A3 Refereed book chapter or chapter in a compilation book
Machine Learning in Practice—Evaluation of Clinical Value, Guidelines
Authors: Juarez-Orozco, Luis Eduardo; Ruijsink, Bram; Yeung, Ming Wai; Benjamins, Jan Walter; van der Harst, Pim
Editors: Asselbergs, Folkert W.; Denaxas, Spiros; Oberski, Daniel L.; Moore, Jason H.
Publisher: Springer International Publishing
Publication year: 2023
Book title : Clinical Applications of Artificial Intelligence in Real-World Data
Journal name in source: Clinical Applications of Artificial Intelligence in Real-World Data
First page : 247
Last page: 261
ISBN: 978-3-031-36677-2
eISBN: 978-3-031-36678-9
DOI: https://doi.org/10.1007/978-3-031-36678-9_16
Web address : https://link.springer.com/chapter/10.1007/978-3-031-36678-9_16
Machine learning research in health care literature has grown at an unprecedented pace. This development has generated a clear disparity between the number of first publications involving machine learning implementations and that of orienting guidelines and recommendation statements to promote quality and report standardization. In turn, this hinders the much-needed evaluation of the clinical value of machine learning studies and applications. This appraisal should constitute a continuous process that allows performance evaluation, facilitates repeatability, leads optimization and boost clinical value while minimizing research waste. The present chapter outlines the need for machine learning frameworks in healthcare research to guide efforts in reporting and evaluating clinical value these novel implementations, and it discusses the emerging recommendations and guidelines in the area.
