55 citations · 67 across the 14 of their papers we have counts for
10 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…
An efficient representation of chronological events in medical texts
Andrey Kormilitzin, Nemanja Vaci, Qiang Liu +3
In this work we addressed the problem of capturing sequential information contained in longitudinal electronic health records (EHRs). Clinical notes, which is a particular type of…
Information Extraction from Swedish Medical Prescriptions with Sig-Transformer Encoder
John Pougue Biyong, Bo Wang, Terry Lyons +1
Relying on large pretrained language models such as Bidirectional Encoder Representations from Transformers (BERT) for encoding and adding a simple prediction layer has led to impr…
Med7: a transferable clinical natural language processing model for electronic health records
Andrey Kormilitzin, Nemanja Vaci, Qiang Liu +1
The field of clinical natural language processing has been advanced significantly since the introduction of deep learning models. The self-supervised representation learning and th…
Named Entity Recognition in Electronic Health Records Using Transfer Learning Bootstrapped Neural Networks
Luka Gligic, Andrey Kormilitzin, Paul Goldberg +1
Neural networks (NNs) have become the state of the art in many machine learning applications, especially in image and sound processing [1]. The same, although to a lesser extent [2…
Few-shot Learning for Named Entity Recognition in Medical Text
Maximilian Hofer, Andrey Kormilitzin, Paul Goldberg +1
Deep neural network models have recently achieved state-of-the-art performance gains in a variety of natural language processing (NLP) tasks (Young, Hazarika, Poria, & Cambria, 201…