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Hospital Data Interpretation: A Self-Organizing Map Approach

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Book cover Fuzzy Techniques: Theory and Applications (IFSA/NAFIPS 2019 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1000))

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Abstract

It is not feasible to attempt to interpret complex health care data with large dimensions using typical 2D or 3D charts, diagrams, or graphs. It is helpful to be able to correlate the dimensions against one another to discover new patterns and obtain fresh knowledge. Effective interpretation of the statistical data, collected from health care centers, helps physicians and clinicians to improve their efficiency and the quality of care. This work has used different types of Self-Organizing Maps (SOM) in order to provide visual interpretability of the collected data to the hospital administration. Using the approach presented in this work, existing correlations among different attributes of collected data can be discovered and utilized to uncover hidden patterns. We illustrate how Self-Organizing Maps can be effectively used in interpretation of health care and similar data.

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Notes

  1. 1.

    For more information refer to [2]. This task is currently performed under the supervision of Centers for Medicare and Medicaid Services https://www.cms.gov.

  2. 2.

    https://www.cms.gov.

  3. 3.

    It was formed in 2002 and later disbanded in 2011. Since then, Hospital Compare has been maintained by CMS only.

  4. 4.

    Refer to [2] for a detailed description of all of the inputs and measurements.

References

  1. Price, R.A., Elliott, M.N., Zaslavsky, A.M., Hays, R.D., Lehrman, W.G., Rybowski, L., Edgman-Levitan, S., Cleary, P.D.: Examining the role of patient experience surveys in measuring health care quality. Med. Care Res. Rev. 71(5), 522–554 (2014). PMID: 25027409

    Article  Google Scholar 

  2. Centers for Medicare and Medicaid Services, Baltimore, MD. https://data.medicare.gov/data/archives/hospital-compare. Accessed 01 July 2013

  3. Elliott, M.N., Cohea, C.W., Lehrman, W.G., Goldstein, E.H., Cleary, P.D., Giordano, L.A., Beckett, M.K., Zaslavsky, A.M.: Accelerating improvement and narrowing gaps: trends in patients experiences with hospital care reflected in HCAHPS public reporting. Health Serv. Res. 50, 1850–1867 (2015)

    Article  Google Scholar 

  4. Kohonen, T.: Essentials of the self-organizing map. Neural Netw. 37, 52–65 (2013). Twenty-fifth Anniversay Commemorative Issue

    Article  Google Scholar 

  5. Miljković, D.: Brief review of self-organizing maps. In: 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 1061–1066, May 2017

    Google Scholar 

  6. Lipton, Z.C.: The Mythos of model interpretability. CoRR, vol. abs/1606.03490 (2016)

    Google Scholar 

  7. Balasupramanian, N., Ephrem, B.G., Al-Barwani, I.S.: User pattern based online fraud detection and prevention using big data analytics and self organizing maps. In: 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), pp. 691–694, July 2017

    Google Scholar 

  8. Jian, L., Ruicheng, Y., Rongrong, G.: Self-organizing map method for fraudulent financial data detection. In: 2016 3rd International Conference on Information Science and Control Engineering (ICISCE), pp. 607–610, July 2016

    Google Scholar 

  9. Bach, M.P., Vlahović, N., Pivar, J.: Self-organizing maps for fraud profiling in leasing. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 1203–1208, May 2018

    Google Scholar 

  10. Langin, C., Zhou, H., Rahimi, S., Gupta, B., Zargham, M., Sayeh, M.R.: A self-organizing map and its modeling for discovering malignant network traffic. In: 2009 IEEE Symposium on Computational Intelligence in Cyber Security, pp. 122–129, March 2009

    Google Scholar 

  11. Yao, C., Luo, X., Zincir-Heywood, A.N.: Data analytics for modeling and visualizing attack behaviors: a case study on SSH brute force attacks. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8, November 2017

    Google Scholar 

  12. AlHamouz, S., Abu-Shareha, A.: Hybrid classification approach using self-organizing map and back propagation artificial neural networks for intrusion detection. In: 2017 10th International Conference on Developments in eSystems Engineering (DeSE), pp. 83–87, June 2017

    Google Scholar 

  13. Almi’ani, M., Ghazleh, A.A., Al-Rahayfeh, A., Razaque, A.: Intelligent intrusion detection system using clustered self organized map. In: 2018 Fifth International Conference on Software Defined Systems (SDS), pp. 138–144, April 2018

    Google Scholar 

  14. Langin, C., Wainer, M., Rahimi, S.: ANNaBell Island: a 3D color hexagonal SOM for visual intrusion detection. Int. J. Comput. Sci. Inf. Secur. 9(1), 1–7 (2011)

