Annals of Computer Science and Information Systems, Volume 18

DOI: http://dx.doi.org/10.15439/2019F69

Citation: Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 18, pages 599–603 (2019)

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Abstract. Online registers contain a large amount of data about healthcare providers in the Czech Republic. Information is available to all citizens and can be useful to patients, governmental organisations or employers. Based on these data, we are able to create a high-quality snapshot of the current state of healthcare providers. Interconnecting data from more data sources together is an interesting task, and accomplishing it enables us to ask more complex questions. This paper focuses on answering several questions about dentists in our country. A dataset from one online database was created, using automated data mining methods and a subsequent analysis. Results are presented via an online tool, which was provided to owners of the data. They reviewed our results and decided to use our findings for the presentation to the Czech government and subsequent negotiation processes. Our paper describes used methods, shows some results and outlines possibilities for further work.

References

1. M. Karolyi and M. Komenda, ‘PŘEHLED ELEKTRONICKÝCH INFORMAČNÍCH ZDROJŮ VE ZDRAVOTNICTVÍ ČR’, MEDSOFT 2019, p. 5.
2. L. Dušek, J. Mužík, M. Karolyi, M. Šalko, D. Malúšková, and M. Komenda, ‘A Pilot Interactive Data Viewer for Cancer Screening’, in Environmental Software Systems. Computer Science for Environmental Protection: 12th IFIP WG 5.11 International Symposium, ISESS 2017, Zadar, Croatia, May 10-12, 2017, Proceedings 12, 2017, pp. 173–183.
3. C. Vaitsis et al., ‘Standardization in medical education: review, collection and selection of standards to address’, MEFANET J., vol. 5, no. 1, pp. 28–39, Nov. 2017.
4. M. Komenda, M. Karolyi, C. Vaitsis, D. Spachos, and L. Woodham, ‘A Pilot Medical Curriculum Analysis and Visualization According to Medbiquitous Standards’, in 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), 2017, pp. 144–149.
5. R. Wirth, ‘CRISP-DM: Towards a standard process model for data mining’, in Proceedings of the Fourth International Conference on the Practical Application of Knowledge Discovery and Data Mining, 2000, pp. 29–39.
6. S. vanden Broucke and B. Baesens, Practical Web Scraping for Data Science: Best Practices and Examples with Python. Apress, 2018.
7. R. Mitchell, Web scraping with Python: collecting data from the modern web, First edition. Sebastopol, CA: O’Reilly Media, 2015.
8. L. Woodham, J. Ščavnický, M. Karolyi, and M. Komenda, ‘Interactive presentation of evaluation data in training against medical errors’, Masarykova univerzita, 2018. [Online]. Available: https://www.muni.cz/vyzkum/publikace/1476359. [Accessed: 30- Apr-2019].
9. M. Komenda, J. Ščavnický, P. Růžičková, M. Karolyi, P. Štourač, and D. Schwarz, ‘Similarity Detection Between Virtual Patients and Medical Curriculum Using R’, Stud. Health Technol. Inform., vol. 255, pp. 222–226, 2018.