Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 8

Proceedings of the 2016 Federated Conference on Computer Science and Information Systems

Customized Web-based System for Elderly People Using Elements of Artificial Intelligence

, ,

DOI: http://dx.doi.org/10.15439/2016F165

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 277280 ()

Full text

Abstract. Making life easier for the elderly represents a new challenge for the ICT sector. This paper presents a new web-based system designed and implemented with the aim to support the social inclusion and to improve the daily routine of the elderly people within basic information and communication features. The system provides some advanced functionalities to utilise the information value of the data collected within the presented system, e.g. the recommendations based on similar hobbies or health problems; a simple medical diagnostics; a creation of a knowledge base containing experiences and best practices, etc. We designed the system in accordance with local conditions in Slovakia, so its full functioning relies on the progress in e-Health legislation. Presented version is a preliminary result that will be further improved and tested within a real practice.

References

  1. F. Babič, L. Majnarič, A. Lukáčová, J. Paralič, A. Holzinger, “On Patient’s Characteristics Extraction for Metabolic Syndrome Diagnosis: Predictive Modelling Based on Machine Learning“, in Information Technology in Bio- and Medical Informatics, LNCS Vol. 8649, 2014, pp. 118-132, http://dx.doi.org/10.1007/978-3-319-10265-8_11.
  2. L. Breiman, J. H. Friedman, R. A. Olshen, C. J. Stone, “Classification and regression trees“, Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software, 1984.
  3. B. G., Buchanan, E.H. Shortliffe,“ Rule Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project“, Reading, MA: Addison-Wesley, 1984.
  4. P. Butka, J. Pócs, J. Pócsová, “On Equivalence of Conceptual Scaling and Generalized One-Sided Concept Lattices”, in Information Sciences 259, 2017, pp. 57-70, http://dx.doi.org/10.1016/j.ins.2013.08.047.
  5. P. Butka, J. Pócs, J. Pócsová, “Distributed Computation of Generalized One-Sided Concept Lattices on Sparse Data Tables”, in Computing and Informatics 34 (1), 2015, pp. 77-98.
  6. R. J. Conejar, H. K. Kim, “A Medical Decision Support System (DSS) for Ubiquitous Healthcare Diagnosis System“, in International Journal of Software Engineering and Its Applications, 8 (10), 2014, pp. 234-244, http://dx.doi.org/10.14257/ijseia.20104.8.10.22.
  7. R. Edwards, “Changing Places?: Flexibility, Lifelong Learning and a Learning Society”, Routledge, 1997.
  8. P. Han-Saem, C. Sung-Bae, “Evolutionary attribute ordering in Bayesian networks for predicting the metabolic syndrome“, in Expert Systems with Applications, 39(4), 2012, pp. 4240-4249, http://dx.doi.org/10.1016/j.eswa.2011.09.110.
  9. R. J. Hodes, D. A. B. Lindberg, “Making Your Web Site Senior Friendly”, published by the National Institute on Aging and the National Library of Medicine, 2002.
  10. A. Holzinger, M. Dehmer, I. Jurisica, “Knowledge Discovery and Interactive Data Mining in Bioinformatics – State-of-the-Art, in Future challenges and Research Directions”, BMC Bioinformatics 15(suppl. 6), I1, 2014, http://dx.doi.org/10.1186/1471-2105-15-S6-I1.
  11. V. A. Kamaev, D. P. Panchenko, N. V. Le, O. A. Trushkina, „An Intelligent Medical Differential Diagnosis System Based on Expert Systems“, in Knowledge-Based Software Engineering, Communications in Computer and Information Science, Volume 466, 2014, pp. 576-584.
  12. T. Matsumoto, Y. Ueda, S. Kawaji, “A software system for giving clues of medical diagnosis to clinician“, in Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002), IEEE, Maribor, Slovenia, 2002, pp. 65-70, http://dx.doi.org/10.1109/CBMS.2002.1011356.
  13. D. Novák, O. Štepanková, M. Mráz, M. Haluzík, M. Bussoli, M. Uller, K. Maly, L. Nováková, P. Novák, “OLDES: new solution for long-term diabetes compensation management”, in Proceedings of 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver, Canada, 2008, pp. 4346-4349, http://dx.doi.org/10.1109/IEMBS.2008.4650172.
  14. D. Novák, O. Štepánková, S. Rousseaux, M. Busuoli, M. Carulli, G. D’Agosta, T. Gallelli, M. Uller, et al., “Does IT Bring Hope for Wellbeing?”, in book Handbook of Research on ICTs for Human-Centered Healthcare and Social Care Services, IGI Global, Editors: Maria Manuela Cruz-Cunha, Maria Miranda, 2103, pp. 270-302.
  15. D. Novák, M. Uller, S. Rousseaux, M. Mráz, J. Smrž, O. Štepanková, M. Haluzík, M. Busuoli, “Diabetes management in OLDES project“, in Proceedings of 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, Minnesota, USA, 2009, pp. 7228-7231, http://dx.doi.org/10.1109/IEMBS.2009.5335256.
  16. W. Oude Nijeweme-d'Hollosy., L. S. van Velsen, R. Soer, H. J. Hermens, “Design of a web-based clinical decision support system for guiding patients with low back pain to the best next step in primary healthcare“, in: Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016), Rome, Italy, 2016, pp. 229-239, http://dx.doi.org/10.5220/0005662102290239.
  17. J. R. Quinlan, “C4.5: Programs for Machine Learning”, San Mateo: Morgan Kaufmann, 1993.
  18. N. Pérez, M. A. Guevara, A. Silva, I. Ramos, J. Loureiro, “Improving the performance of machine learning classifiers for Breast Cancer diagnosis based on feature selection”, in Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, 2014, pp. 209-217, 10.15439/2014F249.
  19. H. L. Semigran, J. A. Linder, C. Gidengil, A. Mehrotra, “Evaluation of symptom checkers for self-diagnosis and triage: audit study”, in BMJ, 351:h3480, 2015, http://dx.doi.org/10.1136/bmj.h3480.
  20. C. Shearer, “The CRISP-DM Model: The New Blueprint for Data Mining”, in Journal of Data Warehousing, 5 (4), 2000, pp. 13-22.
  21. G. Tutoky, F. Babic, Wagner J., “ICT-based solution for elderly people”, in Proceedings of IEEE 11th International Conference on Emerging eLearning Technologies and Applications, Stará Lesná, Slovakia, 2013, pp. 366-404, http://dx.doi.org/10.1109/ICETA.2013.6674466.
  22. E. Zaitseva, M. Kvassay, V. Levashenko, J. Kostolny, “Introduction to knowledge discovery in medical databases and use of reliability analysis in data mining”, in Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, 2015, pp. 311-320, http://dx.doi.org/10.15439/2015F327.
  23. W3C: Web Accessibility Initiative: Developing Websites for Older People: How Web Content Accessibility Guidelines (WCAG) 2.0 Applies.