# EHR2Path: Comprehensive Pathway-Level Modeling of Longitudinal Patient Trajectories from Multimodal Electronic Health Records

arXiv: https://arxiv.org/abs/2506.04831 · HTML: https://mlsanity.com/paper/2506.04831/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 35 references that are in the map.
- First posted on arXiv 5 Jun 2025.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction](https://mlsanity.com/paper/2005.12833/) (2020)
- [BEHRT: Transformer for Electronic Health Records](https://mlsanity.com/paper/1907.09538/) (2019)
- [GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records](https://mlsanity.com/paper/2203.03540/) (2022)
- [MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III](https://mlsanity.com/paper/1907.08322/) (2019)
- [EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Models](https://mlsanity.com/paper/2307.02028/) (2023)
- [MOTOR: A Time-To-Event Foundation Model For Structured Medical Records](https://mlsanity.com/paper/2301.03150/) (2023)
- [Zero Shot Health Trajectory Prediction Using Transformer](https://mlsanity.com/paper/2407.21124/) (2024)
- [CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction tasks](https://mlsanity.com/paper/2111.08585/) (2021)
- [Hi-BEHRT: Hierarchical Transformer-based model for accurate prediction of clinical events using multimodal longitudinal electronic health records](https://mlsanity.com/paper/2106.11360/) (2021)
- [Event Stream GPT: A Data Pre-processing and Modeling Library for Generative, Pre-trained Transformers over Continuous-time Sequences of Complex Events](https://mlsanity.com/paper/2306.11547/) (2023)
- [Context Clues: Evaluating Long Context Models for Clinical Prediction Tasks on EHRs](https://mlsanity.com/paper/2412.16178/) (2024)
- [GenHPF: General Healthcare Predictive Framework with Multi-task Multi-source Learning](https://mlsanity.com/paper/2207.09858/) (2022)
- [EHRMamba: Towards Generalizable and Scalable Foundation Models for Electronic Health Records](https://mlsanity.com/paper/2405.14567/) (2024)
- [Emergency Department Decision Support using Clinical Pseudo-notes](https://mlsanity.com/paper/2402.00160/) (2024)
- [CEHR-GPT: Generating Electronic Health Records with Chronological Patient Timelines](https://mlsanity.com/paper/2402.04400/) (2024)
- [General-Purpose Retrieval-Enhanced Medical Prediction Model Using Near-Infinite History](https://mlsanity.com/paper/2310.20204/) (2023)
- [Unsupervised pre-training of graph transformers on patient population graphs](https://mlsanity.com/paper/2207.10603/) (2022)
- [Revisiting the MIMIC-IV Benchmark: Experiments Using Language Models for Electronic Health Records](https://mlsanity.com/paper/2504.20547/) (2025)
- [UniHPF : Universal Healthcare Predictive Framework with Zero Domain Knowledge](https://mlsanity.com/paper/2211.08082/) (2022)

## Built on by (most cited first, 3 of 3)

- [A multimodal and temporal foundation model for virtual patient representations at healthcare system scale](https://mlsanity.com/paper/2604.18570/) (2026)
- [TwinWeaver: An LLM-Based Foundation Model Framework for Pan-Cancer Digital Twins](https://mlsanity.com/paper/2601.20906/) (2026)
- [OphthaDT: Generative Digital Twins for Forecasting Visual Acuity Trajectories in Ophthalmology](https://mlsanity.com/paper/2606.22101/) (2026)

## Abstract (arXiv)

Forecasting how a patient's condition is likely to evolve, including possible deterioration, recovery, treatment needs, and care transitions, could support more proactive and personalized care, but requires modeling heterogeneous and longitudinal electronic health record (EHR) data. Yet, existing approaches typically focus on isolated prediction tasks, narrow feature spaces, or short context windows, limiting their ability to model full patient pathways. To address this gap, we introduce EHR2Path, a multimodal framework for forecasting and simulating full in-hospital patient pathways from routine EHRs. EHR2Path converts diverse clinical inputs into a unified temporal representation, enabling modeling of a substantially broader set of patient information, including radiology reports, physician notes, vital signs, medication and laboratory patterns, and dense bedside charting. To support long clinical histories and broad feature spaces, we introduce a Masked Summarization Bottleneck that compresses long-term history into compact, task-optimized summary tokens while preserving recent context, improving both performance and token efficiency. In retrospective experiments on MIMIC-IV, EHR2Path enables next-step pathway forecasting and iterative simulation of complete in-hospital trajectories, while outperforming strong baselines on directly comparable tasks. These results demonstrate the feasibility of pathway-level modeling from routine EHRs, and indicate potential for supporting anticipatory clinical decision-making. Our code is available at https://github.com/ChantalMP/EHR2Path.