MEDS-Tab: Scalable Tabularization for Reproducible MEDS Baselines

Classical tabular models are widely recognized as strong, often competitive baselines for structured prediction tasks on electronic health record (EHR) data, making them an important evaluation target for new machine learning models in this space. However, producing such baselines for longitudinal EHR datasets remains difficult: before any downstream model can be trained, researchers must convert irregular longitudinal event streams into temporal leakage-safe, task-aligned, fixed-dimensional feature matrices. While modern AutoML and hyperparameter optimization tools are incredibly effective at tuning models once such a matrix exists, they largely assume that this upstream tabularization problem has already been solved; however, this is often not the case. To help address this challenge, we introduce MEDS-Tab, a scalable tabularization system for datasets represented in the Medical Event Data Standard (MEDS). Given a MEDS event-stream-formatted dataset and task labels, MEDS-Tab constructs sparse, sharded feature matrices over heterogeneous clinical codes, lookback windows, and aggregation functions, with optional utilities for training downstream tabular baselines. MEDS-Tab scales to datasets with hundreds of millions of events and tens of thousands of codes. In our scalability experiments, MEDS-Tab completes all runs, while competing tools take much longer, and use substantially more memory or run out of memory. MEDS-Tab is also usable as shared infrastructure: it provides public documentation and a MIMIC-IV tutorial. It has received 17,836 PyPI downloads as of September 25, 2026 and has been used to generate baselines in five external studies. Across these studies, MEDS-Tab provides strong tabular baselines, achieving higher reported AUROC than every conventional baseline in 90.2% of study-dataset-task settings and outperforms all models, including neural and foundation models, in 62.7% of settings. By filling the infrastructure gap between standardized EHR event streams and reproducible classical ML baselines, MEDS-Tab improves the ease, scalability, and reproducibility of benchmarking across clinical prediction tasks and publications.