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Telemedicine Software Application

  • Received : 2021.02.05
  • Published : 2021.02.28

Abstract

Currently, hospitals and medical practices have a large amount of unstructured information, gathered in time at each ward or practice by physicians in a wide range of medical branches. The data requires processing in order to be able to extract relevant information, which can be used to improve the medical system. It is useful for a physician to have access to a patient's entire medical history when he or she is in an emergency situation, as relevant information can be found about the patient's problems such as: allergies to various medications, personal history, or hereditary collateral conditions etc. If the information exists in a structured form, the detection of diseases based on specific symptoms is much easier, faster and with a higher degree of accuracy. Thus, physicians may investigate certain pathological profiles and conduct cohort clinical trials, including comparing the profile of a particular patient with other similar profiles that already have a confirmed diagnosis. Involving information technology in this field will change so the time which the physicians should spend in front of the computer into a much more beneficial one, providing them with the possibility for more interaction with the patient while listening to the patient's needs. The expert system, described in the paper, is an application for medical diagnostic of the most frequently met conditions, based on logical programming and on the theory of probabilities. The system rationale is a search item in the field basic knowledge on the condition. The web application described in the paper is implemented for the ward of pathological anatomy of a hospital in Romania. It aims to ease the healthcare staff's work, to create a connection of communication at one click between the necessary wards and to reduce the time lost with bureaucratic proceedings. The software (made in PHP programming language, by writing directly in the source code) is developed in order to ease the healthcare staff's activity, being created in a simpler and as elegant way as possible.

Keywords

References

  1. M.C. Popescu, Artificial intelligence, Reprographics of the University of Tg. Jiu (in Romanian), 1999.
  2. L. Zhou, M. Sordo, Expert systems in medicine, Artificial Intelligence in Medicine, Technical Basis and Clinical Applications, p.75-100, 2021.
  3. S. Kaur, J. Singla, L. Nkenyereye, S. Jha, D. Prashar, G.P. Joshi, S. El-Sappach, MD. S. Islam, Medical Diagnostic Systems Using Artificial Intelligence Algorithms: Principles and Perspectives, IEEE Acces, Vol.8, p.228049-228069, 2020. https://doi.org/10.1109/ACCESS.2020.3042273
  4. P Bidyuk, I Kalinina, A Gozhyj, Methodology of Constructing Statistical Models for Nonlinear Non-stationary Processes in Medical Diagnostic Systems, 3rd International Conference on Informatics & Data-Driven Medicine, Vaxjo, Sweden, 2020.
  5. L.M. Fagan, E.H. Shortliffe, B.G. Buchanan, Computer-based medical decision making: from MYCIN to VM, Automedica, Vol.3, p.97-106, 1980.
  6. S. Allier, O. Barais, B. Baudry, J. Bourcier, Multitier Diversification in Web-Based Software Applications, IEEE Software, Vol. 32(1), p.83-90, 2015. https://doi.org/10.1109/MS.2014.150
  7. K.F. Braekkan Payne, H. Wharrad, K. Watts, Smartphone and medical related App use among medical students and junior physician s in the United Kingdom: a regional survey, BMC Medical Informatics and Decision Making, p.1-11, 2012.
  8. P. Sherman, Usability success stories: How organizations improve by making easier-to-use software and web sites, Taylor&Francis, 1st Edition, London, 2007.
  9. P. Todinca, Management of medical bulletins, Dissertation thesis, unpublished (in Romanian), Arad, 2018.
  10. http://php.net/manual/ro/langref.php, accessed in February 2021.
  11. http://www.javascript.com, accessed in February 2021.
  12. http://codeigniter.com, accessed in January 2021.
  13. R.R. Al Hakim, E. Rusdi, M.A. Setiawan, Android Based Expert System Application for Diagnose COVID-19 Disease: Cases Study of Banyumas Regency, J. Int. Comp &He Inf, Vol.1(2), p.1-13, 2020.
  14. M.C. Popescu, V.E. Balas, L. Perescu, N. Mastorakis, Multilayer perceptron and Neural Networks, WSEAS Trans. Cir.and Sys., p.579-588, 2009.
  15. B. Befani, C. Elsenbroich, Diagnostic evaluation with simulated probabilities, Journal Evaluation, Vol. 27(1), p.102-115, 2021. https://doi.org/10.1177/1356389020980476
  16. A. Saxena, V, Kumar, Bayesian Kernel Methods: Applications in Medical Diagnosis Decision-Making Processes (A Case Study), Journal of Big Data and Analytics in Healthcare, Vol. 6(1), p.26-38, 2021. https://doi.org/10.4018/IJBDAH.20210101.oa3
  17. Y.R. Yue, M.A. Lindquist, J.M. Loh, Meta-Analysis of functional neuroimaging data using Bayesian nonparametric binary regression, The Annals of Applied Statistics, Vol. 6(2), p.697-718, 2012. https://doi.org/10.1214/11-AOAS523
  18. S.M. Fakhrahmad, M.H. Sadreddini, J.M. Zolghadri, A proposed expert system for word sense disambiguation: Deductive ambiguity resolution based on data mining and forward chaining, Expert Systems,Vol.32(2), p.178-191, 2015. https://doi.org/10.1111/exsy.12075
  19. F.M. Salman, S.S. Abu-Naser, Expert System for COVID-19 Diagnosis, International Journal of Academic Information Systems Research. Vol.4(3), p.1-13, 2020.
  20. R. Braniscan, M.C. Popescu, A. Naji, Secure PHP Open SSL Crypto Online Tool, International Journal of Advances in Computer Networks and ITS Security, Vol.5(2) NY, USA, p.108-112, 2015.