Explainable machine learning in healthcare: methods, interpretation, and applications for clinical research

Area of Science:

• Healthcare data science
• Machine learning interpretability
• Clinical decision support systems

Background:

• Traditional predictive models in healthcare often lack transparency.
• Bridging the gap between complex machine learning (ML) and clinical practice is crucial.
• Fostering trust and accountability in AI-driven healthcare requires interpretable models.

Purpose of the Study:

• To provide a practical overview of explainable machine learning (XML) in healthcare.
• To emphasize the interpretation and application of XML in clinical research and decision support.
• To foster trust and transparency in ML applications for informed clinical decision-making.

Main Methods:

• Structured review of common XML methodologies including SHAP, LIME, PDP, and ICE plots.
• High-level explanation of underlying mechanisms for each XML method.
• Guidance on interpretation, appropriate use, and limitations, illustrated with a Heart Disease dataset.

Main Results:

• XML techniques provide visual and quantitative insights into predictor influence on ML model predictions.
• Global methods characterize population-level effects; local methods offer patient-level contributions.
• Worked examples demonstrate XML's ability to identify relationships, interactions, and risk heterogeneity.

Conclusions:

• XML tools enhance interpretability and transparency in healthcare ML applications.
• This primer bridges advanced ML methodology with clinical applicability through practical examples.
• Adoption of XML supports better understanding, communication, and evaluation of ML predictions in healthcare research.