Papers with ICD
INSIGHTBUDDY-AI: Medication Extraction and Entity Linking using Pre-Trained Language Models and Ensemble Learning (2025.naacl-srw)
Copied to clipboard
| Challenge: | InsightBuddy-AI is a system for extracting medication mentions and their associated attributes. |
| Approach: | They propose a system for extracting medication mentions and their associated attributes . they use stacked and voting ensembles built upon pre-trained language models . |
| Outcome: | The proposed system outperforms fine-tuned models in the extraction of medication mentions and associated attributes. |
AnEMIC: A Framework for Benchmarking ICD Coding Models (2022.emnlp-demos)
Copied to clipboard
| Challenge: | Diagnostic coding is the task of assigning diagnosis codes defined by the ICD (International Classification of Diseases) standard to patient visits based on clinical notes. |
| Approach: | They propose to use an ICD coding framework to train and benchmark models . they correct errors in preprocessing and provide an interactive demo to analyze the models based on custom inputs. |
| Outcome: | The framework corrects errors in preprocessing and provides key models and weights trained on correctly preprocessed datasets. |
Zero-Shot ATC Coding with Large Language Models for Clinical Assessments (2025.naacl-industry)
Copied to clipboard
| Challenge: | Manual assignment of ATC codes to prescription records is a significant bottleneck in healthcare operations. |
| Approach: | They propose a method to automate the assignment of ATC codes to prescription records . they use locally deployable large language models to guide LLMs through the ontology . |
| Outcome: | The proposed method achieves 78% exact match accuracy with GPT-4o and 60% with Llama 3.1 70B. |
JointCoder: Exploring Automated ICD Coding on Real-World Chinese EHRs with a Multi-Agent Framework (2026.acl-demo)
Copied to clipboard
| 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. |
Clinical-Coder: Assigning Interpretable ICD-10 Codes to Chinese Clinical Notes (2020.acl-demos)
Copied to clipboard
| Challenge: | Existing methods of automatic coding prediction have been successful, but the interpretability of predicted codes is a challenge. |
| Approach: | They propose an online system that can predict ICD codes for Chinese clinical notes by using a Dilated Convolutional Attention network with N-gram Matching mechanism. |
| Outcome: | The proposed system is able to provide supporting information in clinical decision making. |
MedCodER: A Generative AI Assistant for Medical Coding (2025.naacl-industry)
Copied to clipboard
Krishanu Das Baksi, Elijah Soba, John J Higgins, Ravi Saini, Jaden Wood, Jane Cook, Jack I Scott, Nirmala Pudota, Tim Weninger, Edward Bowen, Sanmitra Bhattacharya
| Challenge: | Medical coding is time-consuming and error-prone due to large label space, lengthy text inputs, and the absence of supporting evidence annotations. |
| Approach: | They propose a Generative AI framework for automatic medical coding that leverages extraction, retrieval, and re-ranking techniques as core components. |
| Outcome: | The proposed framework outperforms existing methods on the International Classification of Diseases (ICD) code prediction scale. |
Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration (2021.eacl-main)
Copied to clipboard
Betty van Aken, Jens-Michalis Papaioannou, Manuel Mayrdorfer, Klemens Budde, Felix Gers, Alexander Loeser
| Challenge: | Clinical decision support systems can help in situations where the patient's development is predicted based on textual data. |
| Approach: | They propose to use clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources into the models. |
| Outcome: | The proposed model improves performance against several baselines and demonstrates that it is transferable and can be used in clinical decision support systems. |
Code Synonyms Do Matter: Multiple Synonyms Matching Network for Automatic ICD Coding (2022.acl-short)
Copied to clipboard
| Challenge: | Existing methods for automatic ICD coding use label attention to match related text snippets. |
| Approach: | They propose to use code synonyms to leverage for better code representation learning. |
| Outcome: | The proposed method outperforms previous state-of-the-art methods on the MIMIC-III dataset. |
A Neural Architecture for Automated ICD Coding (P18-1)
Copied to clipboard
| Challenge: | Medical coding is time-consuming, expensive, and error prone. |
| Approach: | They propose to use diagnosis descriptions (DDs) of a patient as inputs to select the most relevant ICD codes. |
| Outcome: | The proposed algorithms perform on a clinical dataset with 59K patient visits. |
Modelling Temporal Document Sequences for Clinical ICD Coding (2023.eacl-main)
Copied to clipboard
| Challenge: | Existing studies on the ICD coding task focus on extracting codes from the discharge summary, but there is potential to automate the task by identifying relevant information from clinical notes. |
| Approach: | They propose a hierarchical transformer architecture that uses text across the entire sequence of clinical notes in each hospital stay for ICD coding. |
| Outcome: | The proposed model exceeds the state-of-the-art when using only discharge summaries as input and achieves performance improvements when all clinical notes are used as input. |
Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Coding (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for ICD coding are limited due to the high-dimensional space of multi-label assignment and the long-tail challenge. |
| Approach: | They propose a prompt-based fine-tuning technique with label semantics to solve this challenge. |
| Outcome: | The proposed method outperforms state-of-the-art methods on a benchmark dataset of code assignment in 14.5% of cases. |
