8 citations · 8 across the 2 of their papers we have counts for
7 papers · 1 filter
LLM4EHR: Aligning Clinical Time Series with Medical Event Sequences via Large Language Models
Jingteng Li, Alexander Capstick, Louise Rigny +3
Recent research in clinical machine learning, focusing on outcome predictions in intensive care unit (ICU), has shifted from bespoke supervised models to foundation models, utilisi…
Evaluating Spoken Language as a Biomarker for Automated Screening of Cognitive Impairment
Maria R. Lima, Alexander Capstick, Fatemeh Geranmayeh +4
Timely and accurate assessment of cognitive impairment remains a major unmet need. Speech biomarkers offer a scalable, non-invasive, cost-effective solution for automated screening…
AutoElicit: Using Large Language Models for Expert Prior Elicitation in Predictive Modelling
Alexander Capstick, Rahul G. Krishnan, Payam Barnaghi
Large language models (LLMs) acquire a breadth of information across various domains. However, their computational complexity, cost, and lack of transparency often hinder their dir…
Training Neural Networks on Data Sources with Unknown Reliability
Alexander Capstick, Francesca Palermo, Tianyu Cui +1
When data is generated by multiple sources, conventional training methods update models assuming equal reliability for each source and do not consider their individual data quality…
Two-Stage Representation Learning for Analyzing Movement Behavior Dynamics in People Living with Dementia
Jin Cui, Alexander Capstick, Payam Barnaghi +1
In remote healthcare monitoring, time series representation learning reveals critical patient behavior patterns from high-frequency data. This study analyzes home activity data fro…
Representation Learning of Daily Movement Data Using Text Encoders
Alexander Capstick, Tianyu Cui, Yu Chen +1
Time-series representation learning is a key area of research for remote healthcare monitoring applications. In this work, we focus on a dataset of recordings of in-home activity f…