Statistical Inference for Clustering Results Interpretation in Clinical Practice

Area of Science:

• Machine Learning
• Data Science
• Statistical Inference

Background:

• Understanding machine learning model outputs, particularly clustering, is crucial for reliable application.
• Clinical pathway modeling often involves complex datasets requiring effective interpretation.

Purpose of the Study:

• To present a novel statistical inference-based method for interpreting clustering results.
• To apply this method to clinical pathway modeling for enhanced understanding.
• To identify characteristic features that differentiate clusters.

Main Methods:

• Developed a statistical inference approach to analyze and describe clusters.
• Quantified the influence of specific features on cluster distinctions.
• Applied the method to a clinical pathway dataset.

Main Results:

• The method successfully described distinct clusters based on feature influence.
• Characteristic features for each cluster were identified.
• The approach provided interpretable insights into the clinical pathway data.

Conclusions:

• The proposed method enhances the interpretability of machine learning clustering.
• It offers a valuable tool for analyzing complex datasets, such as clinical pathways.
• The findings support the use of statistical inference for explaining model behavior.