DOI: https://doi.org/10.15587/2312-8372.2018.128455

Development of medical diagnostic decision support systems and their economic efficiency

Olga Kravchenko

Abstract


The object of research is the diagnostic decision support system (DSS). One of the most problematic areas in medical diagnostic systems is the formation of a knowledge base based on expert rules, which provides a recommendation for the disease. The methods of designing medical diagnostic systems have been studied. Methods for applying the potential of artificial intelligence in medicine in the form of fuzzy rules or conducting diagnostics on the basis of Bayesian networks are considered. Intellectual computing tools in the form of expert systems based on rules and fuzzy logic, applied to neural networks and genetic algorithms performed in medical diagnostics are considered.

To develop a decision support system for a pediatrician, a method to build a knowledge base on the basis of logical rules «If ..., then ...» was chosen. Using this method allows to create initial conditions for input data in the system, and speed up their processing in the knowledge base. Although the knowledge base is quite cumbersome, this does not reduce the performance of the system.

In the process of research, the development of a medical diagnostic system for decision support by a pediatrician for the design stages is described. The application of this system allows to automate the process of document circulation for a pediatrician and to speed up the stage of preliminary assessment of the patient's condition.

The built-in pediatrician electronic pediatric module not only automates the workflow process, reduces the doctor's work time with papers, but also allows to obtain complete information about the patient.

The calculation of economic efficiency from the DSS introduction by a pediatrician is performed. The system cost is to be recouped within 1 year.

The prospect of adding modules to the system for individual diseases and forming an electronic record from the moment of birth with the prospect of transferring data to the system for adults are advantages over analogues of this software product.


Keywords


medical decision support systems; software development; economic efficiency

References


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GOST Style Citations


Building a Better Delivery System A New Engineering / ed. by Reid P. P., Compton W. D., Grossman J. H., Fanjiang G. Washington: National Academies Press, 2005. 276 p. doi:10.17226/11378 

Kitporntheranunt M., Wiriyasuttiwong W. Development of a Medical Expert System for the Diagnosis of Ectopic Pregnancy // Journal of the Medical Association of Thailand. 2010. Vol. 93, No. 2. P. 43–49.

Milho I., Fred A. A User-Friendly Development Tool for Medical Diagnosis Based on Bayesian Networks // Enterprise Information Systems II. Dordrecht: Springer, 2001. P. 113–118. doi:10.1007/978-94-017-1427-3_16 

Reddy K. Developing Reliable Clinical Diagnosis Support System. 2009. 56 p. URL: http://www.kiranreddys.com/articles/clinicaldiagnosissupportsystems.pdf

Kovalchuk O. Ya., Ivanytskyi R. I. Ekspertni systemy v medytsyni. Ternopil: Ternopilska derzhavna medychna akademiia imeni I. Ya. Horbachevskoho, 2004. URL: http://studcon.org/perspektyvy-rozvytku-medychnyh-informaciynyh-system

Musabekova L. M., Irsimbetova A. I. Overview of methods and tools for the expert systems in medicine // Eurasian Economic Club of Scientists Association. February 20, 2017. URL: http://group-global.org/en/node/58678

A knowledge-based system for tutoring bronchial asthma diagnosis: proceedings / Prasad B. et al. // Second Annual IEEE Symposium on Computer-Based Medical Systems. 1989. doi:10.1109/cbmsys.1989.47356 

Bursuk E., Ozkan M., Ilerigelen B. A medical expert system in cardiological diseases: proceedings // IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Annual Fall Meeting of the Biomedical Engineering Society. 1999. doi:10.1109/iembs.1999.804376 

Expert system for early diagnosis of eye diseases infecting the Malaysian population: proceedings / Ibrahim F. et al. // IEEE Region 10 International Conference on Electrical and Electronic Technology. TENCON 2001. 2001. doi:10.1109/tencon.2001.949629 

Gebremariam S. A Self Learning Knowledge Based System for Diagnosis and Treatment of Diabetes: Master’s thesis. Ethiopia: Addis Ababa University. URL: http://etd.aau.edu.et/handle/123456789/8770

Fatima B., Amine C. M. A Neuro-Fuzzy Inference Model for Breast Cancer Recognition // International Journal of Computer Science and Information Technology. 2012. Vol. 4, No. 5. P. 163–173. doi:10.5121/ijcsit.2012.4513 

Singla J., Grover D., Bhandari A. Medical Expert Systems for Diagnosis of Various Diseases // International Journal of Computer Applications. 2014. Vol. 93, No. 7. P. 36–43. doi:10.5120/16230-5717 

SushilSikchi S., Sikchi S., Ali M. S. Artificial Intelligence in Medical Diagnosis // International Journal of Applied Engineering Research. 2012. Vol. 7, No. 11. URL: https://pdfs.semanticscholar.org/5bf4/2fe6806ac76065dea9db434c0f8acb5034ef.pdf

Medical Diagnosis: Are Artificial Intelligence Systems Able to Diagnose the Underlying Causes of Specific Headaches?: proceedings / Farrugia A. et al. // Developments in eSystems Engineering. 2013. doi:10.1109/dese.2013.72 

Veres O. M. Otsiniuvannia proektu systemy pidtrymky pryiniattia rishen // Visnyk Natsionalnoho universytetu «Lvivska politekhnika». Informatsiini systemy ta merezhi. 2010. Vol. 673. P. 69–77.

Oksamytna L. P., Kravchenko O. V. Rozrobka avtomatyzovanoi systemy obliku medychnykh doslidzhen // Visnyk Cherkaskoho tekhnolohichnoho universytetu. Seriia: Tekhnichni nauky. 2016. Vol. 4. P. 46–52.

Ekspertna systema MYCIN. URL: http://www.aiportal.ru/articles/expert-systems/expert-systems.html

Skryninhovi kompiuterni diahnostychni systemy. URL: http://pdnr.ru/d155912.html







Copyright (c) 2018 Olga Kravchenko

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ISSN (print) 2664-9969, ISSN (on-line) 2706-5448