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Predicting 30-day readmission following total knee arthroplasty using machine learning and clinical expertise applied to clinical administrative and research registry data in an Australian cohort

Crossref DOI link: https://doi.org/10.1186/s42836-023-00186-3

Published Online: 2023-06-01

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

Authors

Gould, Daniel J. https://orcid.org/0000-0002-0423-5822
Bailey, James A.

Spelman, Tim

Bunzli, Samantha

Dowsey, Michelle M.

Choong, Peter F. M.
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Text and Data Mining valid from 2023-06-01

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Article History

Received: 14 December 2022

Accepted: 10 April 2023

First Online: 1 June 2023

Declarations

:

: Ethical approval for this study was obtained from the St Vincent’s Hospital Melbourne (SVHM) Human Research Ethics Committee (reference number: HREC/76656/SVHM-2021-272152(v2)).

: Not applicable.

: PC: Royalties from Johnson and Johnson, Consultancy with Johnson & Johnson; Consultancy with Stryker Corporation; Emeritus Board Member Musculoskeletal Australia; Chair, Research Committee, Australian Orthopaedic Association (now completed term). MD: Research support paid to my institution for Investigator Initiated Research from: Medacta, Medibank, HCF Foundation; Chair, Australian Orthopaedic Association Research Foundation, Research Advisory Committee. No other competing interests for any authors.

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