BibTex format
@article{Li,
author = {Li, H and Rezvani, R and Kolanko, MA and Sharp, DJ and Wairagkar, M and Vaidyanathan, R and Nilforooshan, R and Barnaghi, P},
title = {An attention model to analyse the risk of agitation and urinary tract infections in people with dementia},
url = {http://arxiv.org/abs/2101.07007v1},
}
RIS format (EndNote, RefMan)
TY - JOUR
AB - Behavioural symptoms and urinary tract infections (UTI) are among the mostcommon problems faced by people with dementia. One of the key challenges in themanagement of these conditions is early detection and timely intervention inorder to reduce distress and avoid unplanned hospital admissions. Using in-homesensing technologies and machine learning models for sensor data integrationand analysis provides opportunities to detect and predict clinicallysignificant events and changes in health status. We have developed anintegrated platform to collect in-home sensor data and performed anobservational study to apply machine learning models for agitation and UTI riskanalysis. We collected a large dataset from 88 participants with a mean age of82 and a standard deviation of 6.5 (47 females and 41 males) to evaluate a newdeep learning model that utilises attention and rational mechanism. Theproposed solution can process a large volume of data over a period of time andextract significant patterns in a time-series data (i.e. attention) and use theextracted features and patterns to train risk analysis models (i.e. rational).The proposed model can explain the predictions by indicating which time-stepsand features are used in a long series of time-series data. The model providesa recall of 91\% and precision of 83\% in detecting the risk of agitation andUTIs. This model can be used for early detection of conditions such as UTIs andmanaging of neuropsychiatric symptoms such as agitation in association withinitial treatment and early intervention approaches. In our study we havedeveloped a set of clinical pathways for early interventions using the alertsgenerated by the proposed model and a clinical monitoring team has been set upto use the platform and respond to the alerts according to the createdintervention plans.
AU - Li,H
AU - Rezvani,R
AU - Kolanko,MA
AU - Sharp,DJ
AU - Wairagkar,M
AU - Vaidyanathan,R
AU - Nilforooshan,R
AU - Barnaghi,P
TI - An attention model to analyse the risk of agitation and urinary tract infections in people with dementia
UR - http://arxiv.org/abs/2101.07007v1
ER -
