mlsanityData to 7 Oct 2026

Description-based Label Attention Classifier for Explainable ICD-9 Classification

arXiv 2109.12026 ↗First posted 24 Sep 2021cs.LG cs.IRcited by 12

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Abstract (arXiv)

ICD-9 coding is a relevant clinical billing task, where unstructured texts with information about a patient's diagnosis and treatments are annotated with multiple ICD-9 codes. Automated ICD-9 coding is an active research field, where CNN- and RNN-based model architectures represent the state-of-the-art approaches. In this work, we propose a description-based label attention classifier to improve the model explainability when dealing with noisy texts like clinical notes. We evaluate our proposed method with different transformer-based encoders on the MIMIC-III-50 dataset. Our method achieves strong results together with augmented explainablilty.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 12 of its 16 references that are in the map. First posted on arXiv 24 Sep 2021.

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12 of 16 references.

References · 12

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  4. 2020Longformer: The Long-Document Transformercited by 3,362
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  7. 2018Explainable Prediction of Medical Codes from Clinical Textcited by 231
  8. 2019Hierarchical Transformers for Long Document Classificationcited by 103
  9. 2020A Label Attention Model for ICD Coding from Clinical Textcited by 47
  10. 2021Does the Magic of BERT Apply to Medical Code Assignment? A Quantitative Studycited by 39
  11. 2021Towards BERT-based Automatic ICD Coding: Limitations and Opportunitiescited by 25
  12. 2021TransICD: Transformer Based Code-wise Attention Model for Explainable ICD Codingcited by 13

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Cited by year

Citing papers per year: 2022: 5; 2023: 3; 2024: 2; 2025: 2.

12 papers.

  1. 2023Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Studycited by 48
  2. 2022Automated Clinical Coding: What, Why, and Where We Are?cited by 33
  3. 2022A Unified Review of Deep Learning for Automated Medical Codingcited by 26
  4. 2022Hierarchical Label-wise Attention Transformer Model for Explainable ICD Codingcited by 13
  5. 2024An Unsupervised Approach to Achieve Supervised-Level Explainability in Healthcare Recordscited by 12
  6. 2022Automated ICD Coding using Extreme Multi-label Long Text Transformer-based Modelscited by 5
  7. 2024Aligning AI Research with the Needs of Clinical Coding Workflows: Eight Recommendations Based on US Data Analysis and Critical Reviewcited by 5
  8. 2022Entity Anchored ICD Codingcited by 3
  9. 2023MHLAT: Multi-hop Label-wise Attention Model for Automatic ICD Codingcited by 1
  10. 2023Automated Clinical Coding for Outpatient Departmentscited by 1
  11. 2025Decoding the Multimodal Maze: A Systematic Review on the Adoption of Explainability in Multimodal Attention-based Modelscited by 0
  12. 2025Structured Information Matters: Explainable ICD Coding with Patient-Level Knowledge Graphscited by 0