Electronic Health Record Error Prevention Approach Using Ontology in Big Data

Electronic Health Record (EHR) systems have been playing a dramatically important role in tele-health domains. One of the major benefits of using EHR systems is assisting physicians to gain patients' healthcare information and shorten the process of the medical decision making. However, physicians' inputs still have a great impact on making decisions that cannot be checked by EHR systems. This consequence can be influenced by human behaviors or physicians' knowledge structures. An efficient approach of alerting to the unusual decisions is an urgent requirement for current EHR systems. This paper proposes a schema using ontology in big data to generate an alerting mechanism to assist physicians to make a proper medical diagnosis. The proposed model is Ontology-based EHR Error Prevention Model (OEHR-EPM), which is implemented by a proposed algorithm, Error Prevention Adjustment Algorithm (EPAA). The ontological approach uses Protege to represent the knowledge-based ontology. The proposed schema has been examined by our experiments and the experimental results show that our schema has a higher-level accuracy rate and acceptable operating time performance.

[1]  Meikang Qiu,et al.  Thermal-aware task scheduling in 3D chip multiprocessor with real-time constrained workloads , 2013, TECS.

[2]  Min Chen,et al.  A novel pre-cache schema for high performance Android system , 2016, Future Gener. Comput. Syst..

[3]  Keke Gai,et al.  A Reusable Software Component for Integrated Syntax and Semantic Validation for Services Computing , 2015, 2015 IEEE Symposium on Service-Oriented System Engineering.

[4]  Meikang Qiu,et al.  Rotation Scheduling and Voltage Assignment to Minimize Energy for SoC , 2009, 2009 International Conference on Computational Science and Engineering.

[5]  S. Brunak,et al.  Mining electronic health records: towards better research applications and clinical care , 2012, Nature Reviews Genetics.

[6]  R. Kalaiselvi,et al.  SCALABLE AND SECURE SHARING OF PERSONAL HEALTH RECORDS IN CLOUD COMPUTING , 2016 .

[7]  Meikang Qiu,et al.  Optimal Data Allocation for Scratch-Pad Memory on Embedded Multi-core Systems , 2011, 2011 International Conference on Parallel Processing.

[8]  Arshdeep Bahga,et al.  A Cloud-based Approach for Interoperable Electronic Health Records (EHRs) , 2013, IEEE Journal of Biomedical and Health Informatics.

[9]  Meikang Qiu,et al.  Cost minimization while satisfying hard/soft timing constraints for heterogeneous embedded systems , 2009, TODE.

[10]  Min Chen,et al.  Balance of security strength and energy for a PMU monitoring system in smart grid , 2012, IEEE Communications Magazine.

[11]  Keke Gai,et al.  Phase-Change Memory Optimization for Green Cloud with Genetic Algorithm , 2015, IEEE Transactions on Computers.

[12]  Meikang Qiu,et al.  Online optimization for scheduling preemptable tasks on IaaS cloud systems , 2012, J. Parallel Distributed Comput..

[13]  Meikang Qiu,et al.  Energy-Aware Loop Parallelism Maximization for Multi-core DSP Architectures , 2010, 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing.

[14]  Meikang Qiu,et al.  Security-aware optimization for ubiquitous computing systems with SEAT graph approach , 2013, J. Comput. Syst. Sci..

[15]  Keke Gai,et al.  Data transfer minimization for financial derivative pricing using Monte Carlo simulation with GPU in 5G , 2016, Int. J. Commun. Syst..

[16]  Olga Boric-Lubecke,et al.  E-healthcare: Remote monitoring, privacy, and security , 2014, 2014 IEEE MTT-S International Microwave Symposium (IMS2014).

[17]  Arantza Illarramendi,et al.  Toward Semantic Interoperability of Electronic Health Records , 2012, IEEE Transactions on Information Technology in Biomedicine.

[18]  Lixin Tao,et al.  Integrated Syntax and Semantic Validation for Services Computing , 2013, 2013 IEEE International Conference on Services Computing.

[19]  Yao Zheng,et al.  Scalable and Secure Sharing of Personal Health Records in Cloud Computing Using Attribute-Based Encryption , 2019, IEEE Transactions on Parallel and Distributed Systems.

[20]  Hamido Fujita,et al.  Mental Ontology model for medical diagnosis based on some intuitionistic fuzzy functions , 2012, 2012 IEEE 10th Jubilee International Symposium on Intelligent Systems and Informatics.

[21]  Keke Gai,et al.  Intrusion detection techniques for mobile cloud computing in heterogeneous 5G , 2016, Secur. Commun. Networks.

[22]  J. Alberto Espinosa,et al.  Big Data: Issues and Challenges Moving Forward , 2013, 2013 46th Hawaii International Conference on System Sciences.

[23]  Keke Gai,et al.  Towards Cloud Computing: A Literature Review on Cloud Computing and Its Development Trends , 2012, 2012 Fourth International Conference on Multimedia Information Networking and Security.

[24]  Ricky K. Taira,et al.  Context-Based Electronic Health Record: Toward Patient Specific Healthcare , 2012, IEEE Transactions on Information Technology in Biomedicine.

[25]  Susan S Woods,et al.  Patient Experiences With Full Electronic Access to Health Records and Clinical Notes Through the My HealtheVet Personal Health Record Pilot: Qualitative Study , 2013, Journal of medical Internet research.

[26]  Hongming Cai,et al.  Personal Healthcare Record Integration Method Based on Linked Data Model , 2014, 2014 IEEE 11th International Conference on e-Business Engineering.

[27]  Min Chen,et al.  Energy Efficient Security Algorithm for Power Grid Wide Area Monitoring System , 2011, IEEE Transactions on Smart Grid.

[28]  Zhi Chen,et al.  Data Allocation for Hybrid Memory With Genetic Algorithm , 2015, IEEE Transactions on Emerging Topics in Computing.