# A Unified Review of Deep Learning for Automated Medical Coding

arXiv: https://arxiv.org/abs/2201.02797 · HTML: https://mlsanity.com/paper/2201.02797/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 66 of its 240 references that are in the map.
- First posted on arXiv 8 Jan 2022.

## Builds on (most cited first, 30 of 66)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [Big Bird: Transformers for Longer Sequences](https://mlsanity.com/paper/2007.14062/) (2020)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission](https://mlsanity.com/paper/1904.05342/) (2019)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [Autoregressive Entity Retrieval](https://mlsanity.com/paper/2010.00904/) (2020)
- [A Structured Self-attentive Sentence Embedding](https://mlsanity.com/paper/1703.03130/) (2017)
- [Explainable Prediction of Medical Codes from Clinical Text](https://mlsanity.com/paper/1802.05695/) (2018)
- [Doctor AI: Predicting Clinical Events via Recurrent Neural Networks](https://mlsanity.com/paper/1511.05942/) (2015)
- [How Many Data Points is a Prompt Worth?](https://mlsanity.com/paper/2103.08493/) (2021)
- [MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare](https://mlsanity.com/paper/2110.15621/) (2021)
- [SGM: Sequence Generation Model for Multi-label Classification](https://mlsanity.com/paper/1806.04822/) (2018)
- [ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network](https://mlsanity.com/paper/1912.00862/) (2019)
- [Joint Embedding of Words and Labels for Text Classification](https://mlsanity.com/paper/1805.04174/) (2018)
- [Multi-domain Clinical Natural Language Processing with MedCAT: the Medical Concept Annotation Toolkit](https://mlsanity.com/paper/2010.01165/) (2020)
- [OWL2Vec*: Embedding of OWL Ontologies](https://mlsanity.com/paper/2009.14654/) (2020)

## Built on by (most cited first, 26 of 26)

- [Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study](https://mlsanity.com/paper/2304.10909/) (2023)
- [Automated Clinical Coding: What, Why, and Where We Are?](https://mlsanity.com/paper/2203.11092/) (2022)
- [MDACE: MIMIC Documents Annotated with Code Evidence](https://mlsanity.com/paper/2307.03859/) (2023)
- [MedCodER: A Generative AI Assistant for Medical Coding](https://mlsanity.com/paper/2409.15368/) (2024)
- [Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities](https://mlsanity.com/paper/2309.17255/) (2023)
- [DKEC: Domain Knowledge Enhanced Multi-Label Classification for Diagnosis Prediction](https://mlsanity.com/paper/2310.07059/) (2023)
- [A General Knowledge Injection Framework for ICD Coding](https://mlsanity.com/paper/2505.18708/) (2025)
- [Exploring the Consistency, Quality and Challenges in Manual and Automated Coding of Free-text Diagnoses from Hospital Outpatient Letters](https://mlsanity.com/paper/2311.10856/) (2023)
- [The Anatomy of Evidence: An Investigation Into Explainable ICD Coding](https://mlsanity.com/paper/2507.01802/) (2025)
- [MedDCR: Learning to Design Agentic Workflows for Medical Coding](https://mlsanity.com/paper/2511.13361/) (2025)
- [An Automatic ICD Coding Network Using Partition-Based Label Attention](https://mlsanity.com/paper/2211.08429/) (2022)
- [SoK: Security and Privacy Risks of Healthcare AI](https://mlsanity.com/paper/2409.07415/) (2024)
- [Similarity-Dissimilarity Loss for Multi-label Supervised Contrastive Learning](https://mlsanity.com/paper/2410.13439/) (2024)
- [Content Reduction, Surprisal and Information Density Estimation for Long Documents](https://mlsanity.com/paper/2309.06009/) (2023)
- [A medical coding language model trained on clinical narratives from a population-wide cohort of 1.8 million patients](https://mlsanity.com/paper/2603.00221/) (2026)
- [Can Post-Training Turn LLMs into Good Medical Coders? An Empirical Study of Generative ICD Coding](https://mlsanity.com/paper/2606.13940/) (2026)
- [Task as Context Prompting for Accurate Medical Symptom Coding Using Large Language Models](https://mlsanity.com/paper/2504.03051/) (2025)
- [Structured Information Matters: Explainable ICD Coding with Patient-Level Knowledge Graphs](https://mlsanity.com/paper/2509.09699/) (2025)
- [LTR-ICD: A Ranking-Aware Framework for Automatic ICD Coding](https://mlsanity.com/paper/2510.13922/) (2025)
- [Taming the Real-world Complexities in CPT E/M Coding with Large Language Models](https://mlsanity.com/paper/2510.25007/) (2025)
- [Training a Large Language Model for Medical Coding Using Privacy-Preserving Synthetic Clinical Data](https://mlsanity.com/paper/2603.23515/) (2026)
- [Bridging the Version Gap: Multi-version Training Improves ICD Code Prediction, Especially for Rare Codes](https://mlsanity.com/paper/2605.17755/) (2026)
- [Systematic Evaluation of the Quality of Synthetic Clinical Notes Rephrased by LLMs at Million-Note Scale](https://mlsanity.com/paper/2605.17775/) (2026)
- [Reliable Multilingual Orthopedic Decision Support from Clinical Narratives: Language-Aware Adaptation and Verification-Guided Deferral](https://mlsanity.com/paper/2605.31512/) (2026)
- [CoLa-ICD: A Knowledge-Enhanced Framework for Long-Tail Automated Medical Coding](https://mlsanity.com/paper/2608.30234/) (2026)
- [CRISS: A Retrieval-Augmented AI Chatbot for Assisting Cancer Registrars](https://mlsanity.com/paper/2609.29075/) (2026)

## 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.