BibTex format
@article{Palermo,
author = {Palermo, F and Li, H and Capstick, A and Fletcher-Lloyd, N and Zhao, Y and Kouchaki, S and Nilforooshan, R and Sharp, D and Barnaghi, P},
title = {Designing A Clinically Applicable Deep Recurrent Model to Identify Neuropsychiatric Symptoms in People Living with Dementia Using In-Home Monitoring Data},
url = {http://arxiv.org/abs/2110.09868v2},
}
RIS format (EndNote, RefMan)
TY - JOUR
AB - Agitation is one of the neuropsychiatric symptoms with high prevalence indementia which can negatively impact the Activities of Daily Living (ADL) andthe independence of individuals. Detecting agitation episodes can assist inproviding People Living with Dementia (PLWD) with early and timelyinterventions. Analysing agitation episodes will also help identify modifiablefactors such as ambient temperature and sleep as possible components causingagitation in an individual. This preliminary study presents a supervisedlearning model to analyse the risk of agitation in PLWD using in-homemonitoring data. The in-home monitoring data includes motion sensors,physiological measurements, and the use of kitchen appliances from 46 homes ofPLWD between April 2019-June 2021. We apply a recurrent deep learning model toidentify agitation episodes validated and recorded by a clinical monitoringteam. We present the experiments to assess the efficacy of the proposed model.The proposed model achieves an average of 79.78% recall, 27.66% precision and37.64% F1 scores when employing the optimal parameters, suggesting a goodability to recognise agitation events. We also discuss using machine learningmodels for analysing the behavioural patterns using continuous monitoring dataand explore clinical applicability and the choices between sensitivity andspecificity in-home monitoring applications.
AU - Palermo,F
AU - Li,H
AU - Capstick,A
AU - Fletcher-Lloyd,N
AU - Zhao,Y
AU - Kouchaki,S
AU - Nilforooshan,R
AU - Sharp,D
AU - Barnaghi,P
TI - Designing A Clinically Applicable Deep Recurrent Model to Identify Neuropsychiatric Symptoms in People Living with Dementia Using In-Home Monitoring Data
UR - http://arxiv.org/abs/2110.09868v2
ER -
