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Found 14 papers, 5 papers with code
Bespoke Large Language Models for Digital Triage Assistance in Mental Health Care
The ability to efficiently recommend a relevant team by ingesting potentially voluminous clinical notes could help services both reduce referral waiting times and with the right technology, improve the evidence available to justify triage decisions.
Developing Healthcare Language Model Embedding Spaces
Pre-trained Large Language Models (LLMs) often struggle on out-of-domain datasets like healthcare focused text.
Efficiency at Scale: Investigating the Performance of Diminutive Language Models in Clinical Tasks
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 widespread desire to downsize and create specialised models without the need for complete fine-tuning, using Parameter Efficient Fine-tuning (PEFT) methods.
Detecting the Clinical Features of Difficult-to-Treat Depression using Synthetic Data from Large Language Models
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 continue to experience significant burden.
Identifying DNA Sequence Motifs Using Deep Learning
Splice sites play a crucial role in gene expression, and accurate prediction of these sites in DNA sequences is essential for diagnosing and treating genetic disorders.
Personalised recommendations of sleep behaviour with neural networks using sleep diaries captured in Sleepio
In collaboration with Big Health, the therapeutics company that created and utilizes Sleepio, we have analysed data from a random sample of 401, 174 sleep diaries and built a neural network to model sleep behaviour and sleep quality of each individual in a personalised manner.
Clinical Prompt Learning with Frozen Language Models
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 benchmarking text datasets in full, few-shot, and zero-shot train-evaluation setups.
Rationale production to support clinical decision-making
In this work, we apply InfoCal, the current state-of-the-art model that produces extractive rationales for its predictions, to the task of predicting hospital readmission using hospital discharge notes.
Population Gradients improve performance across data-sets and architectures in object classification
The most successful methods such as ReLU transfer functions, batch normalization, Xavier initialization, dropout, learning rate decay, or dynamic optimizers, have become standards in the field due, particularly, to their ability to increase the performance of Neural Networks (NNs) significantly and in almost all situations.
An efficient representation of chronological events in medical texts
In this work we addressed the problem of capturing sequential information contained in longitudinal electronic health records (EHRs).
Med7: a transferable clinical natural language processing model for electronic health records
In this work we introduced a named-entity recognition model for clinical natural language processing.
Medical Named Entity Recognition
named-entity-recognition
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Deep Learning for Estimating Synaptic Health of Primary Neuronal Cell Culture
Understanding the morphological changes of primary neuronal cells induced by chemical compounds is essential for drug discovery.
Named Entity Recognition in Electronic Health Records Using Transfer Learning Bootstrapped Neural Networks
In our study, we develop an approach that solves these problems for named entity recognition, obtaining 94. 6 F1 score in I2B2 2009 Medical Extraction Challenge [6], 4. 3 above the architecture that won the competition.
Few-shot Learning for Named Entity Recognition in Medical Text
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, 2017).
