Machine learning models including patient-reported outcome data in oncology: a systematic literature review and analysis of their reporting quality.

ABSTRACT:

Purpose

To critically examine the current state of machine learning (ML) models including patient-reported outcome measure (PROM) scores in cancer research, by investigating the reporting quality of currently available studies and proposing areas of improvement for future use of ML in the field.

Methods

PubMed and Web of Science were systematically searched for publications of studies on patients with cancer applying ML models with PROM scores as either predictors or outcomes. The reporting quality of applied ML models was assessed utilizing an adapted version of the MI-CLAIM (Minimum Information about CLinical Artificial Intelligence Modelling) checklist. The key variables of the checklist are study design, data preparation, model development, optimization, performance, and

SUBMITTER: Krepper D 

PROVIDER: S-EPMC11538124 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature