# MEDS-Tab: Automated tabularization and baseline methods for MEDS datasets

arXiv: https://arxiv.org/abs/2411.00200 · HTML: https://mlsanity.com/paper/2411.00200/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 58 references that are in the map.
- First posted on arXiv 31 Oct 2024.

## Builds on (most cited first, 30 of 30)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [Multitask learning and benchmarking with clinical time series data](https://mlsanity.com/paper/1703.07771/) (2017)
- [sktime: A Unified Interface for Machine Learning with Time Series](https://mlsanity.com/paper/1909.07872/) (2019)
- [Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-Series](https://mlsanity.com/paper/2107.14293/) (2021)
- [HyperImpute: Generalized Iterative Imputation with Automatic Model Selection](https://mlsanity.com/paper/2206.07769/) (2022)
- [HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Data](https://mlsanity.com/paper/2102.04252/) (2021)
- [Learning from Irregularly-Sampled Time Series: A Missing Data Perspective](https://mlsanity.com/paper/2008.07599/) (2020)
- [The choice of scaling technique matters for classification performance](https://mlsanity.com/paper/2212.12343/) (2022)
- [Multimodal Pretraining of Medical Time Series and Notes](https://mlsanity.com/paper/2312.06855/) (2023)
- [DuETT: Dual Event Time Transformer for Electronic Health Records](https://mlsanity.com/paper/2304.13017/) (2023)
- [Deep Contextual Clinical Prediction with Reverse Distillation](https://mlsanity.com/paper/2007.05611/) (2020)
- [The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development](https://mlsanity.com/paper/1905.08942/) (2019)
- [Event-Based Contrastive Learning for Medical Time Series](https://mlsanity.com/paper/2312.10308/) (2023)
- [Clairvoyance: A Pipeline Toolkit for Medical Time Series](https://mlsanity.com/paper/2310.18688/) (2023)
- [Temporal Supervised Contrastive Learning for Modeling Patient Risk Progression](https://mlsanity.com/paper/2312.05933/) (2023)
- [PyHealth: A Python Library for Health Predictive Models](https://mlsanity.com/paper/2101.04209/) (2021)
- [Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task Learning](https://mlsanity.com/paper/2006.12777/) (2020)
- [A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series Data](https://mlsanity.com/paper/2007.10185/) (2020)
- [Interpretable Survival Analysis for Heart Failure Risk Prediction](https://mlsanity.com/paper/2310.15472/) (2023)
- [TemporAI: Facilitating Machine Learning Innovation in Time Domain Tasks for Medicine](https://mlsanity.com/paper/2301.12260/) (2023)
- [On the Importance of Step-wise Embeddings for Heterogeneous Clinical Time-Series](https://mlsanity.com/paper/2311.08902/) (2023)
- [Dynamic Survival Analysis for Early Event Prediction](https://mlsanity.com/paper/2403.12818/) (2024)
- [Privacy-preserving patient clustering for personalized federated learning](https://mlsanity.com/paper/2307.08847/) (2023)
- [Cardea: An Open Automated Machine Learning Framework for Electronic Health Records](https://mlsanity.com/paper/2010.00509/) (2020)
- [MULTIPAR: Supervised Irregular Tensor Factorization with Multi-task Learning](https://mlsanity.com/paper/2208.00993/) (2022)
- [From Basic to Extra Features: Hypergraph Transformer Pretrain-then-Finetuning for Balanced Clinical Predictions on EHR](https://mlsanity.com/paper/2406.05682/) (2024)

## Built on by (most cited first, 4 of 4)

- [Foundation Model of Electronic Medical Records for Adaptive Risk Estimation](https://mlsanity.com/paper/2502.06124/) (2025)
- [PyHealth 2.0: A Comprehensive Open-Source Toolkit for Accessible and Reproducible Clinical Deep Learning](https://mlsanity.com/paper/2601.16414/) (2026)
- [Generating Multi-Table Time Series EHR from Latent Space with Minimal Preprocessing](https://mlsanity.com/paper/2507.06996/) (2025)
- [Clinical Data Goes MEDS? Let's OWL make sense of it](https://mlsanity.com/paper/2601.04164/) (2026)

## Abstract (arXiv)

Effective, reliable, and scalable development of machine learning (ML) solutions for structured electronic health record (EHR) data requires the ability to reliably generate high-quality baseline models for diverse supervised learning tasks in an efficient and performant manner. Historically, producing such baseline models has been a largely manual effort--individual researchers would need to decide on the particular featurization and tabularization processes to apply to their individual raw, longitudinal data; and then train a supervised model over those data to produce a baseline result to compare novel methods against, all for just one task and one dataset. In this work, powered by complementary advances in core data standardization through the MEDS framework, we dramatically simplify and accelerate this process of tabularizing irregularly sampled time-series data, providing researchers the ability to automatically and scalably featurize and tabularize their longitudinal EHR data across tens of thousands of individual features, hundreds of millions of clinical events, and diverse windowing horizons and aggregation strategies, all before ultimately leveraging these tabular data to automatically produce high-caliber XGBoost baselines in a highly computationally efficient manner. This system scales to dramatically larger datasets than tabularization tools currently available to the community and enables researchers with any MEDS format dataset to immediately begin producing reliable and performant baseline prediction results on various tasks, with minimal human effort required. This system will greatly enhance the reliability, reproducibility, and ease of development of powerful ML solutions for health problems across diverse datasets and clinical settings.