Papers with EHRs
Applications of Natural Language Processing in Clinical Research and Practice (N19-5)
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| Challenge: | a tutorial on clinical NLP will introduce students and experts to the field . a focus will be on the use of clinical Nlp in clinical research and practice . |
| Approach: | This tutorial introduces the clinical use of natural language processing (NLP) techniques . it will review techniques and tools developed for the clinical domain . |
| Outcome: | This tutorial will introduce the clinical NLP methodologies and tools at two top universities . the goal of the tutorial is to encourage NLP researchers in the general domain to contribute . |
Assessing the Efficacy of Clinical Sentiment Analysis and Topic Extraction in Psychiatric Readmission Risk Prediction (D19-62)
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Elena Alvarez-Mellado, Eben Holderness, Nicholas Miller, Fyonn Dhang, Philip Cawkwell, Kirsten Bolton, James Pustejovsky, Mei-Hua Hall
| Challenge: | Previously, readmission risk classifications rely on structured information, such as sociodemographic data, comorbidity codes and physiological variables. |
| Approach: | They propose to incorporate additional clinically interpretable NLP-based features such as topic extraction and clinical sentiment analysis to predict early readmission risk in psychiatry patients. |
| Outcome: | The proposed model incorporates topic extraction and clinical sentiment analysis to predict early readmission risk in psychiatry patients. |
JointCoder: Exploring Automated ICD Coding on Real-World Chinese EHRs with a Multi-Agent Framework (2026.acl-demo)
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| Challenge: | Existing automated ICD coding systems face several fundamental challenges due to the limited availability of publicly available Chinese ICD datasets. |
| Approach: | They propose to use a Chinese ICD coding dataset and a multi-agent framework to reformulate ICD as a joint disease-procedure coding task. |
| Outcome: | The proposed system outperforms state-of-the-art methods on real-world Chinese ICD coding datasets and 1.7B-parameter models. |
Medical Summarization in Practice: Design, Deployment, and Analysis of a Clinical Summarization System for a German Hospital (2026.eacl-industry)
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| Challenge: | a large number of EHRs are created for a patient, which must be summarized into a discharge summary. |
| Approach: | They propose to integrate a clinical summarization system into a live german hospital workflow to help with the generation of discharge summaries. |
| Outcome: | The proposed system can be used in a live german hospital to help with discharge summaries. |
Efficient Diagnosis Assignment Using Unstructured Clinical Notes (2023.acl-short)
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| Challenge: | Electronic phenotyping entails using electronic health records (EHRs) to identify patients with specific clinical outcomes and determine when those outcomes occurred. |
| Approach: | They propose a framework for electronic phenotyping that integrates labeling functions and a disease-agnostic neural network to assign diagnoses to patients. |
| Outcome: | The proposed framework disambiguates hypertension true positives and false positives with a supervised area under the precision-recall curve (AUPRC) of 0.85. |
THCM-CAL: Temporal-Hierarchical Causal Modelling with Conformal Calibration for Clinical Risk Prediction (2025.findings-emnlp)
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| Challenge: | Existing approaches to risk prediction from EHRs handle structured diagnostic codes and unstructured narrative notes separately. |
| Approach: | They propose a Temporal-Hierarchical Causal Model with Conformal Calibration . they construct a multimodal causal graph where nodes represent clinical entities from two modalities . |
| Outcome: | The proposed model infers three clinically grounded interactions from textual propositions and ICD codes mapped to textual descriptions. |
DialMed: A Dataset for Dialogue-based Medication Recommendation (2022.coling-1)
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| Challenge: | Existing studies on medication recommendation mainly rely on EHRs, but some details of interactions between doctors and patients may be ignored or omitted in EHR. |
| Approach: | They propose to use medical dialogues to recommend medications with medical dialogue data . they propose to model dialogue structure and disease knowledge aware network . |
| Outcome: | The proposed method is a promising solution to recommend medications with medical dialogues. |
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. |
DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related Queries (2022.lrec-1)
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| Challenge: | a new question answering dataset is being developed for electronic health records . structured tables and unstructured notes can be duplicated, contradictory or provide additional context . |
| Approach: | They develop a question-answer-matching dataset using structured tables and unstructured notes from an EHR. |
| Outcome: | The proposed model is based on a model with a modality selection network . it uses the prediction of a RAT-SQL to choose between EHR tables and clinical notes . |
RuCCoD: Towards Automated ICD Coding in Russian (2025.emnlp-main)
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Alexandr Nesterov, Andrey Sakhovskiy, Ivan Sviridov, Airat Valiev, Vladimir Makharev, Petr Anokhin, Galina Zubkova, Elena Tutubalina
| Challenge: | a new dataset for clinical coding in Russian is available for download . human coders must navigate a wide array of medical terminology and time pressures . |
| Approach: | They present a new dataset for ICD coding in Russian, a language with limited biomedical resources. |
