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1. Mason, E., Jain, S., Kendall, M., Mostashari, F., Blumenthal, D., The regional extension center program: Helping physicians meaningfully use health information technology. Ann. Intern. Med., 153, 666–670, 2010.

2. Mossialos, E., Wenzl, M., Osborn, R., Sarnak, D., International Profiles of Healthcare Systems, The Commonwealth Fund, New York, NY, 2016.

3. Parikh, R.B., Kakad, M., Bates, D.W., Integrating predictive analytics into high-value care: the dawn of precision delivery. JAMA, 315, 651–652, 2016.

4. Goldstein, B.A., Navar, A.M., Pencina, M.J., Ioannidis, J.P., Opportunities and challenges in developing risk prediction models with electronic health records data: A systematic review. J. Am. Med. Inform. Assoc., 27, 1, 198–208, 2016.

5. Jung, K., Covington, S., Sen, C.K., Januszyk, M., Kirsner, R.S., Gurtner, G.C. et al., Rapid identification of slow healing wounds. Wound Repair Regen., 24, 181–188, 2016.

6. Pencina, M.J. and Peterson, E.D., Moving from clinical trials to precision medicine: The role for predictive modeling. JAMA, 315, 1713–1714, 2016.

7. Chen, R. and Michael, S., Promise of personalized comics to precision medicine. Wiley Interdiscip. Rev. Syst. Biol. Med., 5.1, 77–82, 2013.

8. Nickel, M. and Kiela, D., Poincare Embeddings for Learning Hierarchical Representations arXiv:170508039, abs/1705.08039, 1–10, 2017.

9. Lin, C., Jain, S., Kim, H., Bar-Joseph, Z., Using neural networks for reducing the dimensions of single-cell RNA-Seq data. Nucleic Acids Res., 45, 17, e156–e156, 2017.

10. Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J., GRAM: Graph-based attention model for healthcare representation learning. International Conference on Knowledge Discovery and Data Mining (KDD), ACM, pp. 787–795, 2017.

11. Silver, M., Sakata, T., Su, H.C., Herman, C., Dolins, S.B., O’Shea, M.J., Case study: How to apply data mining techniques in a healthcare dataware house. Healthcare Inf. Manage., 15, 2, 155–164, 2001.

12. NHCAA, https://www.nhcaa.org, 2020.

13. FBI Reports and publications: Financial, https://www.fbi.gov/starts-services/publications, 2009.

14. FBI Scams and Safety: Common fraud schemes, https://www.fbi.gov/, Scams-and-safety, 2011.

15. Spencer, K. and Herbert, D., Mass Marketing of Property and Liability Insurance, Department of Transportation, United States of America, 1970.

16. Li, J., Huang, K., Jin, J., Shi, J., A survey on statistical methods for healthcare fraud detection. Healthcare Manage. Sci., 11, 275–287, 2008.

17. London: The Guardian. The mystery of John Darwin, https://www.theguardian.com, 2007.

18. Relles, D., Ridgeway, G., Cater, G., Data mining and the implementation of a prospective payment system for inpatient rehabilitation. Health Serv. Outcomes Res. Method, 3, 3–4, 247–266, 2002.

19. Koh, H. and Tan, G., Data mining applications in healthcare. J. Healthc Inf. Mgmt., 19, 2, 64–72, 2005.

20. Hall, C., Intelligent data mining at IBM: New products and applications. Intel Software Strategy, 7, 5, 1–11, 1996.

21. Jeffries, D., Zaidi, I., Jong, B., Holland, M., Miles, D., Analysis of flow cytometry data using an automatic processing tool. Cytometry Part A, 73A, 9, 857–867, 2008.

^* Corresponding author: parthafca@kiit.ac.in