# Automated Clinical Coding: What, Why, and Where We Are?

arXiv: https://arxiv.org/abs/2203.11092 · HTML: https://mlsanity.com/paper/2203.11092/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 109 references that are in the map.
- First posted on arXiv 21 Mar 2022.

## Builds on (most cited first, 26 of 26)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Explainable Prediction of Medical Codes from Clinical Text](https://mlsanity.com/paper/1802.05695/) (2018)
- [Multi-domain Clinical Natural Language Processing with MedCAT: the Medical Concept Annotation Toolkit](https://mlsanity.com/paper/2010.01165/) (2020)
- [Code Synonyms Do Matter: Multiple Synonyms Matching Network for Automatic ICD Coding](https://mlsanity.com/paper/2203.01515/) (2022)
- [An Empirical Study on Large-Scale Multi-Label Text Classification Including Few and Zero-Shot Labels](https://mlsanity.com/paper/2010.01653/) (2020)
- [Multimodal Machine Learning for Automated ICD Coding](https://mlsanity.com/paper/1810.13348/) (2018)
- [Knowledge-aware Zero-Shot Learning: Survey and Perspective](https://mlsanity.com/paper/2103.00070/) (2021)
- [Does the Magic of BERT Apply to Medical Code Assignment? A Quantitative Study](https://mlsanity.com/paper/2103.06511/) (2021)
- [Explainable Automated Coding of Clinical Notes using Hierarchical Label-wise Attention Networks and Label Embedding Initialisation](https://mlsanity.com/paper/2010.15728/) (2020)
- [Multi-Label Classification of Patient Notes a Case Study on ICD Code Assignment](https://mlsanity.com/paper/1709.09587/) (2017)
- [Large-scale Multi-label Text Classification - Revisiting Neural Networks](https://mlsanity.com/paper/1312.5419/) (2013)
- [A Unified Review of Deep Learning for Automated Medical Coding](https://mlsanity.com/paper/2201.02797/) (2022)
- [Read, Attend, and Code: Pushing the Limits of Medical Codes Prediction from Clinical Notes by Machines](https://mlsanity.com/paper/2107.10650/) (2021)
- [Experimental Evaluation and Development of a Silver-Standard for the MIMIC-III Clinical Coding Dataset](https://mlsanity.com/paper/2006.07332/) (2020)
- [Description-based Label Attention Classifier for Explainable ICD-9 Classification](https://mlsanity.com/paper/2109.12026/) (2021)
- [Estimating Redundancy in Clinical Text](https://mlsanity.com/paper/2105.11832/) (2021)
- [A Systematic Literature Review of Automated ICD Coding and Classification Systems using Discharge Summaries](https://mlsanity.com/paper/2107.10652/) (2021)
- [Multitask Balanced and Recalibrated Network for Medical Code Prediction](https://mlsanity.com/paper/2109.02418/) (2021)
- [CoPHE: A Count-Preserving Hierarchical Evaluation Metric in Large-Scale Multi-Label Text Classification](https://mlsanity.com/paper/2109.04853/) (2021)
- [Clinical Concept Extraction for Document-Level Coding](https://mlsanity.com/paper/1906.03380/) (2019)
- [MedCATTrainer: A Biomedical Free Text Annotation Interface with Active Learning and Research Use Case Specific Customisation](https://mlsanity.com/paper/1907.07322/) (2019)
- [Rare Disease Identification from Clinical Notes with Ontologies and Weak Supervision](https://mlsanity.com/paper/2105.01995/) (2021)
- [Dynamically Extracting Outcome-Specific Problem Lists from Clinical Notes with Guided Multi-Headed Attention](https://mlsanity.com/paper/2008.01197/) (2020)
- [Deployment of a Free-Text Analytics Platform at a UK National Health Service Research Hospital: CogStack at University College London Hospitals](https://mlsanity.com/paper/2108.06835/) (2021)
- [Active learning for medical code assignment](https://mlsanity.com/paper/2104.05741/) (2021)
- [Improving Predictions of Tail-end Labels using Concatenated BioMed-Transformers for Long Medical Documents](https://mlsanity.com/paper/2112.01718/) (2021)

