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A Unified Review of Deep Learning for Automated Medical Coding

arXiv 2201.02797 ↗First posted 8 Jan 2022cs.CL cs.IRcited by 26

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Abstract (arXiv)

Automated medical coding, an essential task for healthcare operation and delivery, makes unstructured data manageable by predicting medical codes from clinical documents. Recent advances in deep learning and natural language processing have been widely applied to this task. However, deep learning-based medical coding lacks a unified view of the design of neural network architectures. This review proposes a unified framework to provide a general understanding of the building blocks of medical coding models and summarizes recent advanced models under the proposed framework. Our unified framework decomposes medical coding into four main components, i.e., encoder modules for text feature extraction, mechanisms for building deep encoder architectures, decoder modules for transforming hidden representations into medical codes, and the usage of auxiliary information. Finally, we introduce the benchmarks and real-world usage and discuss key research challenges and future directions.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 66 of its 240 references that are in the map. First posted on arXiv 8 Jan 2022.

Builds on

66 of 240 references.

References · 66

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Citing papers per year: 2022: 2; 2023: 6; 2024: 3; 2025: 7; 2026: 8.

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  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. 2023MDACE: MIMIC Documents Annotated with Code Evidencecited by 19
  4. 2023Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunitiescited by 10
  5. 2024MedCodER: A Generative AI Assistant for Medical Codingcited by 10
  6. 2023DKEC: Domain Knowledge Enhanced Multi-Label Classification for Diagnosis Predictioncited by 8
  7. 2025A General Knowledge Injection Framework for ICD Codingcited by 7
  8. 2025The Anatomy of Evidence: An Investigation Into Explainable ICD Codingcited by 3
  9. 2025MedDCR: Learning to Design Agentic Workflows for Medical Codingcited by 3
  10. 2022An Automatic ICD Coding Network Using Partition-Based Label Attentioncited by 2
  11. 2023Exploring the Consistency, Quality and Challenges in Manual and Automated Coding of Free-text Diagnoses from Hospital Outpatient Letterscited by 2
  12. 2024SoK: Security and Privacy Risks of Healthcare AIcited by 2
14 more
  1. 2024Similarity-Dissimilarity Loss for Multi-label Supervised Contrastive Learningcited by 2
  2. 2023Content Reduction, Surprisal and Information Density Estimation for Long Documentscited by 1
  3. 2026A medical coding language model trained on clinical narratives from a population-wide cohort of 1.8 million patientscited by 1
  4. 2025Task as Context Prompting for Accurate Medical Symptom Coding Using Large Language Modelscited by 0
  5. 2025Structured Information Matters: Explainable ICD Coding with Patient-Level Knowledge Graphscited by 0
  6. 2025LTR-ICD: A Ranking-Aware Framework for Automatic ICD Codingcited by 0
  7. 2025Taming the Real-world Complexities in CPT E/M Coding with Large Language Modelscited by 0
  8. 2026Training a Large Language Model for Medical Coding Using Privacy-Preserving Synthetic Clinical Datacited by 0
  9. 2026Bridging the Version Gap: Multi-version Training Improves ICD Code Prediction, Especially for Rare Codescited by 0
  10. 2026Systematic Evaluation of the Quality of Synthetic Clinical Notes Rephrased by LLMs at Million-Note Scalecited by 0
  11. 2026Reliable Multilingual Orthopedic Decision Support from Clinical Narratives: Language-Aware Adaptation and Verification-Guided Deferralcited by 0
  12. 2026Can Post-Training Turn LLMs into Good Medical Coders? An Empirical Study of Generative ICD Codingcited by 0
  13. 2026CoLa-ICD: A Knowledge-Enhanced Framework for Long-Tail Automated Medical Codingcited by 0
  14. 2026CRISS: A Retrieval-Augmented AI Chatbot for Assisting Cancer Registrarscited by 0