ArticleProceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing2022
Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Coding.
Article in Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Keyword-optimized template insertion for clinical note classification via prompt-based learning.BMC medical informatics and decision making · 2025Article
- Knowledge-point classification using simple LSTM-based and siamese-based networks for virtual patient simulation.BMC medical informatics and decision making · 2025Article
- Ensemble neural models for ICD code prediction using unstructured and structured healthcare data.Heliyon · 2024Article
- 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
- A two-stream deep model for automated ICD-9 code prediction in an intensive care unit.Heliyon · 2024Article
- Multi-label Few-shot ICD Coding as Autoregressive Generation with Prompt.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
Automatic International Classification of Diseases (ICD) coding aims to assign multiple ICD codes to a medical note with average length of 3,000+ tokens. This task is challenging due to a high-dimensional space of multi-label assignment (tens of thousands of ICD codes) and the long-tail challenge: only a few codes (common diseases) are frequently assigned while most codes (rare diseases) are infrequently assigned. This study addresses the long-tail challenge by adapting a prompt-based fine-tuning technique with label semantics, which has been shown to be effective under few-shot setting. To further enhance the performance in medical domain, we propose a knowledge-enhanced longformer by injecting three domain-specific knowledge: hierarchy, synonym, and abbreviation with additional pretraining using contrastive learning. Experiments on MIMIC-III-full, a benchmark dataset of code assignment, show that our proposed method outperforms previous state-of-the-art method in 14.5% in marco F1 (from 10.3 to 11.8, P<0.001). To further test our model on few-shot setting, we created a new rare diseases coding dataset, MIMIC-III-rare50, on which our model improves marco F1 from 17.1 to 30.4 and micro F1 from 17.2 to 32.6 compared to previous method.
Identifiers
36848298PMC9958514What OpenQuestion holds
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.