# Scalable and Privacy-Conscious End-to-End Processing of Large-Scale Clinical Data for Precision Medicine: Empirical Evaluation Study

## TL;DR

This study shows that using Apache Parquet improves efficiency in processing large clinical data without sacrificing privacy or predictive accuracy.

## Contribution

A Parquet-based pipeline is empirically shown to enhance scalability and efficiency in clinical data analysis while preserving privacy and predictive performance.

## Key findings

- Parquet reduced disk access time by 95.3% compared to CSV, improving computational efficiency.
- Classification performance remained equivalent across metrics with no significant privacy risks detected.
- The CC ensemble minimized Hamming loss and maintained robustness in imbalanced clinical datasets.

## Abstract

In large-scale clinical data analysis, CSV and traditional relational database management system–based approaches are widely used but impose substantial storage and processing constraints that delay research preparation and hinder multicenter collaboration. Although column-oriented storage formats such as Apache Parquet have gained attention in data science, systematic end-to-end evaluations in clinical environments remain limited, particularly regarding efficiency and scalability.

This study aimed to empirically evaluate whether a Parquet-based end-to-end pipeline could improve computational efficiency and scalability in large-scale clinical data analysis while preserving predictive performance and protecting privacy.

Electronic health record data comprising 13.76 million rows from a large academic medical center in Korea were analyzed using Parquet, CSV, PostgreSQL, and DuckDB environments. Standardized SQL workloads and multilabel classification models—implemented using graphics processing unit–accelerated Extreme Gradient Boosting and classifier chain (CC) ensembles to address class imbalance—were applied to evaluate storage efficiency, time to analysis, and predictive performance. Statistical equivalence testing with prespecified clinical margins and bootstrap resampling ensured rigorous comparison, while privacy risks were assessed through advanced membership inference attacks (MIA), including shadow MIA and likelihood ratio attacks.

Compared with CSV, Parquet demonstrated enhanced computational efficiency by lowering disk access from 940.2 to 44.2 seconds (95.3% reduction). End-to-end processing latency was substantially reduced across feature transformation (15.0 vs 9.3 s) and model training (8.1 vs 6.7 s). To address complex clinical correlations, we implemented CC and one-vs-rest architectures, which effectively captured interdependencies between disease labels. Classification performance remained statistically equivalent across area under the receiver operating characteristic curve, area under the precision-recall curve, accuracy, and F1-score, with all differences falling within prespecified clinical equivalence margins (P<.001). Notably, the CC ensemble demonstrated high technical rigor, minimizing Hamming loss (2.2×10–4) and ensuring robustness even in imbalanced cohorts. MIA performed at chance level (area under the curve=0.500), suggesting no measurable increase in privacy risk.

By significantly mitigating data processing bottlenecks, a Parquet-based pipeline enabled high-throughput, large-scale clinical evidence generation without compromising model integrity or patient privacy. This framework provides a scalable and robust infrastructure for precision medicine, facilitating agile multicenter collaborations and real-world data analysis in resource-constrained clinical environments.