Machine learning versus logistic regression for the prediction of complications after pancreatoduodenectomy
Erik W. Ingwersen; Wessel T. Stam; Bono J.V. Meijs; Joran Roor; Marc G. Besselink; Bas Groot Koerkamp; Ignace H. J. T. de Hingh; Hjalmar C. van Santvoort · 2023 · Surgery
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
BACKGROUND: Machine learning is increasingly advocated to develop prediction models for postoperative complications. It is, however, unclear if machine learning is superior to logistic regression when using structured clinical data. Postoperative pancreatic fistula and delayed gastric emptying are the two most common complications with the biggest impact on patient condition and length of hospital stay after pancreatoduodenectomy. This study aimed to compare the performance of machine learning and logistic regression in predicting pancreatic fistula and delayed gastric emptying after pancreato
Abstract by Erik W. Ingwersen; Wessel T. Stam; Bono J.V. Meijs; Joran Roor; Marc G. Besselink; Bas Groot Koerkamp; Ignace H. J. T. de Hingh; Hjalmar C. van Santvoort, Surgery (2023) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1016/j.surg.2023.03.012