Portrait de Samira Abbasgholizadeh-Rahimi

Samira Abbasgholizadeh-Rahimi

Membre académique associé
Professeure adjointe, McGill University, Département de génie électrique et informatique
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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

Chetan Shirvankar
Doctorat - McGill University
chetan.shirvankar@mila.quebec
Gauri Sharma
Maîtrise recherche - McGill University
Superviseur⋅e principal⋅e :
AJung Moon
gauri.sharma@mila.quebec
Github
Google Scholar
Vania Karami
Postdoctorat - McGill University
vania.karami@mila.quebec

Publications

Explainable Machine Learning Model to Predict COVID-19 Severity Among Older Adults in the Province of Quebec.
Samira Abbasgholizadeh-Rahimi
Charlene H Chu
Roland M. Grad
Mark Karanofsky
Mylene Arsenault
Charlene Esteban Ronquillo
Isabelle Vedel
K. McGilton
Machelle Wilchesky
Context: Patients over the age of 65 years are more likely to experience higher severity and mortality rates than other populations from COV… (voir plus)ID-19. Clinicians need assistance in supporting their decisions regarding the management of these patients. Artificial Intelligence (AI) can help with this regard. However, the lack of explainability-defined as "the ability to understand and evaluate the internal mechanism of the algorithm/computational process in human terms"-of AI is one of the major challenges to its application in health care. We know little about application of explainable AI (XAI) in health care. Objective: In this study, we aimed to evaluate the feasibility of the development of explainable machine learning models to predict COVID-19 severity among older adults. Design: Quantitative machine learning methods. Setting: Long-term care facilities within the province of Quebec. Participants: Patients 65 years and older presented to the hospitals who had a positive polymerase chain reaction test for COVID-19. Intervention: We used XAI-specific methods (e.g., EBM), machine learning methods (i.e., random forest, deep forest, and XGBoost), as well as explainable approaches such as LIME, SHAP, PIMP, and anchor with the mentioned machine learning methods. Outcome measures: Classification accuracy and area under the receiver operating characteristic curve (AUC). Results: The age distribution of the patients (n=986, 54.6% male) was 84.5□19.5 years. The best-performing models (and their performance) were as follows. Deep forest using XAI agnostic methods LIME (97.36% AUC, 91.65 ACC), Anchor (97.36% AUC, 91.65 ACC), and PIMP (96.93% AUC, 91.65 ACC). We found alignment with the identified reasoning of our models' predictions and clinical studies' findings-about the correlation of different variables such as diabetes and dementia, and the severity of COVID-19 in this population. Conclusions: The use of explainable machine learning models, to predict the severity of COVID-19 among older adults is feasible. We obtained a high-performance level as well as explainability in the prediction of COVID-19 severity in this population. Further studies are required to integrate these models into a decision support system to facilitate the management of diseases such as COVID-19 for (primary) health care providers and evaluate their usability among them.
2023-01-24
Big data (publié)
doi.org
Willingness to Engage in Shared Decision Making: Impact of an Educational Intervention for Resident Physicians (SDM-FM)
Roland M. Grad
A. Sandhu
Michael Ferrante
Vinita D'souza
Lily Puterman-Salzman
Samira Abbasgholizadeh-Rahimi
Gabrielle Stevens
G. Elwyn
2023-01-01
Education and training (publié)
doi.org
Using incorpoRATE to examine clinician willingness to engage in shared decision making: A study of Family Medicine residents.
Roland Grad
A. Sandhu
Michael Ferrante
Vinita D'souza
Lily Puterman-Salzman
Samira Abbasgholizadeh-Rahimi
Gabrielle Stevens
G. Elwyn
2022-12-01
Patient Education and Counseling (publié)
doi.org
Existing eHealth Solutions for Older Adults Living With Neurocognitive Disorders (Mild and Major) or Dementia and Their Informal Caregivers: Protocol for an Environmental Scan
Ambily Jose
Maxime Sasseville
Samantha Dequanter
Ellen Gorus
Anik Giguère
Anne Bourbonnais
Samira Abbasgholizadeh-Rahimi
Ronald Buyl
Marie-Pierre Gagnon
