Authors:
Chongke Wu
1
;
Jeno Szep
1
;
Salim Hariri
1
;
Nimit K. Agarwal
2
;
Sumit K. Agarwal
2
and
Carlos Nevarez
3
Affiliations:
1
NSF Center for Cloud and Autonomic Computing, The University of Arizona, Tucson, Arizona, U.S.A.
;
2
Department of Medicine, Banner, University Medical Center Phoenix, Phoenix, Arizona, U.S.A.
;
3
SevaTechnology LLC, Tucson, Arizona, U.S.A.
Keyword(s):
Artificial Intelligence, Chatbot, Healthcare, Patient Monitoring, Delirium, Internet of Things.
Abstract:
As a dangerous syndrome, delirium affects more than 50% of hospitalized older adults and has an economic burden of 164 billion US dollars per year. It is crucial to prevent, identify and treat this syndrome systematically on all hospitalized patients to prevent its short and long-term complications. Currently, there are no AI-based tools being utilized at a large scale focused on delirium management in hospital settings. The advancement of the Internet of Things in the medical arena can be leveraged to help clinical teams managing the care of patients in the hospital. The renaissance of Artificial Intelligence brings the chance to analyze a large amount of monitoring data. Deep neural networks like Convolutional Neural Network and Recurrent Neural Network revolutionize the fields of Computer Vision and Natural Language Processing. Deep learning tasks like action recognition and language understanding can be incorporated into the routine workflow of healthcare staff to improve care. B
y leveraging AI and deep learning techniques, we have developed a chatbot based monitoring system (that we refer to as SeVA) to improve the workload of the medical staff by using an Artificial Emotional Intelligence platform. The SeVA platform includes two mobile applications that provide timely patient monitoring, regular nursing checks, and health status recording features. We demonstrate the current progress of deploying the SeVA platform in a healthcare setting.
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