ArticleProceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence2023
Multi-label Few-shot ICD Coding as Autoregressive Generation with Prompt.
Article in Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The trial behind it
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Who cites it
7 citing papers in PubMed.
- CPS-Net (Collaborating Physicians in Silico Network): A Decentralized Multi-Agent Transformer Framework for Specialty-Aware Disease Prediction.Journal of medical systems · 2026Article
- Domain-Specific Pretraining of NorDeClin-Bidirectional Encoder Representations From Transformers forJMIR AI · 2025Article
- Developing an ICD-10 Coding Assistant: Pilot Study Using RoBERTa and GPT-4 for Term Extraction and Description-Based Code Selection.JMIR formative research · 2025Article
- ICDXML: enhancing ICD coding with probabilistic label trees and dynamic semantic representations.Scientific reports · 2024Article
- ODD: A Benchmark Dataset for the Natural Language Processing Based Opioid Related Aberrant Behavior Detection.Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting · 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
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Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Automatic International Classification of Diseases (ICD) coding aims to assign multiple ICD codes to a medical note with an average of 3,000+ tokens. This task is challenging due to the high-dimensional space of multi-label assignment (155,000+ ICD code candidates) and the long-tail challenge - Many ICD codes are infrequently assigned yet infrequent ICD codes are important clinically. This study addresses the long-tail challenge by transforming this multi-label classification task into an autoregressive generation task. Specifically, we first introduce a novel pretraining objective to generate free text diagnoses and procedures using the SOAP structure, the medical logic physicians use for note documentation. Second, instead of directly predicting the high dimensional space of ICD codes, our model generates the lower dimension of text descriptions, which then infers ICD codes. Third, we designed a novel prompt template for multi-label classification. We evaluate our Generation with Prompt (GP
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Registered trials
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.