# Bridging the EHR Divide: Asymmetric Contrastive Learning for Cross-National Medical Representation Transfer

arXiv: https://arxiv.org/abs/2610.04946 · HTML: https://mlsanity.com/paper/2610.04946/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 30 references that are in the map.
- First posted on arXiv 4 Oct 2026.

## Builds on (most cited first, 19 of 19)

- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models](https://mlsanity.com/paper/2506.05176/) (2025)
- [Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction](https://mlsanity.com/paper/2005.12833/) (2020)
- [Multilingual E5 Text Embeddings: A Technical Report](https://mlsanity.com/paper/2402.05672/) (2024)
- [Scalable and accurate deep learning for electronic health records](https://mlsanity.com/paper/1801.07860/) (2018)
- [BEHRT: Transformer for Electronic Health Records](https://mlsanity.com/paper/1907.09538/) (2019)
- [Co2L: Contrastive Continual Learning](https://mlsanity.com/paper/2106.14413/) (2021)
- [Self-Alignment Pretraining for Biomedical Entity Representations](https://mlsanity.com/paper/2010.11784/) (2020)
- [EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Models](https://mlsanity.com/paper/2307.02028/) (2023)
- [Language Models Are An Effective Patient Representation Learning Technique For Electronic Health Record Data](https://mlsanity.com/paper/2001.05295/) (2020)
- [MOTOR: A Time-To-Event Foundation Model For Structured Medical Records](https://mlsanity.com/paper/2301.03150/) (2023)
- [CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction tasks](https://mlsanity.com/paper/2111.08585/) (2021)
- [CODER: Knowledge infused cross-lingual medical term embedding for term normalization](https://mlsanity.com/paper/2011.02947/) (2020)
- [GenHPF: General Healthcare Predictive Framework with Multi-task Multi-source Learning](https://mlsanity.com/paper/2207.09858/) (2022)
- [A Multi-Center Study on the Adaptability of a Shared Foundation Model for Electronic Health Records](https://mlsanity.com/paper/2311.11483/) (2023)
- [Linq-Embed-Mistral Technical Report](https://mlsanity.com/paper/2412.03223/) (2024)
- [SCEHR: Supervised Contrastive Learning for Clinical Risk Prediction using Electronic Health Records](https://mlsanity.com/paper/2110.04943/) (2021)
- [MedRep: Medical Concept Representation for General Electronic Health Record Foundation Models](https://mlsanity.com/paper/2504.08329/) (2025)

## Abstract (arXiv)

Cross-system transfer of longitudinal Electronic Health Record (EHR) representations is challenging because clinical coding, patient populations, and healthcare workflows differ substantially across institutions and countries. We introduce Asymmetric Supervised Contrastive Learning (Asymmetric SupCon), a task-specific pre-training objective motivated by the heterogeneity of negative clinical outcomes. The objective clusters patients sharing a target positive outcome without explicitly attracting negative trajectories toward one another. We pre-train temporal Transformer encoders on longitudinal records from 3.98 million patients in the Taiwanese National Health Insurance Research Database (NHIRD) and transfer them to two U.S. EHR datasets, MIMIC-IV and EHRSHOT. A hybrid semantic mapping pipeline combining direct mappings with embedding-based retrieval enables transfer across heterogeneous clinical vocabularies. On MIMIC-IV, NHIRD pre-training consistently improves over random initialization while substantially narrowing the performance gap to task-specific in-domain pre-training. On EHRSHOT, the transferred models show particularly strong few-shot performance for incident disease prediction. A controlled objective ablation under a matched pre-training scale shows that Asymmetric SupCon achieves higher mean AUPRC than direct supervised BCE transfer on all four evaluated tasks and Standard SupCon on three of four, with a 0.003 AUPRC deficit on readmission. These results support asymmetric contrastive pre-training as an effective approach for task-specific cross-national EHR representation transfer. Code is available at https://github.com/qingYzhang/Asymmetric_SupCon.