Using real-time machine learning to prevent in-hospital hypoglycemia: a prospective study
Intern Emerg Med
.
2023 Jan;18(1):325-328.
doi: 10.1007/s11739-022-03148-w.
Epub 2022 Nov 11.
Authors
Michael Fralick
1
2
,
Meggie Debnath
3
,
Chloe Pou-Prom
3
,
Patrick O'Brien
3
,
Bruce A Perkins
4
5
,
Esmeralda Carson
6
,
Fatima Khemani
6
,
Muhammad Mamdani
3
5
Affiliations
1
Division of General Internal Medicine, Sinai Health System, ON, Toronto, Canada. mike.fralick@mail.utoronto.ca.
2
Data Science and Advanced Analytics, Unity Health Toronto, Toronto, ON, Canada. mike.fralick@mail.utoronto.ca.
3
Data Science and Advanced Analytics, Unity Health Toronto, Toronto, ON, Canada.
4
Division of Endocrinology, Sinai Health System, Toronto, ON, Canada.
5
Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.
6
Division of Vascular and Cardiovascular Surgery, St Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.
PMID:
36369632
PMCID:
PMC9651871
DOI:
10.1007/s11739-022-03148-w
No abstract available
Publication types
Letter
Comment
MeSH terms
Blood Glucose
Hospitals
Humans
Hypoglycemia* / prevention & control
Machine Learning
Prospective Studies
Substances
Blood Glucose