Prognostication Stereotype of Patients Morbidity and Mortality by Extraction of E-Health Records

Sunitha .T, Shyamala .J, Annie Jesus Suganthi Rani.A

Abstract


Data mining suggest an innovative way of prognostication stereotype of Patients health risks. Large amount of Electronic Health Records (EHRs) collected over the years have provided a rich base for risk analysis and prediction. An EHR contains digitally stored healthcare information about an individual, such as observations, laboratory tests, diagnostic reports, medications, procedures, patient identifying information and allergies. A special type of EHR is the Health Examination Records (HER) from annual general health check-ups. Identifying participants at risk based on their current and past HERs is important for early warning and preventive intervention. By “risk”, we mean unwanted outcomes such as mortality and morbidity. This approach is limited due to the classification problem and consequently it is not informative about the specific disease area in which a personal is at risk. Limited amount of data extracted from the health record is not feasible for providing the accurate risk prediction. The main motive of this project is for risk prediction to classify progressively developing situation with the majority of the data unlabeled.

Full Text:

PDF

References


A Bayesian perspective on early stage event prediction in longitudinal data Mahtab J. Fard, Ping Wang, Sanjay Chawla, and Chandan K. Reddy, Senior Member, IEEE,2016

Cog Boost- Boosting for fast cost sensitive graph classifications S. Pan, J. Wu, and X. Zhu, 2015

A Relative similarity based method for interactive patient risk prediction B. Qian, X. Wang, N. Cao, H. Li, and Y.-G. Jiang., 2015

Mining personal health index from annual geriatric medical examinations L. Chen, X. Li, S. Wang, H.-Y. Hu, N. Huang, Q. Z. , and M. Sharaf., 2014

Extending association rule summarization techniques to assess risk of diabetes mellitus , G. J. Simon, P. J. Caraballo, T. M. Therneau, S. S. Cha, M. R.Castro and P. W. Li., 2013

Varun Chandola and Vipin Kumar. Summarization – compressing data into an informative representation. Knowledge and Information Systems, 2006.

Gary S Collins, Susan Mallett, Omar Omar, and Ly-Mee Yu. Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting. BMC Medicine, 2011.

Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. The New England Journal of Medicine, 346(6), 2002.

Gang Fang, Majda Haznadar, Wen Wang, Haoyu Yu, Michael Steinbach, Timothy R Church, William S Oetting, Brian Van Ness, and Vipin Kumar. High-order snp combinations associated with complex diseases: efficient discovery, statistical power and functional interactions. PLoS One, 7(4):e33531, 2012.

Mohammad Al Hasan. Summarization in pattern mining. In Encyclopedia of Data Warehousing and Mining, (2nd Ed). Information Science Reference, 2008.




DOI: https://doi.org/10.23956/ijermt.v6i6.271

Refbacks

  • There are currently no refbacks.