8 citations · 11 across the 12 of their papers we have counts for
9 papers · 1 filter
RACER: Role-Aligned Competence Estimation for Human-AI Routing
Joshua Strong, Emma Sun, Alexander Capstick +3
Learning to defer asks a predictive system when to act autonomously and when to defer to a human expert. Population-adaptive deferral extends this problem to unseen experts using a…
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…
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…
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…
Enabling Regional Explainability by Automatic and Model-agnostic Rule Extraction
Yu Chen, Tianyu Cui, Alexander Capstick +2
In Explainable AI, rule extraction translates model knowledge into logical rules, such as IF-THEN statements, crucial for understanding patterns learned by black-box models. This c…
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…
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…
Designing A Clinically Applicable Deep Recurrent Model to Identify Neuropsychiatric Symptoms in People Living with Dementia Using In-Home Monitoring Data
Francesca Palermo, Honglin Li, Alexander Capstick +6
Agitation is one of the neuropsychiatric symptoms with high prevalence in dementia which can negatively impact the Activities of Daily Living (ADL) and the independence of individu…