclusion Throughout the Lifecycle of Artificial Intelligence for Better Health and Oral Health Care: A Workshop Summary.
Elham Emami
Samira Abbasgholizadeh-Rahimi
Milka Nyariro
Professors Elham Emami and Samira Rahimi organized and co-led an international interdisciplinary workshop in June 2023 at McGill University,… (voir plus) built upon an intersectoral approach addressing equity, diversity and inclusion within the field of AI.
2023-11-01
Journal (publié)
pubmed.ncbi.nlm.nih.gov
Artificial Intelligence for Detection of Dementia Using Motion Data: A Scoping Review
Lily Puterman-Salzman
Jory Katz
Howard Bergman
Roland Grad
Vladimir Khanassov
Genevieve Gore
Isabelle Vedel
Machelle Wilchesky
Narges Armanfard
Negar Ghourchian
Samira Abbasgholizadeh-Rahimi
Background: Dementia is a neurodegenerative disease resulting in the loss of cognitive and psychological functions. Artificial intelligence … (voir plus)(AI) may help in detection and screening of dementia; however, little is known in this area. Objectives: The objective of this study was to identify and evaluate AI interventions for detection of dementia using motion data. Method: The review followed the framework proposed by O’Malley’s and Joanna Briggs Institute methodological guidance for scoping reviews. We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist for reporting the results. An information specialist performed a comprehensive search from the date of inception until November 2020, in five bibliographic databases: MEDLINE, EMBASE, Web of Science Core Collection, CINAHL, and IEEE Xplore. We included studies aimed at the deployment and testing or implementation of AI interventions using motion data for the detection of dementia among a diverse population, encompassing varying age, sex, gender, economic backgrounds, and ethnicity, extending to their health care providers across multiple health care settings. Studies were excluded if they focused on Parkinson’s or Huntington’s disease. Two independent reviewers screened the abstracts, titles, and then read the full-texts. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. The reference lists of included studies were also screened. Results: After removing duplicates, 2,632 articles were obtained. After title and abstract screening and full-text screening, 839 articles were considered for categorization. The authors categorized the papers into six categories, and data extraction and synthesis was performed on 20 included papers from the motion tracking data category. The included studies assessed cognitive performance (n = 5, 25%); screened dementia and cognitive decline (n = 8, 40%); investigated visual behaviours (n = 4, 20%); and analyzed motor behaviors (n = 3, 15%). Conclusions: We presented evidence of AI systems being employed in the detection of dementia, showcasing the promising potential of motion tracking within this domain. Although some progress has been made in this field recently, there remain notable research gaps that require further exploration and investigation. Future endeavors need to compare AI interventions using motion data with traditional screening methods or other tech-enabled dementia detection mechanisms. Besides, future works should aim at understanding how gender and sex, and ethnic and cultural sensitivity can contribute to refining AI interventions, ensuring they are accessible, equitable, and beneficial across all society.
2023-09-13
Dementia and Geriatric Cognitive Disorders EXTRA (publié)
doi.org