Background Dementia is one of the main public health priorities for current and future societies worldwide. Over the past years, eHealth sol… (voir plus)utions have added numerous promising solutions to enhance the health and wellness of people living with dementia-related cognitive problems and their primary caregivers. Previous studies have shown that an environmental scan identifies the knowledge-to-action gap meaningfully. This paper presents the protocol of an environmental scan to monitor the currently available eHealth solutions targeting dementia and other neurocognitive disorders against selected attributes. Objective This study aims to identify the characteristics of currently available eHealth solutions recommended for older adults with cognitive problems and their informal caregivers. To inform the recommendations regarding eHealth solutions for these people, it is important to obtain a comprehensive view of currently available technologies and document their outcomes and conditions of success. Methods We will perform an environmental scan of available eHealth solutions for older adults with cognitive impairment or dementia and their informal caregivers. Potential solutions will be initially identified from a previous systematic review. We will also conduct targeted searches for gray literature on Google and specialized websites covering the regions of Canada and Europe. Technological tools will be scanned based on a preformatted extraction grid. The relevance and efficiency based on the selected attributes will be assessed. Results We will prioritize relevant solutions based on the needs and preferences identified from a qualitative study among older adults with cognitive impairment or dementia and their informal caregivers. Conclusions This environmental scan will identify eHealth solutions that are currently available and scientifically appraised for older adults with cognitive impairment or dementia and their informal caregivers. This knowledge will inform the development of a decision support tool to assist older adults and their informal caregivers in their search for adequate eHealth solutions according to their needs and preferences based on trustable information. International Registered Report Identifier (IRRID) DERR1-10.2196/41015
2022-11-04
JMIR Research Protocols (publié)
doi.org
The use of artificial intelligence and virtual reality in doctor-patient risk communication: A scoping review.
Ryan Antel
Samira Abbasgholizadeh-Rahimi
Elena Guadagno
Jason M. Harley
Dan Poenaru
2022-10-01
Patient Education and Counseling (publié)
doi.org
GCNFusion: An efficient graph convolutional network based model for information diffusion
Bahare Fatemi
Soheila Mehr Molaei
Shirui Pan
Samira Abbasgholizadeh-Rahimi
2022-09-01
Expert Systems with Applications (publié)
doi.org
Application of Artificial Intelligence in Shared Decision Making: Scoping Review
Samira Abbasgholizadeh-Rahimi
Michelle Cwintal
Yuhui Huang
Pooria Ghadiri
Roland Grad
Dan Poenaru
Genevieve Gore
Hervé Tchala Vignon Zomahoun
France Légaré
Pierre Pluye
Background Artificial intelligence (AI) has shown promising results in various fields of medicine. It has the potential to facilitate shared… (voir plus) decision making (SDM). However, there is no comprehensive mapping of how AI may be used for SDM. Objective We aimed to identify and evaluate published studies that have tested or implemented AI to facilitate SDM. Methods We performed a scoping review informed by the methodological framework proposed by Levac et al, modifications to the original Arksey and O'Malley framework of a scoping review, and the Joanna Briggs Institute scoping review framework. We reported our results based on the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) reporting guideline. At the identification stage, an information specialist performed a comprehensive search of 6 electronic databases from their inception to May 2021. The inclusion criteria were: all populations; all AI interventions that were used to facilitate SDM, and if the AI intervention was not used for the decision-making point in SDM, it was excluded; any outcome related to patients, health care providers, or health care systems; studies in any health care setting, only studies published in the English language, and all study types. Overall, 2 reviewers independently performed the study selection process and extracted data. Any disagreements were resolved by a third reviewer. A descriptive analysis was performed. Results The search process yielded 1445 records. After removing duplicates, 894 documents were screened, and 6 peer-reviewed publications met our inclusion criteria. Overall, 2 of them were conducted in North America, 2 in Europe, 1 in Australia, and 1 in Asia. Most articles were published after 2017. Overall, 3 articles focused on primary care, and 3 articles focused on secondary care. All studies used machine learning methods. Moreover, 3 articles included health care providers in the validation stage of the AI intervention, and 1 article included both health care providers and patients in clinical validation, but none of the articles included health care providers or patients in the design and development of the AI intervention. All used AI to support SDM by providing clinical recommendations or predictions. Conclusions Evidence of the use of AI in SDM is in its infancy. We found AI supporting SDM in similar ways across the included articles. We observed a lack of emphasis on patients’ values and preferences, as well as poor reporting of AI interventions, resulting in a lack of clarity about different aspects. Little effort was made to address the topics of explainability of AI interventions and to include end-users in the design and development of the interventions. Further efforts are required to strengthen and standardize the use of AI in different steps of SDM and to evaluate its impact on various decisions, populations, and settings.
