Explainable machine learning using perioperative serial laboratory results to predict postoperative mortality in patients with peritonitis-induced sepsis

PURPOSE: Sepsis is one of the most common causes of death after surgery. Several conventional scoring systems have been developed to predict the outcome of sepsis; however, their predictive power is insufficient. The present study applies explainable machine-learning algorithms to improve the accura...

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Detalles Bibliográficos
Autores principales: Lim, Seung Hee, Kim, Min Jeong, Choi, Won Hyuk, Cheong, Jin Cheol, Kim, Jong Wan, Lee, Kyung Joo, Park, Jun Ho
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Surgical Society 2023
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10613826/
https://www.ncbi.nlm.nih.gov/pubmed/37908377
http://dx.doi.org/10.4174/astr.2023.105.4.237

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10613826/
https://www.ncbi.nlm.nih.gov/pubmed/37908377
http://dx.doi.org/10.4174/astr.2023.105.4.237

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