8 papers
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…
Urinary Tract Infection Detection in Digital Remote Monitoring: Strategies for Managing Participant-Specific Prediction Complexity
Kexin Fan, Alexander Capstick, Ramin Nilforooshan +1
Urinary tract infections (UTIs) are a significant health concern, particularly for people living with dementia (PLWD), as they can lead to severe complications if not detected and…
Analyzing Patient Daily Movement Behavior Dynamics Using Two-Stage Encoding Model
Jin Cui, Alexander Capstick, Payam Barnaghi +1
In the analysis of remote healthcare monitoring data, time series representation learning offers substantial value in uncovering deeper patterns of patient behavior, especially giv…
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…