Pontes, BeatrizNuñez, FranciscoRubio, CristinaMoreno, AlbertoNepomuceno, IsabelMoreno, JesusCacicedo, JonPraena-Fernandez, Juan ManuelEscobar-Rodriguez, German AntonioParra, CarlosDelgado-Leon, Blas DavidDel-Campo, Eleonor RivinCouñago, FelipeRiquelme, JoseLopez-Guerra, Jose Luis2025-01-072025-01-072021-12-30Pontes B, Núñez F, Rubio C, Moreno A, Nepomuceno I, Moreno J, et al. A data mining based clinical decision support system for survival in lung cancer. Rep Pract Oncol Radiother. 2021 Dec 30;26(6):839-848.1507-1367https://hdl.handle.net/10668/27937A clinical decision support system (CDSS ) has been designed to predict the outcome (overall survival) by extracting and analyzing information from routine clinical activity as a complement to clinical guidelines in lung cancer patients. Prospective multicenter data from 543 consecutive (2013-2017) lung cancer patients with 1167 variables were used for development of the CDSS. Data Mining analyses were based on the XGBoost and Generalized Linear Models algorithms. The predictions from guidelines and the CDSS proposed were compared. Overall, the highest (> 0.90) areas under the receiver-operating characteristics curve AUCs for predicting survival were obtained for small cell lung cancer patients. The AUCs for predicting survival using basic items included in the guidelines were mostly below 0.70 while those obtained using the CDSS were mostly above 0.70. The vast majority of comparisons between the guideline and CDSS AUCs were statistically significant (p 0.90) areas under the receiver-operating characteristics curve AUCs for predicting survival were obtained for small cell lung cancer patients. The AUCs for predicting survival using basic items included in the guidelines were mostly below 0.70 while those obtained using the CDSS were mostly above 0.70. The vast majority of comparisons between the guideline and CDSS AUCs were statistically significant (p The CDSS successfully showed potential for enhancing prediction of survival. The CDSS could assist physicians in formulating evidence-based management advice in patients with lung cancer, guiding an individualized discussion according to prognosis.enAtribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/clinical decision support systemdata mininglung cancerprognosissurvivalLung NeoplasmsSmall Cell Lung CarcinomaDecision Support Systems, ClinicalLinear ModelsProspective StudiesPrognosisAlgorithmsresearch article34992855open accessSobrevidaNeoplasias pulmonaresCarcinoma pulmonar de células pequeñasProteínas del sistema complementoMinería de datosPronósticoAlgoritmos10.5603/RPOR.a2021.0088PMC8726446https://journals.viamedica.pl/rpor/article/download/RPOR.a2021.0088/65198https://pmc.ncbi.nlm.nih.gov/articles/PMC8726446/pdf