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.
Towards the Design of a Machine Learning-based Consumer Healthcare Platform powered by Electronic Health Records and measurement of Lifestyle through Smartphone Data / Ferri, A.; Rosati, R.; Bernardini, M.; Gabrielli, L.; Casaccia, S.; Romeo, L.; Monteriu, A.; Frontoni, E.. - (2019), pp. 37-40. (Intervento presentato al convegno 23rd IEEE International Symposium on Consumer Technologies, ISCT 2019 tenutosi a ita nel 2019) [10.1109/ISCE.2019.8901034].