35 citations · 66 across the 5 of their papers we have counts for
5 papers
Can GPT-3.5 Generate and Code Discharge Summaries?
Matúš Falis, Aryo Pradipta Gema, Hang Dong +6
Objective: To investigate GPT-3.5 in generating and coding medical documents with ICD-10 codes for data augmentation on low-resources labels. Materials and Methods: Employing GPT-3…
Large Language Models Vote: Prompting for Rare Disease Identification
David Oniani, Jordan Hilsman, Hang Dong +3
The emergence of generative Large Language Models (LLMs) emphasizes the need for accurate and efficient prompting approaches. LLMs are often applied in Few-Shot Learning (FSL) cont…
Ontology-Driven and Weakly Supervised Rare Disease Identification from Clinical Notes
Hang Dong, Víctor Suárez-Paniagua, Huayu Zhang +7
Computational text phenotyping is the practice of identifying patients with certain disorders and traits from clinical notes. Rare diseases are challenging to be identified due to…
Contextual Semantic Embeddings for Ontology Subsumption Prediction
Jiaoyan Chen, Yuan He, Yuxia Geng +3
Automating ontology construction and curation is an important but challenging task in knowledge engineering and artificial intelligence. Prediction by machine learning techniques s…
Explainable Automated Coding of Clinical Notes using Hierarchical Label-wise Attention Networks and Label Embedding Initialisation
Hang Dong, Víctor Suárez-Paniagua, William Whiteley +1
Diagnostic or procedural coding of clinical notes aims to derive a coded summary of disease-related information about patients. Such coding is usually done manually in hospitals bu…