Beyond the Model: The Critical Role of Data Filtering in Clinical Machine Learning

2026-10-05

AI Summary

This study addresses the critical yet underexamined role of data filtering in clinical machine learning, which alters statistical structures and directly impacts task complexity and model performance. Despite these effects, existing research frequently treats filtering as routine preprocessing with insufficient transparency. This work reconceptualizes data filtering as a core component of the scientific method, advocating its integration into the broader research paradigm rather than its treatment as a mere technical step. To this end, we develop a transparent and interpretable clinical data preprocessing pipeline and release the corresponding code as open source. Our analysis elucidates the mechanisms through which filtering decisions critically influence data distributions and downstream model efficacy. Ultimately, this research provides a novel framework for enhancing methodological rigor and reproducibility in clinical artificial intelligence studies.

Abstract

Machine learning (ML) studies using clinical data often rely on preprocessing and filtering pipelines before model development. The filtering decisions made in these pipelines can alter the dataset's statistical structure and may artificially reduce or increase the complexity of the prediction task. We argue that filtering choices should be treated as part of the scientific method rather than as a routine preprocessing step. We further discuss the need for explainable and transparent preprocessing pipelines that allow researchers to understand why specific filtering choices are made and how these choices affect the resulting data distribution and model performance. All of the source code for this work is available on GitHub.

Problem

• Clinical Machine Learning
• Data Filtering
• Preprocessing Pipelines
• Explainability
• Transparency

Innovation

• Data Filtering
• Clinical Machine Learning
• Explainable Preprocessing
• Transparent Pipelines
• Scientific Methodology