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Found 8 papers, 2 papers with code
Developing Healthcare Language Model Embedding Spaces
Pre-trained Large Language Models (LLMs) often struggle on out-of-domain datasets like healthcare focused text.
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.
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.
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.
Learning to Detect Bipolar Disorder and Borderline Personality Disorder with Language and Speech in Non-Clinical Interviews
Bipolar disorder (BD) and borderline personality disorder (BPD) are both chronic psychiatric disorders.
