Health wearable devices are adopted to acquire on the are daily patient physiological data.All data are collected into a Cassandra Big data sstem and are processed by Artificial Intelligence (AI) algorithms such as Support Vector Machine (SVM) and Long Short Term Memory (LSTM).The outputs of the AI algorithms provide,with a good accuracy,the prediction of systemic vascular resistenca, hearth rate and blood pressure parameters.The study is focused on formulation of a four-dimension (4D) model (Cube model updated during the time) mapping for each patient the health risk:the risk maps are based on the simultaneous analysis of multiple information including psychological score,physical activity, and environement pollution.
Health wearable devices for patient monitoring and enabling big data multi parametric analysis by artificial intelligence.
Fabio Manca
;Angelo Vacca
2020-01-01
Abstract
Health wearable devices are adopted to acquire on the are daily patient physiological data.All data are collected into a Cassandra Big data sstem and are processed by Artificial Intelligence (AI) algorithms such as Support Vector Machine (SVM) and Long Short Term Memory (LSTM).The outputs of the AI algorithms provide,with a good accuracy,the prediction of systemic vascular resistenca, hearth rate and blood pressure parameters.The study is focused on formulation of a four-dimension (4D) model (Cube model updated during the time) mapping for each patient the health risk:the risk maps are based on the simultaneous analysis of multiple information including psychological score,physical activity, and environement pollution.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
