Machine learning-based clinical prediction modeling -- A practical guide for clinicians

TL;DR

This paper provides clinicians with a comprehensive, practical guide to understanding, developing, and evaluating machine learning models for clinical prediction, emphasizing methodological rigor and interpretability.

Contribution

It offers a detailed, step-by-step practical guide for clinicians on machine learning-based predictive modeling, including coding pipelines and evaluation strategies.

Findings

Highlights importance of model validation and generalizability

Provides coding pipelines for classification and regression models

Emphasizes understanding of machine learning principles for clinical use

Abstract

In the emerging era of big data, larger available clinical datasets and computational advances have sparked a massive interest in machine learning-based approaches. The number of manuscripts related to machine learning or artificial intelligence has exponentially increased over the past years. As analytical machine learning tools become readily available for clinicians to use, the understanding of key concepts and the awareness of analytical pitfalls are increasingly required for clinicians, investigators, reviewers and editors, who even as experts in their clinical field, sometimes find themselves insufficiently equipped to evaluate machine learning methodologies. In the first section, we provide explanations on the general principles of machine learning, as well as analytical steps required for successful machine learning-based predictive modelling - which is the focus of this series. In further sections, we review the importance of resampling, overfitting and model generalizability as well as feature reduction and selection (Part II), strategies for model evaluation, reporting and discussion of common caveats and other points of significance (Part III), as well as offer a practical guide to classification (Part IV) and regression modelling (Part V), with a complete coding pipeline. Methodological rigor and clarity as well as understanding of the underlying reasoning of the internal workings of a machine learning approach are required, otherwise predictive applications despite being strong analytical tools are not well accepted into the clinical routine. Going forward, machine learning and artificial intelligence shape and influence modern medicine across disciplines including the field of neurosurgery.