Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Modeling Studies: Development and Validation.

ABSTRACT:

Background

The reporting of machine learning (ML) prognostic and diagnostic modeling studies is often inadequate, making it difficult to understand and replicate such studies. To address this issue, multiple consensus and expert reporting guidelines for ML studies have been published. However, these guidelines cover different parts of the analytics lifecycle, and individually, none of them provide a complete set of reporting requirements.

Objective

We aimed to consolidate the ML reporting guidelines and checklists in the literature to provide reporting items for prognostic and diagnostic ML in in-silico and shadow mode studies.

Methods

We conducted a literature search that identified 192 unique peer-reviewed English articles that provide guidance and checklists for re

SUBMITTER: Klement W 

PROVIDER: S-EPMC10502599 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature