Patient Phenotyping with Scalable Clustering in Electronic Record Warehouses: Benchmark Study

Abstract

The exponential growth of data within electronic record warehouses presents significant opportunities and challenges for clinical informatics, particularly in the domain of patient phenotyping. Patient phenotyping involves the classification of individuals into clinically meaningful subgroups based on shared characteristics derived from longitudinal health data. As dataset sizes expand to encompass millions of records, traditional rule-based and supervised machine learning approaches struggle with scalability, generalizability, and the discovery of novel or atypical patient presentations. This paper presents a comprehensive benchmark study evaluating the application of scalable clustering algorithms for unsupervised patient phenotyping within large-scale electronic record warehouses. By systematically comparing distributed and mini-batch clustering architectures, the research identifies optimal methodologies for processing high-dimensional, sparse, and temporally complex clinical data. The study addresses the critical bottlenecks of data preprocessing, feature extraction, and algorithm selection, providing a robust analytical framework for evaluating clustering validity and clinical relevance. Through rigorous empirical analysis, the benchmark highlights the trade-offs between computational efficiency and phenotypic accuracy. The findings demonstrate that highly scalable clustering techniques not only match the precision of traditional methods but also uncover nuanced sub-phenotypes that enhance personalized medicine initiatives. This research establishes foundational guidelines for implementing high-throughput phenotyping pipelines in modern healthcare systems.