Trusting Machine Learning Results from Medical Procedures in the OperatingRoom
Övrigt konferensbidrag, 2022
Machine learning can be used to analyse physiological data for several purposes. Detection of cerebral ischemia is an achievement that would have high impact on patient care. We attempted to study if collection of continous physiological data from non-invasive monitors, and analysis with machine learning could detect cerebral ischemia in tho different setting, during surgery for carotid endarterectomy and during endovascular thrombectomy in acute stroke. We compare the results from the two different group and one patient from each group in details. While results from CEA-patients are consistent, those from thrombectomy patients are not and frequently contain extreme values such as 1.0 in accuracy. We conlcude that this is a result of short duration of the procedure and abundance of data with bad quality resulting in small data sets. These results can therefore not be trusted.
Författare
Ali El-Merhi
Göteborgs universitet
Sahlgrenska universitetssjukhuset
Helena Odenstedt Hergès
Göteborgs universitet
Sahlgrenska universitetssjukhuset
Linda Block
Göteborgs universitet
Sahlgrenska universitetssjukhuset
Mikael Elam
Göteborgs universitet
Richard Vithal
Göteborgs universitet
Sahlgrenska universitetssjukhuset
Jaquette Liljencrantz
Göteborgs universitet
Miroslaw Staron
Göteborgs universitet
Chalmers, Data- och informationsteknik, Software Engineering
AAAI 2022 Workshop: Trustworthy AI for Healthcare
Online, ,
Online, ,
Ämneskategorier (SSIF 2025)
Neurovetenskaper
Kardiologi och kardiovaskulära sjukdomar
Neurologi