Healthcare predictive analytics for disease progression: a longitudinal data fusion approach
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Healthcare predictive analytics for disease progression: a longitudinal data fusion approach Yi Zheng1
· Xiangpei Hu1
Received: 1 November 2019 / Revised: 20 April 2020 / Accepted: 22 April 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Healthcare predictive analytics using electronic health records (EHR) offers a promising direction to address the challenging tasks of health assessment. It is highly important to precisely predict the potential disease progression based on the knowledge in the EHR data for chronic disease care. In this paper, we utilize a novel longitudinal data fusion approach to model the disease progression for chronic disease care. Different from the conventional method using only initial or static clinical data to model the disease progression for current time prediction, we design a temporal regularization term to maintain the temporal successivity of data from different time points and simultaneously analyze data from data source level and feature level based on a sparse regularization regression approach. We examine our approach through extensive experiments on the medical data provided by the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The results show that the proposed approach is more useful to simulate and predict the disease progression compared with the existing methods. Keywords Healthcare predictive analytics · Longitudinal data fusion · Machine learning · Regression · Group lasso
1 Introduction Improvements in healthcare in the past century have contributed to people living longer and healthier lives. However, this has also resulted in an increase in the number of people with non-communicable diseases, including Alzheimer’s disease. Alzheimer’s disease (AD), the most common type of dementia, is characterized by the progressive impairment of neurons and their connections resulting in loss of cognitive function and ultimately death (Khachaturian 1985). According to the World Health Organization (2012), most new cases and Yi Zheng
[email protected] Xiangpei Hu [email protected] 1
Institute of Systems Engineering, Dalian University of Technology, Dalian, 116024, People’s Republic of China
Journal of Intelligent Information Systems
mortalities of Alzheimer’s disease occur in low- and middle- income countries. Current estimates indicate 31.2 million people worldwide are living with AD in 2015, and this number will almost double every 20 years. AD has caused substantial social and economic burden to every country, the total estimated worldwide cost of AD in 2015 is $600 billion (Prince 2015). The surging cases and expenses make patients, clinical experts, and health policymakers around the world believe that effective interventions are needed to prevent, detect, and manage Alzheimer’s disease and their sequelae (OECD 2014). With increased adoption of electronic health record (EHR) systems in clinical practices, EHR data analytics for advanced clinical decision support is attracting both scientific and practical interest (Agarwal e
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