ArticleJMIR medical informatics2021
Automatic ICD-10 Coding and Training System: Deep Neural Network Based on Supervised Learning.
Article in JMIR medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Validity and reliability International Classification of Diseases-10 codes for all forms of injury: A systematic review.PloS one · 2024Pooled it
- Artificial Intelligence to Improve Clinical Coding Practice in Scandinavia: Crossover Randomized Controlled Trial.Journal of medical Internet research · 2025Trial
- Underutilization of syndrome-specific ICD-10 codes for genetic epilepsies: Implications for precision medicine.Epilepsia · 2026Article
- Classifying American Society of Anesthesiologists Physical Status With a Low-Rank-Adapted Large Language Model: Development and Validation Study.Journal of medical Internet research · 2026Article
- Evaluating real-world deployment of an HL7-CDA-aligned LLM for ICD-10-CM coding.NPJ digital medicine · 2026Article
- Leveraging AI solutions for sustainable practice in pediatric radiology: a practical guide and an educational tool.Pediatric radiology · 2026Review
- Scalable and Privacy-Conscious End-to-End Processing of Large-Scale Clinical Data for Precision Medicine: Empirical Evaluation Study.JMIR medical informatics · 2026Article
- Annotating and indexing scientific articles with rare diseases.Journal of biomedical semantics · 2026Article
- Automated data collection in cancer care: State of play among registries in the United Kingdom and Europe.Health information management : journal of the Health Information Management Association of Australia · 2026Article
- A Machine Learning Approach for Identifying People With Neuroinfectious Diseases in Electronic Health Records: Algorithm Development and Validation.JMIR medical informatics · 2025Article
- Domain-Specific Pretraining of NorDeClin-Bidirectional Encoder Representations From Transformers forJMIR AI · 2025Article
- Application of Clinical Department-Specific AI-Assisted Coding Using Taiwan Diagnosis-Related Groups: Retrospective Validation Study.JMIR human factors · 2025Article
- Article
- Large language models for disease diagnosis: a scoping review.NPJ artificial intelligence · 2025Review
- Optimising the paradigms of human AI collaborative clinical coding.NPJ digital medicine · 2024Article
- Digitalising the past decades: automated ICD-10 coding of unstructured free text dermatological diagnoses.BMC health services research · 2024Article
- Automating surgical procedure extraction for society of surgeons adult cardiac surgery registry using pretrained language models.JAMIA open · 2024Article
- Evaluating a Natural Language Processing-Driven, AI-Assisted International Classification of Diseases, 10th Revision, Clinical Modification, Coding System for Diagnosis Related Groups in a Real Hospital Environment: Algorithm Development and Validation Study.Journal of medical Internet research · 2024Article
- Using natural language processing for automated classification of disease and to identify misclassified ICD codes in cardiac disease.European heart journal. Digital health · 2024Article
- Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe International Classification of Diseases (ICD) code is widely used as the reference in medical system and billing purposes. However, classifying diseases into ICD codes still mainly relies on humans reading a large amount of written material as the basis for coding. Coding is both laborious and time-consuming. Since the conversion of ICD-9 to ICD-10, the coding task became much more complicated, and deep learning- and natural language processing-related approaches have been studied to assist disease coders.
objectiveThis paper aims at constructing a deep learning model for ICD-10 coding, where the model is meant to automatically determine the corresponding diagnosis and procedure codes based solely on free-text medical notes to improve accuracy and reduce human effort.
methodsWe used diagnosis records of the National Taiwan University Hospital as resources and apply natural language processing techniques, including global vectors, word to vectors, embeddings from language models, bidirectional encoder representations from transformers, and single head attention recurrent neural network, on the deep neural network architecture to implement ICD-10 auto-coding. Besides, we introduced the attention mechanism into the classification model to extract the keywords from diagnoses and visualize the coding reference for training freshmen in ICD-10. Sixty discharge notes were randomly selected to examine the change in the F
resultsIn experiments on the medical data set of National Taiwan University Hospital, our prediction results revealed F
conclusionsThe proposed model significantly improved the F
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.