6 citations · 12 across the 8 of their papers we have counts for
5 papers · 1 filter
Developing Healthcare Language Model Embedding Spaces
Niall Taylor, Dan Schofield, Andrey Kormilitzin +2
Pre-trained Large Language Models (LLMs) often struggle on out-of-domain datasets like healthcare focused text. We explore specialized pre-training to adapt smaller LLMs to differe…
Efficiency at Scale: Investigating the Performance of Diminutive Language Models in Clinical Tasks
Niall Taylor, Upamanyu Ghose, Omid Rohanian +4
The entry of large language models (LLMs) into research and commercial spaces has led to a trend of ever-larger models, with initial promises of generalisability, followed by a wid…
Detecting the Clinical Features of Difficult-to-Treat Depression using Synthetic Data from Large Language Models
Isabelle Lorge, Dan W. Joyce, Niall Taylor +3
Difficult-to-treat depression (DTD) has been proposed as a broader and more clinically comprehensive perspective on a person's depressive disorder where despite treatment, they con…
Clinical Prompt Learning with Frozen Language Models
Niall Taylor, Yi Zhang, Dan Joyce +2
Prompt learning is a new paradigm in the Natural Language Processing (NLP) field which has shown impressive performance on a number of natural language tasks with common benchmarki…
Rationale production to support clinical decision-making
Niall Taylor, Lei Sha, Dan W Joyce +3
The development of neural networks for clinical artificial intelligence (AI) is reliant on interpretability, transparency, and performance. The need to delve into the black-box neu…