FACILITATION AND ACCELERATION OF DIAGNOSIS AND TREATMENT OF COVID-19 USING AN EXPERT SYSTEM
Asian Journal of Mathematics and Computer Research,
Page 53-61
DOI:
10.56557/ajomcor/2022/v29i17767
Abstract
This study examines the forward method for establishing an expert system for the diagnosis of COVID-19 disease in the Python environment. To do this, first the tree related to this expert system is drawn. The laws of Knowledge Tree in expert system is designed to treat all known symptoms for the diagnosis of COVID-19, including severe fever and headache, dry cough, losing the sense of smell and taste, fatigue, sore throat and shortness of breath (asthma). Based on the drawn tree, the studied expert system is designed considering its general architecture consisting of inference engine, knowledge base, and result description, working memory, external programs, constructor interfaces and user interfaces. This in turn will be a good supportive aspect along with the methods of diagnosis by the physician, PCR, CT scan and serology to be effective in facilitating and accelerating the diagnosis and treatment.
Keywords:
- Expert system
- knowledge base
- COVID-19
- backward-chaining and forward-chaining
How to Cite
HASAN-ZADEH, A., & FARAJI, M. (2022). FACILITATION AND ACCELERATION OF DIAGNOSIS AND TREATMENT OF COVID-19 USING AN EXPERT SYSTEM. Asian Journal of Mathematics and Computer Research, 29(1), 53-61. https://doi.org/10.56557/ajomcor/2022/v29i17767
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Jain MS, Barhate SD. Corona viruses are a family of viruses that range from the common cold to MERS corona virus: A review. Asian Journal of Research in Pharmaceutical Science. 2020;10(3):204-210.
Liu Y, He Z, Zhou X. Space-time variation and spatial differentiation of COVID-19 confirmed cases in Hubei province based on extended GWR. ISPRS International Journal of Geo-Information. 2020;9(9):536.
Callaway E. The coronavirus is mutating--does it matter?. Nature. 2020;585(7824):174-178.
Yang Y, Peng F, Wang R, Guan K, Jiang T, Xu G, Chang C. The deadly coronaviruses: The 2003 SARS pandemic and the 2020 novel coronavirus epidemic in China. Journal of autoimmunity. 2020;109:102434.
Park SE. Epidemiology, virology, and clinical features of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2; Coronavirus Disease-19). Clinical and experimental pediatrics. 2020;63(4):119.
Ganjali S, Bianconi V, Penson PE, Pirro M, Banach M, Watts GF, Sahebkar A. Commentary: Statins, COVID-19, and coronary artery disease: Killing two birds with one stone. Metabolism-Clinical and Experimental. 2020;113.
Zhang C, Wu Z, Li JW, Zhao H, Wang GQ. Cytokine release syndrome in severe COVID-19: interleukin-6 receptor antagonist tocilizumab may be the key to reduce mortality. International Journal of Antimicrobial Agents. 2020;55(5):105954.
Venter M, Richter K. Towards effective diagnostic assays for COVID-19: A review. Journal of clinical pathology. 2020;73(7):370-377,
Wijaya YOS, Nishio H, Niba ETE, Shiroshita T, Kato M, Bouike Y, Shinohara M. Dried blood spot screening system for spinal muscular atrophy with allele-specific polymerase chain reaction and melting peak analysis. Genetic Testing and Molecular Biomarkers. 2021;25(4):293-301.
Infantino M, Manfredi M, Grossi V, Lari B, Fabbri S, Benucci M, Pesce G. Closing the serological gap in the diagnostic testing for COVID‐19: The value of anti‐SARS‐CoV‐2 IgA antibodies. Journal of Medical Virology. 2021;93(3):1436-1442.
Lin A, Kolossváry M, Motwani M, Išgum I, Maurovich-Horvat P, Slomka PJ, Dey D. Artificial intelligence in cardiovascular imaging for risk stratification in coronary artery disease. Radiology Cardiothoracic Imaging. 2021;3(1):e200512.
Liebowitz J. Expert systems: A short introduction. Engineering Fracture Mechanics. 1995;50(5-6):601-607.
Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science. 2015;349(6245):255-260.
Feigenbaum E, Buchanan B. Dendral and meta-dendral: Roots of knowledge systems and expert system applications”, Artif. Intell. 1994;59(1-2):233-240.
Miozzo M, Soete L. Internationalization of services: A technological perspective. Technological Forecasting and Social Change. 2001;67(2-3):159-185.
Duda RO, Shortliffe EH. Expert systems research. Science. 1983;220(4594):261-268.
Wamba-Taguimdje SL, Wamba SF, Kamdjoug JRK, Wanko CET. Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business Process Management Journal. 2020;26(7):1893-1924.
Mohammed AA, Ambak K, Mosa A, Syamsunur D. Expert system in engineering transportation: A review Journal of Engineering Science and Technology. 2019;14(1):229-252.
Lehmann J, Isele R, Jakob M, Jentzsch A, Kontokostas D, Mendes PN, Bizer C. Dbpedia–A large-scale, multilingual knowledge base extracted from Wikipedia. Semantic web. 2015;6(2):167-195.
Richards D. A social software/Web 2.0 approach to collaborative knowledge engineering. Information Sciences. 2009;179(15):2515-2523.
Vastrik I, D'Eustachio P, Schmidt E, Joshi-Tope G, Gopinath G, Croft D, Stein L. Reactome: A knowledge base of biologic pathways and processes. Genome biology. 2007;8(3):1-13.
Kolodner J. Retrieval and organizational strategies in conceptual memory (PLE: Memory): A computer model, Psychology Press; 2014.
Hiebert J, Gallimore R, Stigler JW. A knowledge base for the teaching profession: What would it look like and how can we get one?. Educational researcher. 2002;31(5):3-15.
Nunes I, Jannach D. A systematic review and taxonomy of explanations in decision support and recommender systems. User Modeling and User-Adapted Interaction. 2017;27(3):393-444.
Lan H, Ding Y, Hong J. Decision support system for rapid prototyping process selection through integration of fuzzy synthetic evaluation and an expert system. International Journal of Production Research. 2005;43(1):169-194.
Kreimeyer K, Foster M, Pandey A, Arya N, Halford G, Jones SF, Botsis T. Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review. Journal of biomedical informatics. 2017;73:14-29.
Davis E. Representations of commonsense knowledge, Morgan Kaufmann; 2014.
Ceri S, Gottlob G, Tanca L. What you always wanted to know about Datalog (and never dared to ask). IEEE transactions on knowledge and data engineering. 1989;1(1):146-166.
Nugroho AC. Expert system development for course enrollment process using ripple down rules in a University in Surabaya. ComTech: Computer Mathematics and Engineering Applications. 2019;10(1):1-7.
Li W, Chen J. Backward integration strategy in a retailer Stackelberg supply chain. Omega. 2018;75:118-130.
Vrugt JA, Ter Braak CJ, Clark MP, Hyman JM, Robinson BA. Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation. Water Resources Research. 2008;44(12).
Hopgood AA. The state of artificial intelligence. Advances in Computers. 2005;65:1-75.
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