Elaheh Kalantari
Academic and research departments
Centre for Vision, Speech and Signal Processing (CVSSP), Department of Electrical and Electronic Engineering.About
My research project
Machine learning models to analyse in-home observation, measurement, and clinical data for remote patient monitoringThis research will leverage machine learning and deep learning algorithms to integrate and represent multimodal data. The goal is to extract relevant information, detect disparities among subjects, predict disease stages, and develop scalable and generalizable statistical models for guiding therapeutic interventions. The study involves handling sensitive data from wearable and ambient sensors used by individuals with dementia in various monitoring settings, including laboratories and homes. The primary objective is to precisely identify sleep disturbances, which serve as both symptoms and risk factors for dementia.
This research will leverage machine learning and deep learning algorithms to integrate and represent multimodal data. The goal is to extract relevant information, detect disparities among subjects, predict disease stages, and develop scalable and generalizable statistical models for guiding therapeutic interventions. The study involves handling sensitive data from wearable and ambient sensors used by individuals with dementia in various monitoring settings, including laboratories and homes. The primary objective is to precisely identify sleep disturbances, which serve as both symptoms and risk factors for dementia.
Publications
Oncology patients experience numerous co-occurring symptoms during their treatment. The identification of sentinel/core symptoms is a vital prerequisite for therapeutic interventions. In this study, using Network Analysis, we investigated the inter-relationships among 38 common symptoms over time (i.e., a total of six time points over two cycles of chemotherapy) in 987 oncology patients with four different types of cancer (i.e., breast, gastrointestinal, gynaecological, and lung). In addition, we evaluated the associations between and among symptoms and symptoms clusters and examined the strength of these interactions over time. Eight unique symptom clusters were identified within the networks. Findings from this research suggest that changes occur in the relationships and interconnections between and among co-occurring symptoms and symptoms clusters that depend on the time point in the chemotherapy cycle and the type of cancer. The evaluation of the centrality measures provides new insights into the relative importance of individual symptoms within various networks that can be considered as potential targets for symptom management interventions.