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Machine learning versus traditional methods for the development of risk stratification scores: a case study using original Canadian Syncope Risk Score data

Crossref DOI link: https://doi.org/10.1007/s11739-021-02873-y

Published Online: 2021-11-03

Published Print: 2022-06

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Grant, Lars

Joo, Pil

Nemnom, Marie-Joe

Thiruganasambandamoorthy, Venkatesh
Funding

Funding for this research was provided by:

Physicians' Services Incorporated Foundation (09q4017)

Canadian Institutes of Health Research (MOP-114927)

Heart and Stroke Foundation of Canada (G-15-0009006)

Networks of Centres of Excellence of Canada (SRG-15-P10-001)
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Text and Data Mining valid from 2021-11-03

Version of Record valid from 2021-11-03
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Article History

Received: 17 May 2021

Accepted: 13 October 2021

First Online: 3 November 2021

Declarations

:

: The authors declare that they have no competing interests. At the time of the research, Dr. Thiruganasambandamoorthy held a salary award ‘National New Investigator Award’ through the Heart and Stroke Foundation of Canada. Currently, he holds the Physicians’ Services Incorporated (PSI) Foundation’s ‘PSI-50 Mid-Career Clinical Research Award’. The funders take no responsibility for the design, conduct, results or interpretations presented here.

: Ethical approval was obtained for the two prospective cohort studies from Ottawa Health Science Network Research Ethics Board; derivation protocol #2009661-01H and the validation protocol #20160137-01H.

: The two prospective cohort studies included human participants but not animals.

: The ethics committees approved the study with the requirement of only verbal consent.

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