Artificial Intelligence for Prognostic Scores in Oncology: a Benchmarking Study.

ABSTRACT: Introduction: Prognostic scores are important tools in oncology to facilitate clinical decision-making based on patient characteristics. To date, classic survival analysis using Cox proportional hazards regression has been employed in the development of these prognostic scores. With the advance of analytical models, this study aimed to determine if more complex machine-learning algorithms could outperform classical survival analysis methods. Methods: In this benchmarking study, two datasets were used to develop and compare different prognostic models for overall survival in pan-cancer populations: a nationwide EHR-derived de-identified database for training and in-sample testing and the OAK (phase III clinical trial) dataset for out-of-sample testing. A real-world database co

SUBMITTER: Loureiro H 

PROVIDER: S-EPMC8086599 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature