{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["12(1)"],"submitter":["Alsinglawi B"],"pubmed_abstract":["This work introduces a predictive Length of Stay (LOS) framework for lung cancer patients using machine learning (ML) models. The framework proposed to deal with imbalanced datasets for classification-based approaches using electronic healthcare records (EHR). We have utilized supervised ML methods to predict lung cancer inpatients LOS during ICU hospitalization using the MIMIC-III dataset. Random Forest (RF) Model outperformed other models and achieved predicted results during the three framework phases. With clinical significance features selection, over-sampling methods (SMOTE and ADASYN) achieved the highest AUC results (98% with CI 95%: 95.3-100%, and 100% respectively). The combination of Over-sampling and under-sampling achieved the second-highest AUC results (98%, with CI 95%: 95.3"],"journal":["Scientific reports"],"pagination":["607"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8755804"],"repository":["biostudies-literature"],"pubmed_title":["An explainable machine learning framework for lung cancer hospital length of stay prediction."],"pmcid":["PMC8755804"],"pubmed_authors":["Alsinglawi B","Alnajjar F","Mubin O","Alorjani M","Novoa M","Alshari O","Darwish O"],"additional_accession":[]},"is_claimable":false,"name":"An explainable machine learning framework for lung cancer hospital length of stay prediction.","description":"This work introduces a predictive Length of Stay (LOS) framework for lung cancer patients using machine learning (ML) models. The framework proposed to deal with imbalanced datasets for classification-based approaches using electronic healthcare records (EHR). We have utilized supervised ML methods to predict lung cancer inpatients LOS during ICU hospitalization using the MIMIC-III dataset. Random Forest (RF) Model outperformed other models and achieved predicted results during the three framework phases. With clinical significance features selection, over-sampling methods (SMOTE and ADASYN) achieved the highest AUC results (98% with CI 95%: 95.3-100%, and 100% respectively). The combination of Over-sampling and under-sampling achieved the second-highest AUC results (98%, with CI 95%: 95.3","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Jan","modification":"2026-07-16T16:15:50.958Z","creation":"2022-02-11T15:16:51.037Z"},"accession":"S-EPMC8755804","cross_references":{"pubmed":["35022512"],"doi":["10.1038/s41598-021-04608-7"]}}