RuCCoD: Towards Automated ICD Coding in Russian (2025.emnlp-main)
Copied to clipboard
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. |
Accurate and Well-Calibrated ICD Code Assignment Through Attention Over Diverse Label Embeddings (2024.eacl-long)
Copied to clipboard
| Challenge: | Existing approaches to assigning ICD codes to clinical text are time-consuming, labor intensive, and error-prone. |
| Approach: | They propose to adapt a Transformer-based model to a longformer model and use it to encode clinical narratives. |
| Outcome: | The proposed approach outperforms current state-of-the-art models in ICD coding with the label embeddings contributing to the good performance. |
Auxiliary Knowledge-Induced Learning for Automatic Multi-Label Medical Document Classification (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for ICD indexing use machine learning to assign subset of codes to medical records . experimental results show proposed method achieves state-of-the-art performance on a number of measures. |
| Approach: | They propose a method that uses a deep dilated residual convolution encoder to learn document representations across different lengths of the texts. |
| Outcome: | The proposed method achieves state-of-the-art performance on a number of measures. |
Fusion: Towards Automated ICD Coding via Feature Compression (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to assign ICD codes from unstructured clinical notes are noisy and prone to errors. |
| Approach: | They propose a feature compressed ICD coding model called Fusion to address this problem. |
| Outcome: | The proposed model outperforms existing models on two widely used datasets. |
Travel on the ICD Tree: Benchmarking Agentic Reasoning for ICD Coding from Chinese Electronic Medical Records (2026.findings-acl)
Copied to clipboard
| Challenge: | Accurate International Classification of Diseases (ICD) coding is crucial for hospital management and healthcare data governance. |
| Approach: | They propose a framework to evaluate ICD coding based on complete EMRs . they use a dataset of 560 real clinical records covering 434 common diseases . |
| Outcome: | The proposed framework explores the capability boundaries of large language models under different paradigms. |
Evaluation and LLM-Guided Learning of ICD Coding Rationales (2026.eacl-long)
Copied to clipboard
| 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. |
Automatic ICD Coding Exploiting Discourse Structure and Reconciled Code Embeddings (2022.coling-1)
Copied to clipboard
| Challenge: | Existing studies did not exploit the discourse structure of clinical notes, which provides rich contextual information for code assignment. |
| Approach: | They propose to leverage section type classification and section type embeddings to exploit the discourse structure of clinical notes to generate rich contextual information for code assignment. |
| Outcome: | The proposed model outperforms state-of-the-art models on a MIMIC dataset by a large margin. |
TreeMAN: Tree-enhanced Multimodal Attention Network for ICD Coding (2022.coling-1)
Copied to clipboard
| 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. |
Multi-stage Retrieve and Re-rank Model for Automatic Medical Coding Recommendation (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing methods for ICD indexing have a heavy label distribution and a manual process . Xie and Xing (2017) propose a new approach to ICD re-ranking . |
| Approach: | They propose a "retrieve and re-rank" framework to allocate subsets of ICD codes to medical records . they leverage auxiliary knowledge of the electronic health records (EHR) and a discrete retrieval method . |
| Outcome: | The proposed method achieves state-of-the-art performance on the MIMIC-III benchmark. |
HyperCore: Hyperbolic and Co-graph Representation for Automatic ICD Coding (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for ICD coding ignore Code Hierarchy and Code Co-occurrence . cost of manual coding estimated to be $25 billion per year in the US . |
| Approach: | They propose a hyperbolic representation method to leverage the code hierarchy and a graph convolutional network to utilize the code co-occurrence. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two widely used datasets. |
A Two-Stage Decoder for Efficient ICD Coding (2023.findings-acl)
Copied to clipboard
| Challenge: | Recent automated ICD coding efforts improve performance by encoding medical notes and codes with additional data and knowledge bases. |
| Approach: | They propose a two-stage decoding mechanism to predict ICD codes using hierarchical properties of the codes to split the prediction into two steps: at first, predict the parent code and then predict the child code based on the previous prediction. |
| Outcome: | Experiments on the public MIMIC-III data show that the proposed model performs well in single-model settings without external data or knowledge. |
SafeDecoding: Defending against Jailbreak Attacks via Safety-Aware Decoding (2024.acl-long)
Copied to clipboard
| Challenge: | Despite advances in large language models, they face substantial challenges in terms of safety. |
| Approach: | They develop a safety-aware decoding strategy for large language models to defend against jailbreak attacks. |
| Outcome: | The proposed strategy outperforms six defense methods against jailbreak attacks on five LLMs. |
Modeling Diagnostic Label Correlation for Automatic ICD Coding (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing work built a binary prediction for each label independently, ignoring the dependencies between labels. |
| Approach: | They propose a framework to capture the label correlation and train a reranking estimator to rescore the probability of each label set candidate generated by a base predictor. |