| Outcome: | The proposed model improves accuracy on an in-house EHR dataset from 2017 to 2021. |
MedEureka: A Medical Domain Benchmark for Multi-Granularity and Multi-Data-Type Embedding-Based Retrieval (2025.findings-naacl)
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| Challenge: | Embedding-based retrieval (EBR) is a mainstream approach in information retrieval. |
| Approach: | They propose an enriched benchmark to evaluate retrieval capabilities of embedding models . they use four levels of granularity and six types of medical texts to prompt instruction-fine-tuned embeddable models. |
| Outcome: | The proposed benchmark evaluates the retrieval capabilities of embedding models with multi-granularity and multi-data types. |
Using Deep Neural Networks with Intra- and Inter-Sentence Context to Classify Suicidal Behaviour (2020.lrec-1)
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Xingyi Song, Johnny Downs, Sumithra Velupillai, Rachel Holden, Maxim Kikoler, Kalina Bontcheva, Rina Dutta, Angus Roberts
| Challenge: | Mental health problems are a major risk factor for suicide attempts. |
| Approach: | They propose to integrate information from sentences to left and right of the target sentence into the model to improve classification accuracy. |
| Outcome: | The proposed model was able to classify suicidal behaviour in autism spectrum disorder patient records significantly better than previous approaches. |
A Corpus for Detecting High-Context Medical Conditions in Intensive Care Patient Notes Focusing on Frequently Readmitted Patients (2020.lrec-1)
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Edward T. Moseley, Joy T. Wu, Jonathan Welt, John Foote, Patrick D. Tyler, David W. Grant, Eric T. Carlson, Sebastian Gehrmann, Franck Dernoncourt, Leo Anthony Celi
| Challenge: | Currently, most medical data is generated and stored in unstructured, text-based format. |
| Approach: | They propose to use a patient phenotyping dataset to identify whether a given medical condition is present in their notes. |
| Outcome: | The proposed dataset contains 1102 Discharge Summaries and 1000 Nursing Progress Notes. |
PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learning (2022.emnlp-main)
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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. |
RGAR: Recurrence Generation-augmented Retrieval for Factual-aware Medical Question Answering (2025.findings-emnlp)
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| Challenge: | Existing retrieval approaches often overlook patient-specific factual knowledge embedded in EHRs . existing retrieval frameworks often overlook this factual information, limiting its effectiveness in clinical decision-making. |
| Approach: | They propose a recurrence generation-augmented retrieval framework that synergizes factual and conceptual knowledge from dual sources. |
| Outcome: | The proposed framework improves on factual-aware medical QA benchmarks. |
Evaluation and LLM-Guided Learning of ICD Coding Rationales (2026.eacl-long)
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| Challenge: | Existing studies on the explainability of ICD coding rely on attention-based rationales and qualitative assessments conducted by physicians. |
| Approach: | They propose to evaluate the explainability of rationales in ICD coding using a multi-granular rationale-annotated dataset. |
| Outcome: | The proposed model improves the explainability of rationales in ICD coding by using human-annotated rationale-announced rationale models. |
That’s the Wrong Lung! Evaluating and Improving the Interpretability of Unsupervised Multimodal Encoders for Medical Data (2022.emnlp-main)
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| Challenge: | Recent multimodal models induce soft local alignments between image regions and sentences. |
| Approach: | They compare alignments from a state-of-the-art multimodal model for EHR with human annotations that link image regions to sentences. |
| Outcome: | The proposed models induce soft local alignments between image regions and sentences . the text has an often weak or unintuitive influence on attention, the authors found . |
Development of a Corpus Annotated with Medications and their Attributes in Psychiatric Health Records (2020.lrec-1)
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Jaya Chaturvedi, Natalia Viani, Jyoti Sanyal, Chloe Tytherleigh, Idil Hasan, Kate Baird, Sumithra Velupillai, Robert Stewart, Angus Roberts
| Challenge: | Free text fields within electronic health records (EHRs) contain valuable clinical information which is often missed when conducting research using EHR databases. |
| Approach: | They propose to extract medication annotations from mental health records by including contextual information around them. |
| Outcome: | The aim of the study is to provide a more complete picture behind the mention of medications in the health records, by including additional contextual information around them. |
TreeMAN: Tree-enhanced Multimodal Attention Network for ICD Coding (2022.coling-1)
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| Challenge: | Existing methods to automatically assign ICD codes ignore crucial information contained in structured medical data, which is hard to be captured from the noisy clinical notes. |
| Approach: | They propose to use a Tree-enhanced multimodal attention network to fuse tabular features and textual features into multimodal representations by enhancing the text representations with tree-based features. |
| Outcome: | The proposed method outperforms state-of-the-art methods on two MIMIC datasets. |
Clinical Note Owns its Hierarchy: Multi-Level Hypergraph Neural Networks for Patient-Level Representation Learning (2023.acl-long)
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| Challenge: | Clinical notes of patient EHRs contain valuable information from healthcare professionals, but have been underutilized due to their difficult-to-understand contents and complex hierarchies. |
