mlsanityData to 7 Oct 2026

A Systematic Literature Review of Automated ICD Coding and Classification Systems using Discharge Summaries

arXiv 2107.10652 ↗First posted 12 Jul 2021cs.CL cs.LGcited by 10

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Abstract (arXiv)

Codification of free-text clinical narratives have long been recognised to be beneficial for secondary uses such as funding, insurance claim processing and research. The current scenario of assigning codes is a manual process which is very expensive, time-consuming and error prone. In recent years, many researchers have studied the use of Natural Language Processing (NLP), related Machine Learning (ML) and Deep Learning (DL) methods and techniques to resolve the problem of manual coding of clinical narratives and to assist human coders to assign clinical codes more accurately and efficiently. This systematic literature review provides a comprehensive overview of automated clinical coding systems that utilises appropriate NLP, ML and DL methods and techniques to assign ICD codes to discharge summaries. We have followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA) guidelines and conducted a comprehensive search of publications from January, 2010 to December 2020 in four academic databases- PubMed, ScienceDirect, Association for Computing Machinery(ACM) Digital Library, and the Association for Computational Linguistics(ACL) Anthology. We reviewed 7,556 publications; 38 met the inclusion criteria. This review identified: datasets having discharge summaries; NLP techniques along with some other data extraction processes, different feature extraction and embedding techniques. To measure the performance of classification methods, different evaluation metrics are used. Lastly, future research directions are provided to scholars who are interested in automated ICD code assignment. Efforts are still required to improve ICD code prediction accuracy, availability of large-scale de-identified clinical corpora with the latest version of the classification system. This can be a platform to guide and share knowledge with the less experienced coders and researchers.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 24 of its 100 references that are in the map. First posted on arXiv 12 Jul 2021.

Builds on

24 of 100 references.

References · 24

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12 more
  1. 2017B-CNN: Branch Convolutional Neural Network for Hierarchical Classificationcited by 37
  2. 2020BERT-XML: Large Scale Automated ICD Coding Using BERT Pretrainingcited by 37
  3. 2017Towards Automated ICD Coding Using Deep Learningcited by 35
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  6. 2020Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Textcited by 21
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Citing papers per year: 2021: 1; 2022: 3; 2023: 1; 2024: 4; 2025: 1.

10 papers.

  1. 2022Automated Clinical Coding: What, Why, and Where We Are?cited by 33
  2. 2022A Unified Review of Deep Learning for Automated Medical Codingcited by 26
  3. 2024Exploring LLM Multi-Agents for ICD Codingcited by 14
  4. 2024Generalist embedding models are better at short-context clinical semantic search than specialized embedding modelscited by 7
  5. 2024Beyond Label Attention: Transparency in Language Models for Automated Medical Coding via Dictionary Learningcited by 5
  6. 2023Multi-Modal Perceiver Language Model for Outcome Prediction in Emergency Departmentcited by 1
  7. 2021Secondary Use of Clinical Problem List Entries for Neural Network-Based Disease Code Assignmentcited by 0
  8. 2022Semi-self-supervised Automated ICD Codingcited by 0
  9. 2024Improving ICD coding using Chapter based Named Entities and Attentional Modelscited by 0
  10. 2025COPE: Chain-Of-Thought Prediction Engine for Open-Source Large Language Model Based Stroke Outcome Prediction from Clinical Notescited by 0