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An Attention Model to Analyse the Risk of Agitation and Urinary Tract Infections in People with Dementia by Li, Honglin; Rezvani, Roonak; Kolanko, Magdalena Anita; Sharp, David J.; Wairagkar, Maitreyee; Vaidyanathan, Ravi; Nilforooshan, Ramin; Barnaghi, Payam is a scholarly article available to read on EtoBox.

What is An Attention Model to Analyse the Risk of Agitation and Urinary Tract Infections in People with Dementia about?

Behavioural symptoms and urinary tract infections (UTI) are among the most common problems faced by people with dementia. One of the key challenges in the management of these conditions is early detection and timely intervention in order to reduce distress and avoid unplanned hospital admissions. Using in-home sensing technologies and machine learning models for sensor data integration and analysis provides opportunities to detect and predict clinically significant events and changes in health status. We have developed an integrated platform to collect in-home sensor data and performed an observational study to apply machine learning models for agitation and UTI risk analysis. We collected a large dataset from 88 participants with a mean age of 82 and a standard deviation of 6.5 (47 females and 41 males) to evaluate a new deep learning model that utilises attention and rational mechanism. The proposed solution can process a large volume of data over a period of time and extract significant patterns in a time-series data (i.e. attention) and use the extracted features and patterns to train risk analysis models (i.e. rational). The proposed model can explain the predictions by indica

Author

Li, Honglin; Rezvani, Roonak; Kolanko, Magdalena Anita; Sharp, David J.; Wairagkar, Maitreyee; Vaidyanathan, Ravi; Nilforooshan, Ramin; Barnaghi, Payam

Published

2021

Language

EN