2022-08-01
JMIR Medical Informatics (publié)
doi.org
Exploring the roles of artificial intelligence in surgical education: A scoping review
Elif Bilgic
Andrew Gorgy
Alison Yang
Michelle Cwintal
Hamed Ranjbar
Kalin Kahla
Dheeksha Reddy
Kexin Li
Helin Ozturk
Eric Zimmermann
Andrea Quaiattini
Samira Abbasgholizadeh-Rahimi
Dan Poenaru
Jason M. Harley
2022-07-01
The American Journal of Surgery (publié)
doi.org
Integrating Equity, Diversity, and Inclusion throughout the lifecycle of Artificial Intelligence in health
Milka Nyariro
Elham Emami
Samira Abbasgholizadeh-Rahimi
Health care systems are the infrastructures that are put together to deliver health and social services to the population at large. These or… (voir plus)ganizations are increasingly applying Artificial Intelligence (AI) to improve the efficiency and effectiveness of health and social care. Unfortunately, both health care systems and AI are confronted with a lack of Equity, Diversity, and Inclusion (EDI). This short paper focuses on the importance of integrating EDI concepts throughout the life cycle of AI in health. We discuss the risks that the lack of EDI in the design, development and implementation of AI-based tools might have on the already marginalized communities and populations in the healthcare setting. Moreover, we argue that integrating EDI principles and practice throughout the lifecycle of AI in health has an important role in achieving health equity for all populations. Further research needs to be conducted to explore how studies in AI-health have integrated.
2022-06-29
13th Augmented Human International Conference (publié)
doi.org
Ageism and Artificial Intelligence: Protocol for a Scoping Review
Charlene H Chu
Kathleen Leslie
Jiamin Shi
Rune Nyrup
Andria Bianchi
Shehroz S Khan
Samira Abbasgholizadeh-Rahimi
Alexandra Lyn
Amanda Grenier
Background Artificial intelligence (AI) has emerged as a major driver of technological development in the 21st century, yet little attention… (voir plus) has been paid to algorithmic biases toward older adults. Objective This paper documents the search strategy and process for a scoping review exploring how age-related bias is encoded or amplified in AI systems as well as the corresponding legal and ethical implications. Methods The scoping review follows a 6-stage methodology framework developed by Arksey and O’Malley. The search strategy has been established in 6 databases. We will investigate the legal implications of ageism in AI by searching grey literature databases, targeted websites, and popular search engines and using an iterative search strategy. Studies meet the inclusion criteria if they are in English, peer-reviewed, available electronically in full text, and meet one of the following two additional criteria: (1) include “bias” related to AI in any application (eg, facial recognition) and (2) discuss bias related to the concept of old age or ageism. At least two reviewers will independently conduct the title, abstract, and full-text screening. Search results will be reported using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) reporting guideline. We will chart data on a structured form and conduct a thematic analysis to highlight the societal, legal, and ethical implications reported in the literature. Results The database searches resulted in 7595 records when the searches were piloted in November 2021. The scoping review will be completed by December 2022. Conclusions The findings will provide interdisciplinary insights into the extent of age-related bias in AI systems. The results will contribute foundational knowledge that can encourage multisectoral cooperation to ensure that AI is developed and deployed in a manner consistent with ethical values and human rights legislation as it relates to an older and aging population. We will publish the review findings in peer-reviewed journals and disseminate the key results with stakeholders via workshops and webinars. Trial Registration OSF Registries AMG5P; https://osf.io/amg5p International Registered Report Identifier (IRRID) DERR1-10.2196/33211
2022-06-01
JMIR Research Protocols (publié)
doi.org
Moving shared decision making forward in Iran.
Samira Abbasgholizadeh-Rahimi
Nam Nguyen
Mahasti Alizadeh
Dan Poenaru
2022-05-01
Zeitschrift fur Evidenz, Fortbildung und Qualitat im Gesundheitswesen (publié)
doi.org
Determinants of technology adoption and continued use among cognitively impaired older adults: a qualitative study
Samantha Dequanter
Maaike Fobelets
Iris Steenhout
Marie-Pierre Gagnon
Anne Bourbonnais
Samira Abbasgholizadeh-Rahimi
Ronald Buyl
Ellen Gorus
2022-04-28
BMC Geriatrics (published)
doi.org