In clinical practice, patient care flows are generally subject to recommended and standardized therapeutic interventions. Especially in a home care setting, situation-Aware adherence to therapy can be both difficult for the patient to follow and difficult for the physician to assess. Process mining techniques may be useful artificial intelligence solutions for remotely assessing the compliance of patients' behavior with the corresponding care path, especially if adopted in a cognitive IoT Edge infrastructure, dedicated to the acquisition and analysis of daily routines in a form of event log. In this paper, we present an innovative method to measure in-home adherence to metabolic syndrome management with the aim of providing awareness of the patient's current situation. The analytical results demonstrate the validity of using process mining techniques to remotely evaluate patient behavior.
A Situation Awareness Computational Intelligent Model for Metabolic Syndrome Management / Lofù, D.; Pazienza, A.; Ardito, C.; Di Noia, T.; Di Sciascio, E.; Vitulano, F.. - (2022), pp. 118-124. (Intervento presentato al convegno 2022 IEEE International Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2022 tenutosi a ita nel 2022) [10.1109/CogSIMA54611.2022.9830673].
A Situation Awareness Computational Intelligent Model for Metabolic Syndrome Management
Lofù D.
;Ardito C.;Di Noia T.;Di Sciascio E.;
2022-01-01
Abstract
In clinical practice, patient care flows are generally subject to recommended and standardized therapeutic interventions. Especially in a home care setting, situation-Aware adherence to therapy can be both difficult for the patient to follow and difficult for the physician to assess. Process mining techniques may be useful artificial intelligence solutions for remotely assessing the compliance of patients' behavior with the corresponding care path, especially if adopted in a cognitive IoT Edge infrastructure, dedicated to the acquisition and analysis of daily routines in a form of event log. In this paper, we present an innovative method to measure in-home adherence to metabolic syndrome management with the aim of providing awareness of the patient's current situation. The analytical results demonstrate the validity of using process mining techniques to remotely evaluate patient behavior.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
