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Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A

Wei Luo¹, Dinh Phung², Truyen Tran²

¹Centre for Pattern Recognition and Data Analytics, School of Information Technology, Deakin University, Geelong, Australia.

Journal of Medical Internet Research

|December 18, 2016

Summary

This summary is machine-generated.

New guidelines ensure correct use and reporting of machine learning models in biomedical research. This promotes reliable big data analysis and accelerates discoveries using machine learning methods.

Keywords:

clinical prediction rule guideline machine learning

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Area of Science:

Biomedical research
Data science
Clinical informatics

Background:

Machine learning (ML) models are increasingly used in biomedical research for big data analysis.
The complexity of ML methods can lead to misuse and insufficient reporting in publications.
Lack of standardized reporting hinders the assessment of ML model validity and interpretation of results.

Framework:

Develop guidelines for the application and reporting of ML predictive models in clinical settings.
Ensure correct application and sufficient reporting to distinguish true discoveries from coincidental findings.
Establish a consensus among experts through a Delphi method involving ML specialists, clinicians, and statisticians.

Implementation:

A multidisciplinary panel utilized an iterative Delphi method for guideline development.
The process resulted in a comprehensive set of guidelines.
Guidelines include essential reporting items for research articles and practical steps for developing ML predictive models.

Implications:

Generated guidelines facilitate the correct application and consistent reporting of ML models in biomedical research.
These guidelines aim to accelerate the adoption of big data analysis and ML methods in the biomedical field.
Promotes reliable assessment and interpretation of ML model outputs, fostering trust in big data-driven discoveries.