Understanding and interpreting artificial intelligence, machine learning and deep learning in Emergency Medicine
Emerg Med J
.
2022 May;39(5):380-385.
doi: 10.1136/emermed-2021-212068.
Epub 2022 Mar 3.
Authors
Shammi Ramlakhan
1
,
Reza Saatchi
2
,
Lisa Sabir
3
,
Yardesh Singh
4
,
Ruby Hughes
5
,
Olamilekan Shobayo
2
,
Dale Ventour
4
Affiliations
1
Emergency Department, Sheffield Children's Hospital, Sheffield, UK sramlakhan@nhs.net.
2
Electronics and Computer Engineering Research Institute, Sheffield Hallam University, Sheffield, UK.
3
Emergency Department, Sheffield Children's Hospital, Sheffield, UK.
4
Department of Clinical Surgical Sciences, Faculty of Medical Sciences, The University of the West Indies, St Augustine, Trinidad and Tobago.
5
Simulation and Modelling Unit, Advanced Forming Research Centre, University of Strathclyde, Sheffield, UK.
PMID:
35241440
DOI:
10.1136/emermed-2021-212068
No abstract available
Keywords:
methods; statistics.
MeSH terms
Algorithms
Artificial Intelligence
Deep Learning*
Emergency Medicine*
Humans
Machine Learning
Grants and funding
MC_PC_19051/MRC_/Medical Research Council/United Kingdom