Table 1.

Recommended structure for reporting ML^a models.

Research question and ML justification	Data sources and preprocessing (feature selection)	Model training and validation
Clinical question	Population	Hardware, software, and packages used
Intended use of the result	Sample record and measurement characteristics	Evaluation (calibration and discrimination)
Defined problem type	Data collection and quality	Configuration (parameters and hyperparameters)
Available data	Data structure and types	Model optimization and generalization (hyperparameter tuning and parameter limits)
Defined ML method and rationale	Differences between evaluation and validation sets	Validation method and data split and cross-validation
Defined evaluation measures, training protocols, and validation	Data preprocessing (data aggregation, missing data, transformation, and label source)	Validation method performance metrics on an external data set
N/Ab	Input configuration	Reproducibility, code reuse, and explainability

^aML: machine learning.

^bN/A: not applicable.