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Knowledge-Based Decision Support in Healthcare via Near Field Communication
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 (D...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506702/ https://www.ncbi.nlm.nih.gov/pubmed/32878204 http://dx.doi.org/10.3390/s20174923 |
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author | Loseto, Giuseppe Scioscia, Floriano Ruta, Michele Gramegna, Filippo Ieva, Saverio Pinto, Agnese Scioscia, Crescenzio |
author_facet | Loseto, Giuseppe Scioscia, Floriano Ruta, Michele Gramegna, Filippo Ieva, Saverio Pinto, Agnese Scioscia, Crescenzio |
author_sort | Loseto, Giuseppe |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7506702 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75067022020-09-26 Knowledge-Based Decision Support in Healthcare via Near Field Communication Loseto, Giuseppe Scioscia, Floriano Ruta, Michele Gramegna, Filippo Ieva, Saverio Pinto, Agnese Scioscia, Crescenzio Sensors (Basel) Article 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. MDPI 2020-08-31 /pmc/articles/PMC7506702/ /pubmed/32878204 http://dx.doi.org/10.3390/s20174923 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Loseto, Giuseppe Scioscia, Floriano Ruta, Michele Gramegna, Filippo Ieva, Saverio Pinto, Agnese Scioscia, Crescenzio Knowledge-Based Decision Support in Healthcare via Near Field Communication |
title | Knowledge-Based Decision Support in Healthcare via Near Field Communication |
title_full | Knowledge-Based Decision Support in Healthcare via Near Field Communication |
title_fullStr | Knowledge-Based Decision Support in Healthcare via Near Field Communication |
title_full_unstemmed | Knowledge-Based Decision Support in Healthcare via Near Field Communication |
title_short | Knowledge-Based Decision Support in Healthcare via Near Field Communication |
title_sort | knowledge-based decision support in healthcare via near field communication |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506702/ https://www.ncbi.nlm.nih.gov/pubmed/32878204 http://dx.doi.org/10.3390/s20174923 |
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