## Built on by (most cited first, 30 of 33)

- [A Survey of Large Language Models in Medicine: Progress, Application, and Challenge](https://mlsanity.com/paper/2311.05112/) (2023)
- [Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study](https://mlsanity.com/paper/2304.10909/) (2023)
- [Exploring LLM Multi-Agents for ICD Coding](https://mlsanity.com/paper/2406.15363/) (2024)
- [A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises](https://mlsanity.com/paper/2306.04802/) (2023)
- [Can GPT-3.5 Generate and Code Discharge Summaries?](https://mlsanity.com/paper/2401.13512/) (2024)
- [Code Like Humans: A Multi-Agent Solution for Medical Coding](https://mlsanity.com/paper/2509.05378/) (2025)
- [Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities](https://mlsanity.com/paper/2309.17255/) (2023)
- [MAP: Evaluation and Multi-Agent Enhancement of Large Language Models for Inpatient Pathways](https://mlsanity.com/paper/2503.13205/) (2025)
- [Can Current Explainability Help Provide References in Clinical Notes to Support Humans Annotate Medical Codes?](https://mlsanity.com/paper/2210.15882/) (2022)
- [Abstraction Alignment: Comparing Model-Learned and Human-Encoded Conceptual Relationships](https://mlsanity.com/paper/2407.12543/) (2024)
- [Reinforcement Learning for Out-of-Distribution Reasoning in LLMs: An Empirical Study on Diagnosis-Related Group Coding](https://mlsanity.com/paper/2505.21908/) (2025)
- [Shimo Lab at "Discharge Me!": Discharge Summarization by Prompt-Driven Concatenation of Electronic Health Record Sections](https://mlsanity.com/paper/2406.18094/) (2024)
- [e-Health CSIRO at "Discharge Me!" 2024: Generating Discharge Summary Sections with Fine-tuned Language Models](https://mlsanity.com/paper/2407.02723/) (2024)
- [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)
- [Fine-tuning foundational models to code diagnoses from veterinary health records](https://mlsanity.com/paper/2410.15186/) (2024)
- [MedDCR: Learning to Design Agentic Workflows for Medical Coding](https://mlsanity.com/paper/2511.13361/) (2025)
- [Infusing clinical knowledge into tokenisers for language models](https://mlsanity.com/paper/2406.14312/) (2024)
- [A Comparative Study on Automatic Coding of Medical Letters with Explainability](https://mlsanity.com/paper/2407.13638/) (2024)
- [OntologyRAG: Better and Faster Biomedical Code Mapping with Retrieval-Augmented Generation (RAG) Leveraging Ontology Knowledge Graphs and Large Language Models](https://mlsanity.com/paper/2502.18992/) (2025)
- [Improving Text-based Early Prediction by Distillation from Privileged Time-Series Text](https://mlsanity.com/paper/2301.10887/) (2023)
- [Automatic identification of diagnosis from hospital discharge letters via weakly supervised Natural Language Processing](https://mlsanity.com/paper/2410.15051/) (2024)
- [Explainable ICD Coding via Entity Linking](https://mlsanity.com/paper/2503.20508/) (2025)
- [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)
- [Symphony for Medical Coding: A Next-Generation Agentic System for Scalable and Explainable Medical Coding](https://mlsanity.com/paper/2603.29709/) (2026)
- [Automatic Coding at Scale: Design and Deployment of a Nationwide System for Normalizing Referrals in the Chilean Public Healthcare System](https://mlsanity.com/paper/2307.05560/) (2023)
- [Improving ICD coding using Chapter based Named Entities and Attentional Models](https://mlsanity.com/paper/2407.17230/) (2024)
- [Can Modern NLP Systems Reliably Annotate Chest Radiography Exams? A Pre-Purchase Evaluation and Comparative Study of Solutions from AWS, Google, Azure, John Snow Labs, and Open-Source Models on an Independent Pediatric Dataset](https://mlsanity.com/paper/2505.23030/) (2025)
- [A Hierarchical Error Framework for Reliable Automated Coding in Communication Research: Applications to Health and Political Communication](https://mlsanity.com/paper/2509.24841/) (2025)
- [TraceCoder: Towards Traceable ICD Coding via Multi-Source Knowledge Integration](https://mlsanity.com/paper/2510.15267/) (2025)
- [Ontology-Aware Design Patterns for Clinical AI Systems: Translating Reification Theory into Software Architecture](https://mlsanity.com/paper/2604.01661/) (2026)

## Abstract (arXiv)

Clinical coding is the task of transforming medical information in a patient's health records into structured codes so that they can be used for statistical analysis. This is a cognitive and time-consuming task that follows a standard process in order to achieve a high level of consistency. Clinical coding could potentially be supported by an automated system to improve the efficiency and accuracy of the process. We introduce the idea of automated clinical coding and summarise its challenges from the perspective of Artificial Intelligence (AI) and Natural Language Processing (NLP), based on the literature, our project experience over the past two and half years (late 2019 - early 2022), and discussions with clinical coding experts in Scotland and the UK. Our research reveals the gaps between the current deep learning-based approach applied to clinical coding and the need for explainability and consistency in real-world practice. Knowledge-based methods that represent and reason the standard, explainable process of a task may need to be incorporated into deep learning-based methods for clinical coding. Automated clinical coding is a promising task for AI, despite the technical and organisational challenges. Coders are needed to be involved in the development process. There is much to achieve to develop and deploy an AI-based automated system to support coding in the next five years and beyond.