Journal article
Fuzzy reasoning of accident provenance in pervasive healthcare monitoring systems
IEEE journal of biomedical and health informatics, v 17(6), pp 1015-1022
Nov 2013
PMID: 24240719
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
In pervasive healthcare monitoring environments, data provenance, as one metadata, can help people analyze the reasons for medical accidents that are generated by complex events. This reasoning processing often encounters inaccurate time and irreversible reasoning problems. How to solve the uncertain process and fuzzy transformation time presents many challenges to the study of data provenance. In this paper, we propose a backward derivation model with the provenance semantic, backward fuzzy time reasoning net (BFTRN), to solve these two problems. We design a backward reasoning algorithm motivated by time automation theory based on this model. With regard to given life-critical alarms and some constraints, it cannot only derive all evolution paths and the possibility distribution of paths from historical information, but also efficiently compute the value of fuzzy time function for each transition of lift-critical complex alarms in the healthcare monitoring system. We also analyze the properties of BFTRN model in this paper. Experiments on real dataset show that the proposed model is efficient.
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Details
- Title
- Fuzzy reasoning of accident provenance in pervasive healthcare monitoring systems
- Creators
- Yongli WangXiaohua Hu
- Publication Details
- IEEE journal of biomedical and health informatics, v 17(6), pp 1015-1022
- Publisher
- The Institute of Electrical and Electronics Engineers, Inc. (IEEE); United States
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000327394200005
- Scopus ID
- 2-s2.0-84888359216
- Other Identifier
- 991014877711404721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Information Systems
- Computer Science, Interdisciplinary Applications
- Mathematical & Computational Biology
- Medical Informatics