Paper Info
Title
Towards the Design of a Machine Learning-based Consumer Healthcare Platform powered by Electronic Health Records and measurement of Lifestyle through Smartphone Data
Abstract
The estimation of Biological Age (BA) has been debated for several years and no clear and universal understanding has yet been reached to solve this task. Accordingly, the knowledge of an accurate BA index for each individual may be relevant in various areas including health, economy, social policies and decision making processes. The main contribution of this work is the design of a Machine Learning based-consumer healthcare platform powered by electronic health record data (clinical features) and smartphone data (lifestyle features) in order to estimate a sub-index that is strictly correlated with the BA. Preliminary results extracted from a representative subset of clinical and lifestyle features, highlight the potential of the proposed framework in order to estimate the health and physical status of each subject (in terms of the difference between the predicted Chronological Age and the real Chronological Age). Future work will be conducted to encapsulate more information and validate the predicted BA sub-index.
Year
DOI
Venue
2019
10.1109/ISCE.2019.8901034
2019 IEEE 23rd International Symposium on Consumer Technologies (ISCT)
Keywords
DocType
ISSN
social policies,decision making processes,clinical features,smartphone data,lifestyle features,physical status,chronological age,machine learning-based consumer healthcare platform,electronic health records,biological age,universal understanding,BA index,health status,economy policies,health policies
Conference
0747-668X
ISBN
Citations 
PageRank 
978-1-7281-3571-7
0
0.34
References 
Authors
3
8
Name
Order
Citations
PageRank
Alessandro Ferri100.34
Riccardo Rosati24655270.37
Michele Bernardini323.07
Leonardo Gabrielli400.34
Sara Casaccia500.34
luca romeo6219.59
Andrea Monteriu710227.48
Emanuele Frontoni824847.04