苹果淫院

Updated: Sun, 10/06/2024 - 10:30

From Saturday, Oct. 5 through Monday, Oct. 7, the Downtown and Macdonald Campuses will be open only to 苹果淫院 students, employees and essential visitors. Many classes will be held online. Remote work required where possible. See Campus Public Safety website for details.

Du samedi 5 octobre au lundi 7 octobre, le campus du centre-ville et le campus Macdonald ne seront accessibles qu鈥檃ux 茅tudiants et aux membres du personnel de l鈥橴niversit茅 苹果淫院, ainsi qu鈥檃ux visiteurs essentiels. De nombreux cours auront lieu en ligne. Le personnel devra travailler 脿 distance, si possible. Voir le site Web de la Direction de la protection et de la pr茅vention pour plus de d茅tails.

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Lars Grant

Academic title(s): 

Assistant Professor

Lars Grant
Location: 
Jewish General Hospital
Degree(s): 

MD, PhD

Areas of interest: 

- The development and use of artificial intelligence in Emergency Medicine.

- Automated data collection and use in the Emergency Department.

- Emergency Department Triage.

- Emergency Department operations in the context of the COVID-19 pandemic and COVID-19 patient registries.

- The appropriate use of antibiotics for urinary infections in geriatric Emergency Department patients.

Biography: 

Dr. Grant completed a PhD in theoretical physics at Harvard University and completed medical school and residency at 苹果淫院.

Current research: 

My primary research focus is currently the development of an Emergency Department Artificial Intelligence Flow Assistant. The central question here is: Can machine learning and artificial intelligence methods genuinely improve care? A related question that I am also studying is: What is the value of machine learning methods in the development of generalizable clinical decision rules and risk stratification tools?

In the context of the current COVID-19 pandemic, I am also carrying out research designed to assess the value of modifications to usual Emergency Department operations in response to the pandemic.

Funded by CIHR, NSERC and SSHRC

Selected publications: 

Grant L, Joo P, Nemnom MJ, Thiruganasambandamoorthy V. Machine learning versus traditional methods for the development of risk stratification scores: a case study using original Canadian Syncope Risk Score data. Intern Emerg Med. 2021 Nov 3. doi: 10.1007/s11739-021-02873-y. Epub ahead of print. PMID: 34734350.

Grant, L., Xue, X., Vajihi, Z., Azuelos, A., Rosenthal, S., Hopkins, D., . . . Afilalo, M. (2020). LO32: Artificial intelligence to predict disposition to improve flow in the emergency department.聽CJEM,聽22(S1), S18-S19. doi:10.1017/cem.2020.88

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