| Challenge: | Electronic medical record (EMR) coding is the process of extracting diagnosis and procedure codes from the digital record (the EMR) pertaining to a patient's visit. |
| Approach: | They propose a neural network architecture that combines ideas from few-shot learning matching networks, multi-label loss functions, and convolutional neural networks for text classification to significantly outperform other state-of-the-art models. |
| Outcome: | The proposed model outperforms existing models on a well known de-identified EMR dataset with multi-label performance measures. |
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| 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. |
Scalable Wide and Deep Learning for Computer Assisted Coding (N18-3)
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Marilisa Amoia, Frank Diehl, Jesus Gimenez, Joel Pinto, Raphael Schumann, Fabian Stemmer, Paul Vozila, Yi Zhang
| Challenge: | In recent years the use of electronic medical records has accelerated resulting in large volumes of medical data when a patient visits a healthcare facility. |
| Approach: | They propose to use convolutional neural networks and logistic regression to build a machine learning based system for predicting ICD-10 codes from electronic medical records. |
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MedCodER: A Generative AI Assistant for Medical Coding (2025.naacl-industry)
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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. |
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Effective Convolutional Attention Network for Multi-label Clinical Document Classification (2021.emnlp-main)
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| Challenge: | a large number of medical encounters need to be coded everyday due to long document sets and large label set. |
| Approach: | They propose a convolutional attention network for multi-label document classification problem . they use convolution-based encoders and convolution networks to aggregate information across documents . |
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Less is More: Explainable and Efficient ICD Code Prediction with Clinical Entities (2025.acl-long)
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| 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. |
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Analyzing Code Embeddings for Coding Clinical Narratives (2021.findings-acl)
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| 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. |
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Graph Enhanced Contrastive Learning for Radiology Findings Summarization (2022.acl-long)
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| Challenge: | Existing methods for automating impression generation have limited the relationship between extra knowledge and the original findings. |
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Bridging the Code Gap: A Joint Learning Framework across Medical Coding Systems (2024.lrec-main)
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| Challenge: | Existing methods for automating medical coding focus on a single coding system . however, there are still challenges to overcome in coding. |
| Approach: | They propose a joint learning framework for Across Medical coding systems which jointly learns different coding system through multi-task learning. |
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Code Synonyms Do Matter: Multiple Synonyms Matching Network for Automatic ICD Coding (2022.acl-short)
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| 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. |
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. |