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Classification Method for Differential Diagnosis Based on the Course of Episode of Care

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Hybrid Artificial Intelligent Systems (HAIS 2013)

Abstract

The main goal of the paper is to propose a classification method for differential diagnosis in primary care domain. Commonly, the final diagnosis for the episode of care is related with the initial reason for encounter (RfE). However, many distinct diagnoses can follow from a single RfE and they need to be distinguished. The new method exploits the data about whole episodes of care quantified by individual patients’ encounters and it extracts episode features from electronic health record to learn the classifier. The experimental studies carried out on two primary care dataset from Malta and the Netherlands for three distinct diagnostic groups revealed the validity of the proposed approach.

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References

  1. Soler, J.K., Okkes, I.: Reasons for encounter and symptom diagnoses: a superior description of patients’ problems in contrast to medically unexplained symptoms (mus). Family Practice 29(3), 272–282 (2012)

    Article  Google Scholar 

  2. Schiff, G., Bates, D.: Can electronic clinical documentation help prevent diagnostic errors? The New England Journal of Medicine 362(12), 1066–1069 (2010)

    Article  Google Scholar 

  3. Romano, M., Stafford, R.: Electronic health records and clinical decision support systems. Arch. Intern. Med. 171(10), 897–903 (2011)

    Article  Google Scholar 

  4. Kortteisto, T., Komulainen, J., Kunnamo, I., Makela, M., Kaila, M.: Implementing clinical decision support for primary care professionals – the process. Finnish Journal of eHealth and eWelfare 4(3), 153–164 (2012)

    Google Scholar 

  5. Yoo, I., Alafaireet, P., Marinov, M.: Data mining in healthcare and biomedicine: A survey of the literature. J. Med. Syst. 36, 2431–2448 (2012)

    Article  Google Scholar 

  6. Li, J., Fu, A., Fahey, P.: Efficient discovery of risk patterns in medical data. Artificial Intelligence in Medicine 45, 77–89 (2009)

    Article  Google Scholar 

  7. Yang, F., Wang, H.: Using random forest for reliable classification and cost-sensitive learning for medical diagnosis. In: The Seventh Asia Pacific Bioinformatics Conference (2009)

    Google Scholar 

  8. WONCA: An introduction to the international classification of primary care version 2. Technical report, World Organization of Family Doctors (WONCA), WONCA International Classification Committee (WICC) (2004)

    Google Scholar 

  9. Olson, D., Delen, D.: Advanced Data Mining Techniques. Springer (2008)

    Google Scholar 

  10. Tiwari, A., Sekhar, A.K.T.: Review article: Workflow based framework for life science informatics. Comput. Biol. Chem. 31(5-6), 305–319 (2007)

    Article  MATH  Google Scholar 

  11. Frank, E., Hall, M.A., Holmes, G., Kirkby, R., Pfahringer, B., Witten, I.H., Trigg, L.: Weka - a machine learning workbench for data mining. In: Maimon, O., Rokach, L. (eds.) The Data Mining and Knowledge Discovery Handbook, pp. 1305–1314. Springer (2005)

    Google Scholar 

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Popiel, A. et al. (2013). Classification Method for Differential Diagnosis Based on the Course of Episode of Care. In: Pan, JS., Polycarpou, M.M., Woźniak, M., de Carvalho, A.C.P.L.F., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2013. Lecture Notes in Computer Science(), vol 8073. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40846-5_12

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  • DOI: https://doi.org/10.1007/978-3-642-40846-5_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40845-8

  • Online ISBN: 978-3-642-40846-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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