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Found 10 papers, 3 papers with code
Urinary Tract Infection Detection in Digital Remote Monitoring: Strategies for Managing Participant-Specific Prediction Complexity
The current research focuses on improving the performance of previous models, particularly by refining the Multilayer Perceptron (MLP), to better handle variations in home environments and improve sex fairness in predictions by making use of concepts from multitask learning.
Two-Stage Representation Learning for Analyzing Movement Behavior Dynamics in People Living with Dementia
In remote healthcare monitoring, time series representation learning reveals critical patient behavior patterns from high-frequency data.
Evaluating Spoken Language as a Biomarker for Automated Screening of Cognitive Impairment
For ADRD classification, a Random Forest applied to lexical features achieved a mean sensitivity of 69. 4% (95% confidence interval (CI) = 66. 4-72. 5) and specificity of 83. 3% (78. 0-88. 7).
AutoElicit: Using Large Language Models for Expert Prior Elicitation in Predictive Modelling
We find that AutoElicit yields priors that can substantially reduce error over uninformative priors, using fewer labels, and consistently outperform in-context learning.
Enabling Regional Explainability by Automatic and Model-agnostic Rule Extraction
This method enhances the regional explainability of machine learning models and offers wider applicability compared to existing methods.
Representation Learning of Daily Movement Data Using Text Encoders
Time-series representation learning is a key area of research for remote healthcare monitoring applications.
Information Theory Inspired Pattern Analysis for Time-series Data
For applications with stochastic state transitions, features are developed based on Shannon's entropy of Markov chains, entropy rates of Markov chains, entropy production of Markov chains, and von Neumann entropy of Markov chains.
Training Neural Networks on Data Sources with Unknown Reliability
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
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 individuals.
When Complexity Is Good: Do We Need Recurrent Deep Learning For Time Series Outlier Detection?
Recurrent deep learning methods have a larger capacity for learning complex representations in time series data.