| Approach: | They propose to use clinical notes to learn more balanced knowledge from EHRs by assembling useful neutral words with rare keywords via note and taxonomy level hyperedges. |
| Outcome: | The proposed method can retain clinical semantic information by (1) frequent neutral words and (2) hierarchies with imbalanced distribution. |
Towards Reducing Diagnostic Errors with Interpretable Risk Prediction (2024.naacl-long)
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Denis McInerney, William Dickinson, Lucy Flynn, Andrea Young, Geoffrey Young, Jan-Willem van de Meent, Byron Wallace
| Challenge: | Diagnostic errors occur because clinicians cannot easily access relevant information in EHRs. |
| Approach: | They propose a method to use LLMs to identify pieces of evidence that indicate increased or decreased risk of specific diagnoses in patient EHRs. |
| Outcome: | The proposed method reduces diagnostic errors by identifying evidence in patient EHRs . it uses a Neural Additive Model to make predictions backed by evidence at time-points where clinicians are uncertain . |
Uncertainty Quantification for Clinical Outcome Predictions with (Large) Language Models (2025.findings-naacl)
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| Challenge: | Language models (LMs) have significant potential for clinical prediction tasks . however, unreliable decisions can result in significant costs due to compromised patient safety and ethical concerns . |
| Approach: | They propose to combine ensembling and multi-tasking approaches to reduce uncertainty in EHRs by using multi-tapping methods. |
| Outcome: | The proposed framework reduces model uncertainty in white-box and black-box settings, and improves model transparency in both settings. |
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. |
| Outcome: | The proposed taxonomy outperforms existing methods using only the gold dataset and silver datasets. |
Leveraging Medical Literature for Section Prediction in Electronic Health Records (D19-1)
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| Challenge: | Prior approaches to section prediction have only used text data from EHRs and required significant manual annotation. |
| Approach: | They propose to use sections from medical literature to train models to predict sections in EHRs. |
| Outcome: | The proposed model uses sections from medical literature that contain similar content to those found in EHR sections. |
Biomedical Concept Relatedness – A large EHR-based benchmark (2020.coling-main)
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| Challenge: | Existing biomedical concept relatedness datasets are notoriously small and consist of hand-picked concept pairs. |
| Approach: | They propose to use a concept relatedness benchmark to test the suitability of AI in healthcare . they find that it is six times larger than existing concepts relatedness datasets . |
| Outcome: | The proposed benchmark is six times larger than existing biomedical concept relatedness datasets and is relevant for the application of interest. |
RareSyn: Health Record Synthesis for Rare Disease Diagnosis (2025.emnlp-main)
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| 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. |
MediEval: A Unified Medical Benchmark for Patient-Contextual and Knowledge-Grounded Reasoning in LLMs (2026.acl-long)
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| Challenge: | Existing evaluations test factual medical knowledge in isolation or assess patient-level reasoning without verifying correctness, leaving a critical gap. |
| Approach: | They propose a benchmark that links MIMIC-IV EHRs to a unified knowledge base built from UMLS and other biomedical vocabularies. |
| Outcome: | The proposed model improves by +16.4 macro-F1 points over the base model and eliminates truth inversion errors. |
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. |
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. |
MedCPI: A Construct–Personalize–Integrate Framework for KG-enhanced Clinical Prediction (2026.findings-acl)
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| Challenge: | Existing KG-enhanced approaches to clinical prediction are limited . existing approaches to personalize and integrate knowledge are weakly controlled . |
| Approach: | They propose a framework to integrate medical knowledge graphs into EHRs to support KG-enhanced clinical prediction. |
| Outcome: | The proposed framework improves on MIMIC-III and MIMIC IV tasks. |
EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records (2024.emnlp-main)
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Wenqi Shi, Ran Xu, Yuchen Zhuang, Yue Yu, Jieyu Zhang, Hang Wu, Yuanda Zhu, Joyce Ho, Carl Yang, May Dongmei Wang
| Challenge: | EHRAgent enables clinicians to interact with EHRs using natural language . reliance on rule-based conversion systems often necessitates additional training or effort from data engineers. |
| Approach: | They propose a large language model agent that generates and executes code in natural language to facilitate clinicians in directly interacting with EHRs. |
| Outcome: | The proposed agent outperforms the strongest baseline by up to 29.6% in success rate on three real-world EHR datasets. |
RePrompT: Recurrent Prompt Tuning for Integrating Structured EHR Encoders with Large Language Models (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have shown promising results for mining EHRs . translating time-stamped sequences into plain text can obscure both temporal structure and code identities, weakening the ability to capture code co-occurrence and longitudinal regularities. |
| Approach: | They propose a time-aware LLM framework that integrates structured EHR encoders through prompt tuning without modifying underlying architectures. |
| Outcome: | Experiments on MIMIC-III and MIMIC IV show that RePrompT outperforms both EHR-based and LLM-based baselines across multiple clinical prediction tasks. |