    Google Scholar 

  15. Nam, T.M., Phong, P.H., Khoa, T.D., Huong, T.T., Nam, P.N., Thanh, N.H., Thang, L.X., Tuan, P.A., Dung, L.Q., Loi, V.D.: Self-organizing map-based approaches in DDoS flooding detection using SDN. In: 2018 International Conference on Information Networking (ICOIN), pp. 249–254, January 2018

    Google Scholar 

  16. Zribi, M., Boujelbene, Y., Abdelkafi, I., Feki, R.: The self-organizing maps of Kohonen in the medical classification. In: 2012 6th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), pp. 852–856, March 2012

    Google Scholar 

  17. Markey, M.K., Lo, J.Y., Tourassi, G.D., Floyd Jr., C.E.: Self-organizing map for cluster analysis of a breast cancer database. Artif. Intell. Med. 27, 113–127 (2003)

    Article  Google Scholar 

  18. Chandra, B., Nath, S., Malhothra, A.: Classification and clustering of breast cancer images. In: The 2006 IEEE International Joint Conference on Neural Network Proceedings, pp. 3843–3847, July 2006

    Google Scholar 

  19. Platon, L., Zehraoui, F., Tahi, F.: Self-organizing maps with supervised layer. In: 2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM), pp. 1–8, June 2017

    Google Scholar 

  20. Ijaz, A., Choi, J.: Anomaly detection of electromyographic signals. IEEE Trans. Neural Syst. Rehabil. Eng. 26, 770–779 (2018)

    Article  Google Scholar 

  21. Ayesh, A., Arevalillo-Herráez, M., Arnau-González, P.: Class discovery from semi-structured EEG data for affective computing and personalisation. In: 2017 IEEE 16th International Conference on Cognitive Informatics Cognitive Computing (ICCI*CC), pp. 96–101, July 2017

    Google Scholar 

  22. Basara, H.G., Yuan, M.: Community health assessment using self-organizing maps and geographic information systems. Int. J. Health Geogr. 7, 67 (2008)

    Article  Google Scholar 

  23. Tirunagari, S., Poh, N., Hu, G., Windridge, D.: Identifying similar patients using self-organising maps: a case study on type-1 diabetes self-care survey responses. CoRR, vol. abs/1503.06316 (2015)

    Google Scholar 

  24. Shah, S., Luo, X.: Exploring diseases based biomedical document clustering and visualization using self-organizing maps. In: 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom), pp. 1–6, October 2017

    Google Scholar 

  25. Pourkia, J.: A self-organizing map approach for hospital data analysis. MS thesis, SIUCOpenAccess (2014)

    Google Scholar 

  26. Grumbach, K., Selby, J.V., Damberg, C., et al.: Resolving the gatekeeper conundrum: what patients value in primary care and referrals to specialists. JAMA 282(3), 261–266 (1999)

    Article  Google Scholar 

  27. Garavaglia, S.B.: Health care customer satisfaction survey analysis using self-organizing maps and “exponentially smeared” data vectors. In: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, vol. 4, pp. 119–124, July 2000

    Google Scholar 

  28. Tabrizi, T.S., Khoie, M.R., Sahebkar, E., Rahimi, S., Marhamati, N.: Towards a patient satisfaction based hospital recommendation system. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 131–138, July 2016

    Google Scholar 

  29. Centers for Medicare and Medicaid Services, Baltimore, MD. http://www.hcahpsonline.org. Accessed 01 July 2013

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Author information

Authors and Affiliations

  1. Southern Illinois University, Carbondale, IL, 62901, USA

    Javid Pourkia

  2. Department of Computer Science and Engineerinbg, Mississippi State University, Starkville, MS, 39759, USA

    Shahram Rahimi & Kourosh Teimouri Baghaei

Authors
  1. Javid Pourkia
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  2. Shahram Rahimi
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  3. Kourosh Teimouri Baghaei
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Corresponding author

Correspondence to Javid Pourkia .

Editor information

Editors and Affiliations

  1. Department of Mathematics, University of Louisiana at Lafayette, Lafayette, LA, USA

    Ralph Baker Kearfott

  2. Center for Computing Research (CIC), Instituto Politecnico Nacional, Ciudad de Mexico, Mexico

    Ildar Batyrshin

  3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Marek Reformat

  4. Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA

    Martine Ceberio

  5. Department of Computer Science, University of Texas at El Paso , El Paso, TX, USA

    Vladik Kreinovich

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Pourkia, J., Rahimi, S., Baghaei, K.T. (2019). Hospital Data Interpretation: A Self-Organizing Map Approach. In: Kearfott, R., Batyrshin, I., Reformat, M., Ceberio, M., Kreinovich, V. (eds) Fuzzy Techniques: Theory and Applications. IFSA/NAFIPS 2019 2019. Advances in Intelligent Systems and Computing, vol 1000. Springer, Cham. https://doi.org/10.1007/978-3-030-21920-8_44

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