55 citations · 67 across the 14 of their papers we have counts for
15 papers
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…
Bespoke Large Language Models for Digital Triage Assistance in Mental Health Care
Niall Taylor, Andrey Kormilitzin, Isabelle Lorge +2
Contemporary large language models (LLMs) may have utility for processing unstructured, narrative free-text clinical data contained in electronic health records (EHRs) -- a particu…
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…
Personalised recommendations of sleep behaviour with neural networks using sleep diaries captured in Sleepio
Alejo Nevado-Holgado, Colin Espie, Maria Liakata +6
SleepioTM is a digital mobile phone and web platform that uses techniques from cognitive behavioural therapy (CBT) to improve sleep in people with sleep difficulty. As part of this…
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…
Population Gradients improve performance across data-sets and architectures in object classification
Yurika Sakai, Andrey Kormilitzin, Qiang Liu +1
The most successful methods such as ReLU transfer functions, batch normalization, Xavier initialization, dropout, learning rate decay, or dynamic optimizers, have become standards…
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…
Learning to Detect Bipolar Disorder and Borderline Personality Disorder with Language and Speech in Non-Clinical Interviews
Bo Wang, Yue Wu, Niall Taylor +4
Bipolar disorder (BD) and borderline personality disorder (BPD) are both chronic psychiatric disorders. However, their overlapping symptoms and common comorbidity make it challengi…
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…
Deep Learning for Estimating Synaptic Health of Primary Neuronal Cell Culture
Andrey Kormilitzin, Xinyu Yang, William H. Stone +5
Understanding the morphological changes of primary neuronal cells induced by chemical compounds is essential for drug discovery. Using the data from a single high-throughput imagin…
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…