Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved.

ABSTRACT:

Objective

Evaluate the completeness of reporting of prognostic prediction models developed using machine learning methods in the field of oncology.

Study design and setting

We conducted a systematic review, searching the MEDLINE and Embase databases between 01/01/2019 and 05/09/2019, for non-imaging studies developing a prognostic clinical prediction model using machine learning methods (as defined by primary study authors) in oncology. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement to assess the reporting quality of included publications. We described overall reporting adherence of included publications and by each section of TRIPOD.

Results

Sixty-two publications met the inclusion criter

SUBMITTER: Dhiman P 

PROVIDER: S-EPMC8592577 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature