Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review

Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review

Abstract Background While many studies have consistently found incomplete reporting of regression-based prediction model studies, evidence is lacking for machine learning-based prediction model studies. We aim to systematically review the adherence of Machine Learning (ML)-based prediction model stu...

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Bibliographic Details
Main Authors: Constanza L. Andaur Navarro, Johanna A. A. Damen, Toshihiko Takada, Steven W. J. Nijman, Paula Dhiman, Jie Ma, Gary S. Collins, Ram Bajpai, Richard D. Riley, Karel G. M. Moons, Lotty Hooft
Format: Article
Language:English
Published: BMC 2022-01-01
Series:BMC Medical Research Methodology
Subjects:
Prediction model
Diagnosis
Prognosis
Development
Validation
Reporting adherence
Online Access:https://doi.org/10.1186/s12874-021-01469-6

Internet

https://doi.org/10.1186/s12874-021-01469-6

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