The Role of Open Standard Electronic Health Record in Medical Data Mining
##plugins.themes.bootstrap3.article.main##
Electronic Health Record (EHR) has received significant attention of all the health service provider in the world. EHR contains electronic information of all the patient information such as demographics, medical history, family medical history, lab tests and results, and prescribed drug. There is not any consistency in type of the EHR software implemented by the hosting organization. So, the EHR is currently vendor dependent and is not transferrable to another health service provider. The open standard electronic health record makes it public available to both vendor and patient. It can further aid in creating a universal EHR database for medical data mining. Mining the EHR helps in developing the best standard of care and clinical practice. The following paper proposes a universal EHR database and medical data mining. The benefits and challenges of implementing a database system is also discussed in the paper. The following paper will also analyze the different application areas of the EHR data mining.
Downloads
References
-
Brown, M. Data mining techniques. 2012; Available from: https://www.ibm.com/developerworks/library/ba-data-mining-techniques/
Google Scholar
1
-
Goyal, N., et al. A High Performance Computing Framework for Data Mining. in 2016 IEEE 23rd International Conference on High Performance Computing Workshops (HiPCW). 2016.
Google Scholar
2
-
Algwaiz, A., S. Rajasekaran, and R. Ammar. Data mining using Probabilistic Grammars. in 2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). 2016.
Google Scholar
3
-
Sharma, R., S.N. Singh, and S. Khatri. Medical Data Mining Using Different Classification and Clustering Techniques: A Critical Survey. in 2016 Second International Conference on Computational Intelligence & Communication Technology (CICT). 2016.
Google Scholar
4
-
Cios, K.M., William G, Uniqueness of medical data mining. Artificial Intelligence in Medicine, 2002. 26.
Google Scholar
5
-
Butt, N. and J. Shan. CyberCare: A Novel Electronic Health Record Management System. in 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE). 2016.
Google Scholar
6
-
Wei, X., W. Zou, and S. Gao. Mining Electronic Physical Records, a trial. in 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). 2016.
Google Scholar
7
-
John, M. Moving Toward an Open Standard, Universal Health Record. 2010; Available from: http://www.smart-publications.com/articles/moving-toward-an-open-standard-universal-health-record/page-2.
Google Scholar
8
-
McCALLUM, A. Distilling Structured Data from Unstructured Text 2005; Available from: http://delivery.acm.org/10.1145/1110000/1105679/p48-mccallum.pdf?ip=173.3.97.41&id=1105679&acc=OPEN&key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E6D218144511F3437&CFID=917918683&CFTOKEN=36526551&__acm__=1490891086_b750e9b0617beead0c5ac079a014ceef.
Google Scholar
9
-
Tatarinov, I., et al., Storing and querying ordered XML using a relational database system, in Proceedings of the 2002 ACM SIGMOD international conference on Management of data2002, ACM: Madison, Wisconsin. p. 204-215.
Google Scholar
10
-
Gharehchopogh, F.S. and Z.A. Khalifelu. Analysis and evaluation of unstructured data: text mining versus natural language processing. in 2011 5th International Conference on Application of Information and Communication Technologies (AICT). 2011.
Google Scholar
11
-
Gardner, J. and L. Xiong, An integrated framework for de-identifying unstructured medical data. Data & Knowledge Engineering, 2009. 68(12): p. 1441-1451.
Google Scholar
12
-
Jiawei Han, J.P., Micheline Kamber, Data Mining: Concepts and Techniques. 3rd ed2011.
Google Scholar
13
-
Babu, M.C., Souwmya. Claims Fraud: A Big Opportunity for Big Data & Analytics. 2013 July 29, 2013; Available from: http://www.claimsjournal.com/news/national/2013/07/29/233805.htm
Google Scholar
14
-
Schiller, D. EHRs and healthcare interoperability: The challenges, complexities, opportunities and reality. 2015; Available from: http://www.healthcareitnews.com/blog/ehrs-healthcare-interoperability-challenges-complexities-opportunities-reality.
Google Scholar
15
-
Europe, F.S.F. Open Standards. 2008; Available from: https://fsfe.org/about/basics/freesoftware.en.html.
Google Scholar
16
-
Flores, A.E. and V.M. Vergara. Functionalities of open electronic health records system: A follow-up study. in 2013 6th International Conference on Biomedical Engineering and Informatics. 2013.
Google Scholar
17
-
Vieira, M. and H. Madeira. Towards a security benchmark for database management systems. in 2005 International Conference on Dependable Systems and Networks (DSN'05). 2005.
Google Scholar
18
-
Banerjee, S., The Role of Global Educational Database in Educational Data Mining. European Journal of Engineering Research and Science, 2016. 1(6).
Google Scholar
19
-
Pranjul Yadav, M.S., Vipin Kumar & Gyorgy Simon, Mining Electronic Health Records (EHR): A Survey, in Department of Computer Science and Engineering 2015, University of Minnesota.
