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

• URL: http://arxiv.org/abs/2411.00200v1
• Date: Thu, 31 Oct 2024 20:36:37 GMT
• Title: MEDS-Tab: Automated tabularization and baseline methods for MEDS datasets
• Authors: Nassim Oufattole, Teya Bergamaschi, Aleksia Kolo, Hyewon Jeong, Hanna Gaggin, Collin M. Stultz, Matthew B. A. McDermott,
• Abstract summary: This work is powered by complementary advances in core data standardization through the MEDS framework. We dramatically simplify and accelerate this process of scalably featurizing irregularly sampled time-series data. This system will greatly enhance the reliability, scalable, and ease of development of powerful ML solutions for health problems across diverse datasets and clinical settings.
• Score: 2.8209943093430443
• License:
• Abstract: 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.

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