8 citations · 11 across the 12 of their papers we have counts for
12 papers
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…
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…
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…
Information Theory Inspired Pattern Analysis for Time-series Data
Yushan Huang, Yuchen Zhao, Alexander Capstick +3
Current methods for pattern analysis in time series mainly rely on statistical features or probabilistic learning and inference methods to identify patterns and trends in the data.…
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…