OneEHR: Reproducible and AI Agent-Ready Longitudinal EHR Analysis Toolkit
TL;DR
This tutorial presents OneEHR, an open-source toolkit that defines a unified experiment contract for modern EHR modeling and enables head-to-head comparison among conventional, neural, LLM-based, and agentic methods through a single configuration-driven interface.
Abstract
Electronic health records support a wide spectrum of clinical prediction and decision-support studies, but reproducible EHR research now requires more than training a single predictive model. As the field expands from machine learning and deep learning to LLM-based and agentic AI, differences in cohort construction, temporal preprocessing, label definitions, patient-level splits, and evaluation protocols can overshadow the methods being compared, making fair comparison and model selection difficult in practice. This tutorial presents OneEHR, an open-source toolkit that defines a unified experiment contract for modern EHR modeling and enables head-to-head comparison among conventional, neural, LLM-based, and agentic methods through a single configuration-driven interface. The three-hour hands-on session interleaves a methodological survey with guided practice: participants will learn why EHR experiments are vulnerable to leakage, distribution shift, and irreproducible preprocessing, and then use OneEHR to configure, execute, compare, and interpret experiments across this method spectrum. Attendees will leave with reusable configurations and a practical framework for integrating reproducible workflows into their own clinical AI research. Code and documentation are available at https://medx-pku.github.io/OneEHR/.