Machine Learning Surveillance
Machine Learning Surveillance is a scholarly contribution in the domain of electronic health record research that employs algorithmic models to systematically monitor, analyze, and interpret health data for patterns, anomalies, or trends, distinguishing itself through the application of adaptive learning techniques to automate and enhance surveillance processes.
108
Publications
5.7K
Citations
502
Authors
291
Institutions
Publications per year
2017–2026
98
108
Bridging the gaps between cameras
Dimitrios Makris, Tim Ellis, J. H. Black · 2004 · 313 citations
Prem N. Ramkumar, Heather S. Haeberle, Deepak Ramanathan et al. · The Journal of Arthroplasty · 2019 · 210 citations
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291
Harvard University
Cambridge, United States
3 h-index
6 publications
110 citations
Inserm
Paris, France
3 h-index
13 publications
715 citations

Deakin University
Burwood, Australia
3 h-index
5 publications
343 citations
National Institute of Technology Durgapur
Durgapur, India
2 h-index
3 publications
387 citations
University of Wisconsin–Madison
Madison, United States
2 h-index
6 publications
202 citations

University of Toronto
Toronto, Canada
2 h-index
2 publications
84 citations
Université de Montréal
Montreal, Canada
2 h-index
2 publications
377 citations

University of Rochester
Rochester, United States
2 h-index
9 publications
413 citations
1–8 of 291
5 publications · 223 citations
5
h-index
4 publications · 162 citations
4
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Repository · 2 publications · 260 citations
2
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2 publications · 133 citations
2
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2 publications · 92 citations
2
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1–5