Recommendations for Reporting Machine Learning Analyses in Clinical Research

Abstract

Use of machine learning (ML) in clinical research is growing steadily given the increasing availability of complex clinical data sets. ML presents important advantages in terms of predictive performance and identifying undiscovered subpopulations of patients with specific physiology and prognoses. Despite this popularity, many clinicians and researchers are not yet familiar with evaluating and interpreting ML analyses. Consequently, readers and peer-reviewers alike may either overestimate or underestimate the validity and credibility of an ML-based model. Conversely, ML experts without clinical experience may present details of the analysis that are too granular for a clinical readership to assess. Overwhelming evidence has shown poor reproducibility and reporting of ML models in clinical research suggesting the need for ML analyses to be presented in a clear, concise, and comprehensible manner to facilitate understanding and critical evaluation. We present a recommendation for transparent and structured reporting of ML analysis results specifically directed at clinical researchers. Furthermore, we provide a list of key reporting elements with examples that can be used as a template when preparing and submitting ML-based manuscripts for the same audience.

Keywords: bioinformatics; machine learning; prognosis; report; reproducibility; research.

Figure 1 –

Overview of machine learning analysis workflow

Figure 2 –

Key reporting elements for machine learning study design examples

Figure 3 –

Key reporting elements for data sources and preprocessing with examples

Figure 4 –

CONSORT-style diagram illustrating disposition of subjects and features included in analysis

Figure 5 –

Key reporting elements for model training and validation with examples