| Outcome: | The proposed framework improves on the best-performing predictors on MIMIC datasets. |
CoRelation: Boosting Automatic ICD Coding through Contextualized Code Relation Learning (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for boosting ICD coding performance lack a model for complex code relations . current methods overlook the importance of context in clinical notes . |
| Approach: | They propose a contextualized and flexible framework to enhance learning of ICD code relations . they use clinical notes to model all possible code relations using a dependent learning paradigm . |
| Outcome: | The proposed approach improves on six public ICD coding datasets compared to state-of-the-art models. |
A General Knowledge Injection Framework for ICD Coding (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to improve ICD coding focus on a single type of knowledge and design specialized modules that are complex and incompatible with each other. |
| Approach: | They propose a general knowledge injection framework that integrates three key types of knowledge without specialized design of additional modules. |
| Outcome: | The proposed framework outperforms baseline models and is comparable to models relying on extra human annotations. |
Data Drift in Clinical Outcome Prediction from Admission Notes (2024.lrec-main)
Copied to clipboard
Paul Grundmann, Jens-Michalis Papaioannou, Tom Oberhauser, Thomas Steffek, Amy Siu, Wolfgang Nejdl, Alexander Loeser
| Challenge: | a pivotal dataset for clinical NLP research was released in 2016 . public access to such datasets is limited due to privacy and ethical concerns . |
| Approach: | They propose a novel clinical outcome prediction dataset based on MIMIC-IV . they provide initial insights into the performance of models trained on MIDIC-III . |
| Outcome: | The proposed dataset aims to probe the robustness and generalization of clinical outcome prediction models . the study focuses on challenges tied to evolving documentation standards and changing codes in the ICD taxonomy . |
Analyzing Code Embeddings for Coding Clinical Narratives (2021.findings-acl)
Copied to clipboard
| Challenge: | Recent work on automated ICD coding learn mappings between low-dimensional representations of clinical text reports and codes. |
| Approach: | They propose novel neural networks for encoding medical codes based on textual, structural and statistical characteristics using a single deep learning baseline model. |
| Outcome: | The proposed methods improve the accuracy of medical codes based on their textual, structural and statistical characteristics. |
Alleviating Hallucinations of Large Language Models through Induced Hallucinations (2025.findings-naacl)
Copied to clipboard
| Challenge: | Existing studies have shown that large language models generate inaccurate or fabricated information, a phenomenon known as hallucinations. |
| Approach: | They propose a simple strategy to induce-then-contrast decode LLMs to enhance their factuality . they first induce hallucinations from the original model and penalize them . |
| Outcome: | The proposed strategy improves factuality of large language models across task formats, model sizes, and model families. |
Automatic ICD Coding via Interactive Shared Representation Networks with Self-distillation Mechanism (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods for ICD coding ignore the long-tail of code frequency or noisy clinical notes. |
| Approach: | They propose to use an interactive shared representation network to model code co-occurrences while focusing on the clinical note's noteworthy part and extract valuable information through a self-distillation learning mechanism to solve the long-tail problem. |
| Outcome: | The proposed model reduces the long-tail of code frequency and noise in clinical notes and extracts valuable information through a self-distillation learning mechanism. |
ICDAGENT: Empowering Agentic Large Language Models for Explainable Medical Coding (2026.acl-long)
Copied to clipboard
| Challenge: | Existing models lack convincing, human-understandable explanations, making them difficult for physicians to trust and use in practice. |
| Approach: | They propose a framework that aims to automatically assign ICD codes to clinical notes while providing explicit justifications for each assignment. |
| Outcome: | The proposed framework achieves effective ICD coding with accurate explanations using two collaborative LLM agents: a coding agent and a critical agent. |
Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent research in large vision-language models has shown promising results, but the issue of hallucination remains. |
| Approach: | They propose an instruction-based method to reduce hallucinations in large vision-language models . they use disturbance instructions to exacerbate hallucinosity in multimodal fusion modules . |
| Outcome: | The proposed method reduces hallucinations in multimodal fusion modules by reducing alignment uncertainty and subtracting hallucines from the original distribution. |
Less is More: Explainable and Efficient ICD Code Prediction with Clinical Entities (2025.acl-long)
Copied to clipboard
| Challenge: | Clinical coding is labor-intensive and prone to delays, leading to global backlogs. |
| Approach: | They propose an approach that combines Named Entity Recognition (NER) and Assertion Classification (AC) to filter for clinically important content before supervised code prediction. |
| Outcome: | The proposed approach reduces training time by over half on a standard evaluation dataset compared to current methods . it uses Named Entity Recognition (NER) and Assertion Classification (AC) to filter for clinically important content before supervised code prediction. |