Artificial Intelligence Approaches to Predict Postoperative Length of Hospital Stay in Head and Neck Cancer Patients: A Systematic Review.

ABSTRACT: Background/Objectives: The aim of the present systematic review is to evaluate the performance of AI models for length of stay prediction. Methods: This SR was carried out in accordance with PRISMA 2020 and registered in PROSPERO database (CRD420251039985). Using the PICOS framework, we formulated the following research question: "Can artificial intelligence models accurately predict hospital length of stay (LOS) in patients undergoing head and neck (H&N) cancer surgery?" We searched the Cochrane Library, Embase, PubMed, and Scopus, with additional gray literature identified through Google Scholar and ProQuest. Risk of bias (RoB) was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST), and a narrative synthesis was performed to summarize qualitative fin

SUBMITTER: Silva WN 

PROVIDER: S-EPMC12839552 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature