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/2110.09868v1
• Date: Tue, 19 Oct 2021 11:45:01 GMT
• Title: Designing A Clinically Applicable Deep Recurrent Model to Identify Neuropsychiatric Symptoms in People Living with Dementia Using In-Home Monitoring Data
• Authors: Francesca Palermo, Honglin Li, Alexander Capstick, Nan Fletcher-Lloyd, Yuchen Zhao, Samaneh Kouchaki, Ramin Nilforooshan, David Sharp, Payam Barnaghi
• Abstract summary: Agitation is one of the neuropsychiatric symptoms with high prevalence in dementia. Detecting agitation episodes can assist in providing People Living with Dementia (PLWD) with early and timely interventions. This preliminary study presents a supervised learning model to analyse the risk of agitation in PLWD using in-home monitoring data.
• Score: 52.40058724040671
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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. Detecting agitation episodes can assist in providing People Living with Dementia (PLWD) with early and timely interventions. Analysing agitation episodes will also help identify modifiable factors such as ambient temperature and sleep as possible components causing agitation in an individual. This preliminary study presents a supervised learning model to analyse the risk of agitation in PLWD using in-home monitoring data. The in-home monitoring data includes motion sensors, physiological measurements, and the use of kitchen appliances from 46 homes of PLWD between April 2019-June 2021. We apply a recurrent deep learning model to identify agitation episodes validated and recorded by a clinical monitoring team. 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 and 37.64% F1 scores when employing the optimal parameters, suggesting a good ability to recognise agitation events. We also discuss using machine learning models for analysing the behavioural patterns using continuous monitoring data and explore clinical applicability and the choices between sensitivity and specificity in-home monitoring applications.

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