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Found 12 papers, 7 papers with code
MeSHup: Corpus for Full Text Biomedical Document Indexing
Medical Subject Heading (MeSH) indexing refers to the problem of assigning a given biomedical document with the most relevant labels from an extremely large set of MeSH terms.
Multi-stage Retrieve and Re-rank Model for Automatic Medical Coding Recommendation
In this paper, we leverage a multi-stage ``retrieve and re-rank'' framework as a novel solution to ICD indexing, via a hybrid discrete retrieval method, and re-rank retrieved candidates with contrastive learning that allows the model to make more accurate predictions from a simplified label space.
Auxiliary Knowledge-Induced Learning for Automatic Multi-Label Medical Document Classification
The International Classification of Diseases (ICD) is an authoritative medical classification system of different diseases and conditions for clinical and management purposes.
Beyond the Limits: A Survey of Techniques to Extend the Context Length in Large Language Models
Recently, large language models (LLMs) have shown remarkable capabilities including understanding context, engaging in logical reasoning, and generating responses.
Investigating the Learning Behaviour of In-context Learning: A Comparison with Supervised Learning
Large language models (LLMs) have shown remarkable capacity for in-context learning (ICL), where learning a new task from just a few training examples is done without being explicitly pre-trained.
MeSHup: A Corpus for Full Text Biomedical Document Indexing
Medical Subject Heading (MeSH) indexing refers to the problem of assigning a given biomedical document with the most relevant labels from an extremely large set of MeSH terms.
KenMeSH: Knowledge-enhanced End-to-end Biomedical Text Labelling
Currently, Medical Subject Headings (MeSH) are manually assigned to every biomedical article published and subsequently recorded in the PubMed database to facilitate retrieving relevant information.
Exploring Text Specific and Blackbox Fairness Algorithms in Multimodal Clinical NLP
Clinical machine learning is increasingly multimodal, collected in both structured tabular formats and unstructured forms such as freetext.
End-to-end Named Entity Recognition and Relation Extraction using Pre-trained Language Models
In this paper, we propose a neural, end-to-end model for jointly extracting entities and their relations which does not rely on external NLP tools and which integrates a large, pre-trained language model.
L2P: Learning to Place for Estimating Heavy-Tailed Distributed Outcomes
In its placing phase, L2P obtains a prediction by placing the new instance among the known instances.
Incorporating Figure Captions and Descriptive Text in MeSH Term Indexing
The goal of text classification is to automatically assign categories to documents.
Classical Music Clustering Based on Acoustic Features
In this paper we cluster 330 classical music pieces collected from MusicNet database based on their musical note sequence.