Google Scholar
20
-
William P. Castelli , J.T.D., Tavia Gordon , Curtis G. Hames , Marthana C. Hjortland , Ph. D , Stephen B. Hulley , Abraham Kagan , William , J. Zukel, HDL cholesterol and other lipids in coronary heart-disease - cooperative lipoprotein phenotyping study. Circulation, 1977. 55(5).
Google Scholar
21
-
Jensen, P.B., L.J. Jensen, and S. Brunak, Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet, 2012. 13(6): p. 395-405.
Google Scholar
22
-
Ng, K., et al., PARAMO: A Parallel Predictive Modeling Platform for Healthcare Analytic Research using Electronic Health Records. Journal of biomedical informatics, 2014. 48: p. 160-170.
Google Scholar
23
-
Tran, T.P., Dinh Luo, Wei & Venkatesh, Svetha, Stabilized sparse ordinal regression for medical risk stratification. Knowledge and information systems: an international journal, 2015. 43(3): p. 28.
Google Scholar
24
-
George Hripcsak, D.J.A., and Adler Perotte, Parameterizing time in electronic health record studies. Journal of the American Medical Informatics Association, 2015.
Google Scholar
25
-
Lohr, M.J.F.K.N., Guidelines for Clinical Practice: From Development to Use. National Academies Press, 1992.
Google Scholar
26
-
Frydman, H., Nonparametric estimation of a Markov ‘illness-death’ process from interval-censored observations, with application to diabetes survival data. Biometrika, 1995. 82(4): p. 773-789.
Google Scholar
27
-
Ramakrishnan, N., D. Hanauer, and B. Keller, Mining Electronic Health Records. Computer, 2010. 43(10): p. 77-81.
Google Scholar
28
-
Padhukasahasram, B., et al., Joint Impact of Clinical and Behavioral Variables on the Risk of Unplanned Readmission and Death after a Heart Failure Hospitalization. PLOS ONE, 2015. 10(6): p. e0129553.
Google Scholar
29
-
Kim, W., Object-oriented databases: definition and research directions. IEEE Transactions on Knowledge and Data Engineering, 1990. 2(3): p. 327-341.
Google Scholar
30
-
Pereira, R., et al. Usability of an electronic health record. in 2012 IEEE International Conference on Industrial Engineering and Engineering Management. 2012.
Google Scholar
31
-
Rihab A. HASANAIN, K.V.a.M.C., Electronic Medical Record Systems in Saudi Arabia: Knowledge and Preferences of Healthcare Professionals Journal of Health Informatics in Developing Countries, 2015. 9(1).
Google Scholar
32
-
Palma, G. Electronic Health Records: The Good, the Bad and the Ugly. 2013; Available from: http://www.beckershospitalreview.com/healthcare-information-technology/electronic-health-records-the-good-the-bad-and-the-ugly.html.
Google Scholar
33
-
Porter, M., Adoption of Electronic Health Records in the United States. Kaiser Permanente, 2013.
Google Scholar
34
-
Ajami, S. and T. Bagheri-Tadi, Barriers for Adopting Electronic Health Records (EHRs) by Physicians. Acta Informatica Medica, 2013. 21(2): p. 129-134.
Google Scholar
35
-
Gagnon, M.-P., et al., Multi-level analysis of electronic health record adoption by health care professionals: A study protocol. Implementation Science, 2010. 5(1): p. 30.
Google Scholar
36
-
Selvaraj, S. and J. Natarajan, Microarray Data Analysis and Mining Tools. Bioinformation, 2011. 6(3): p. 95-99.
Google Scholar
37
-
Lisboa, P.J.G., et al., Data Mining in Cancer Research [Application Notes]. IEEE Computational Intelligence Magazine, 2010. 5(1): p. 14-18.
Google Scholar
38
-
Jyoti Soni, U.A., Dipesh Sharma and Sunita Soni, Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction. INternational Journal of Computer Applications, 2011. 8(8).
Google Scholar
39
-
Wagle, S., J.A. Mangai, and V.S. Kumar. An improved medical image classification model using data mining techniques. in 2013 7th IEEE GCC Conference and Exhibition (GCC). 2013.
Google Scholar
40
-
Gotz, D., et al., Visual Cluster Analysis in Support of Clinical Decision Intelligence. AMIA Annual Symposium Proceedings, 2011. 2011: p. 481-490.
Google Scholar
41
-
Zhang, C., Zhang, Shichao, Association Rule Mining. Models and Algorithms2002: Springer-Verlag Berlin Heidelberg.
Google Scholar
42
-
Robu, R. and C. Hora. Medical data mining with extended WEKA. in 2012 IEEE 16th International Conference on Intelligent Engineering Systems (INES). 2012.
Google Scholar
43