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Article

Knowledge-Based Decision Support in Healthcare via Near Field Communication

1
Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, via E. Orabona 4, 70125 Bari, Italy
2
Department of Emergency and Organ Transplantation (DETO) Rheumatology Unit, University of Bari, Piazza G. Cesare 11, 70124 Bari, Italy
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(17), 4923; https://doi.org/10.3390/s20174923
Received: 17 July 2020 / Revised: 23 August 2020 / Accepted: 24 August 2020 / Published: 31 August 2020
(This article belongs to the Special Issue Smart IoT Systems for Pervasive Healthcare)
The benefits of automatic identification technologies in healthcare have been largely recognized. Nevertheless, unlocking their potential to support the most knowledge-intensive medical tasks requires to go beyond mere item identification. This paper presents an innovative Decision Support System (DSS), based on a semantic enhancement of Near Field Communication (NFC) standard. Annotated descriptions of medications and patient’s case history are stored in NFC transponders and used to help caregivers providing the right therapy. The proposed framework includes a lightweight reasoning engine to infer possible incompatibilities in treatment, suggesting substitute therapies. A working prototype is presented in a rheumatology case study and preliminary performance tests are reported. The approach is independent from back-end infrastructures. The proposed DSS framework is validated in a limited but realistic case study, and performance evaluation of the prototype supports its practical feasibility. Automated reasoning on knowledge fragments extracted via NFC enables effective decision support not only in hospital centers, but also in pervasive IoT-based healthcare contexts such as first aid, ambulance transport, rehabilitation facilities and home care. View Full-Text
Keywords: decision support; ubiquitous healthcare; knowledge graph; automated reasoning; near-field communication decision support; ubiquitous healthcare; knowledge graph; automated reasoning; near-field communication
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MDPI and ACS Style

Loseto, G.; Scioscia, F.; Ruta, M.; Gramegna, F.; Ieva, S.; Pinto, A.; Scioscia, C. Knowledge-Based Decision Support in Healthcare via Near Field Communication. Sensors 2020, 20, 4923. https://doi.org/10.3390/s20174923

AMA Style

Loseto G, Scioscia F, Ruta M, Gramegna F, Ieva S, Pinto A, Scioscia C. Knowledge-Based Decision Support in Healthcare via Near Field Communication. Sensors. 2020; 20(17):4923. https://doi.org/10.3390/s20174923

Chicago/Turabian Style

Loseto, Giuseppe, Floriano Scioscia, Michele Ruta, Filippo Gramegna, Saverio Ieva, Agnese Pinto, and Crescenzio Scioscia. 2020. "Knowledge-Based Decision Support in Healthcare via Near Field Communication" Sensors 20, no. 17: 4923. https://doi.org/10.3390/s20174923

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