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Communicating exploratory unsupervised machine learning analysis in age clustering for paediatric disease.

Joshua William Spear^1,2, Eleni Pissaridou^1,2, Stuart Bowyer^1,2

¹DRIVE, Great Ormond Street Hospital for Children, London, UK.

BMJ Health & Care Informatics

|July 29, 2024

Summary

Machine learning applied to electronic healthcare records identified four age-based disease clusters in pediatric patients. Communicating uncertainty in data preprocessing is crucial for reliable clinical decision-making.

Keywords:

Data Science Data Visualization Electronic Health Records Machine Learning

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Area of Science:

Healthcare Informatics
Machine Learning Applications
Pediatric Medicine

Background:

Limited adoption of data-driven decision-making in hospitals despite available electronic healthcare record (EHR) data and machine learning (ML) tools.
Need to explore ML analysis of EHR data and effective communication of results to non-expert stakeholders.

Purpose of the Study:

To investigate ML analysis of EHR data for deriving age-based diagnosis clusters.
To assess the clinical validity and communication strategies for ML-driven insights.

Main Methods:

Utilized observational EHR data from a tertiary pediatric hospital (61,522 patients, 3315 diagnosis codes).
Applied K-means clustering to identify age distributions of diagnoses.
Selected the final model using quantitative metrics and expert clinical validation, analyzing preprocessing uncertainty.

Main Results:

Identified four distinct age clusters for diseases (0-1, 1-5, 5-13, 13-18 years).
Clusters aligned with known disease presentations and progressions, validating existing methodologies.
Preprocessing uncertainty significantly impacted individual diagnoses but not population-level results; mitigation strategies were demonstrated.

Conclusions:

Unsupervised ML on EHR data can identify clinically relevant age-disease distributions to enhance decision-making.
Healthcare data biases significantly affect ML results, necessitating mitigation or clear communication of uncertainty.