| Challenge: | RareSyn is a data synthesis approach to augment and de-identify EHRs with a focus on rare diseases. |
| Approach: | They propose a data synthesis approach to augment and de-identify EHRs with a focus on rare diseases. |
| Outcome: | The proposed model augments and de-identifies EHRs with a focus on rare diseases. |
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| Challenge: | Existing methods for generating longitudinal multimodal EHRs are limited due to privacy concerns. |
| Approach: | They propose to generate longitudinal multimodal EHRs by unconditional generation or longitudinal inference . existing methods generate single-modal E HRs by conditional generation or by longitudinal inferment . |
| Outcome: | The proposed method is more flexible and controllable than existing methods and is more cost-effective than existing ones. |
Eliciting Medical Reasoning with Knowledge-enhanced Data Synthesis: A Semi-Supervised Reinforcement Learning Approach (2026.findings-acl)
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| Challenge: | Existing methods to enhance medical reasoning lack high-quality data. |
| Approach: | They propose a medical knowledge-enhanced data Synthesis and Semi-supervised Reinforcement learning framework that uses rare disease knowledge to synthesize distribution-controllable reasoning questions. |
| Outcome: | The proposed method outperforms existing methods across ten medical benchmarks and achieves up to 5.93% gain on rare diseases tasks. |
RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic Health Records (2024.acl-short)
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| Challenge: | Existing deep learning models for EHRs rely on knowledge from a single source and do not capture the semantic information for medical codes. |
| Approach: | They propose a Retrieval AugMentation pipeline to augment clinical prediction on EHRs . they use multiple knowledge sources to convert them into text and use consistency regularization to capture complementary information from patient visits and summarized knowledge. |
| Outcome: | Experiments on two EHR datasets show that RAM-EHR improves clinical prediction tasks. |
MKeCL: Medical Knowledge-Enhanced Contrastive Learning for Few-shot Disease Diagnosis (2024.lrec-main)
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| Challenge: | Existing approaches to disease classification are limited in real-world clinics due to insufficient data and inflexibility. |
| Approach: | They propose a medical knowledge-Enhanced Contrastive Learning approach to disease diagnosis . they incorporate medical knowledge graphs and medical licensing exams in modeling . |
| Outcome: | The proposed model outperforms existing models on real clinical EMRs on a single patient. |
MHGRL: An Effective Representation Learning Model for Electronic Health Records (2024.lrec-main)
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| Challenge: | Effective EHR representations are key to achieving high performance in healthcare applications. |
| Approach: | They propose a multimodal heterogeneous graph-enhanced representation learning to learn EHR representations using medical ontology and textual notes. |
| Outcome: | The proposed model outperforms baseline models on two real clinical datasets in downstream tasks. |
Generating Synthetic Free-text Medical Records with Low Re-identification Risk using Masked Language Modeling (2025.naacl-srw)
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| Challenge: | Existing methods to generate medical records using Causal Language Modelling are limited due to privacy concerns. |
| Approach: | They propose a method for generating medical records using Masked Language Modelling using Causal language models. |
| Outcome: | The proposed method produces high-quality synthetic data with a re-identification risk of only 3.5% and a patient recall of 96%. |
Fine-grained Contrastive Learning for Definition Generation (2022.aacl-main)
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| Challenge: | Recent pre-trained transformer-based definition generation models lack effective representation learning to contain full semantic components of the given word, leading to under-specific definitions. |
| Approach: | They propose a novel contrastive learning method that encourages the model to capture more detailed semantic representations from the definition sequence encoding. |
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How to leverage the multimodal EHR data for better medical prediction? (2021.emnlp-main)
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| Challenge: | Using deep learning to improve healthcare is challenging due to the complexity of EHR data. |
| Approach: | They propose a method to integrate clinical notes from EHR and combine them with different data to improve prediction performance. |
| Outcome: | The proposed model outperforms the state-of-the-art method without clinical notes on two prediction tasks. |
Two Directions for Clinical Data Generation with Large Language Models: Data-to-Label and Label-to-Data (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) can generate natural language texts for various domains and tasks, but their potential for clinical text mining is under-explored. |
| Approach: | They propose a pragmatic taxonomy for AD sign and symptom progression based on expert knowledge and train a system to detect AD-related signs and symptoms from EHRs. |
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Text-Attributed Knowledge Graph Enrichment with Large Language Models for Medical Concept Representation (2026.acl-long)
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| Challenge: | eHRs encode a patient's medical history as a high-dimensional and sparse sequence of diagnosis, medication, and procedure concepts . robust concept representation learning is hindered by key challenges, authors say . clinically important cross-type dependencies are often missing or incomplete in existing ontology resources . |
| Approach: | They propose a graph learning framework that integrates semantics with medical concepts to improve prediction performance. |
| Outcome: | The proposed framework improves prediction performance and integrates semantics with graph structure. |