Нечеткие когнитивные карты: новый подход к моделированию в борьбе с COVID-19

Peter P. Groumpos

Аннотация


Коронавирусная болезнь, впервые обнаруженная в конце 2019 года, пандемия COVID-19 значительно повлияла на жизни всех людей во всем мире. В области здравоохранения широко используются математические модели для решения сложных медицинских проблем. Математические модели никому не могут быть полезны без конкретных теорий. Анализируется и обсуждается решающее и важное различие между корреляцией и причинно-следственной связью. Существует неопределенность и двусмысленность по некоторым аспектам пандемии COVID-19. Симптомов много, и они варьируются от человека к человеку и от географического региона к географическому региону. Анализируются актуальные проблемы пандемии COVID-19. Недавно новые мутации COVID-19 показали нам, как быстро новое заболевание может пустить новые корни и распространиться. Такие события сопровождаются бурным потоком клинической и эпидемиологической информации и исследований. Ежедневно предоставляются миллиарды данных. Многие теории призваны бороться с COVID-19, особенно с искусственным интеллектом (ИИ). Большинство методов, которые используются для обнаружения, диагностики и эпидемиологического прогнозирования, прогнозирования и социального контроля для борьбы с COVID-19, используют статистические теории и коэффициент корреляции. Последние теории нечетких когнитивных карт (FCM) используются для моделирования и изучения COVID-19. Приводятся и обсуждаются результаты моделирования с использованием реальных данных. Использование нечетких когнитивных карт (FCM) оказалось эффективным и полезным подходом в изучении COVID-19. Многие направления будущих исследований освещаются конкретным применением FCM в борьбе с COVID-19.

Ключевые слова


COVID-19; пандемия; корреляция; аутация, искусственный интеллект (ИИ); нечеткая логика; нечеткие когнитивные карты; биомедицинская информатика

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Литература


World Health Organization via http2. World Health Organization (2020) // WHO coronavirus disease (COVID19) dashboard. [Electronic resource]. URL: https://covid19.who.int/. (accessed 17 July 2020).

World Health Statistics 2019. World Health Organization. Geneva, 2019.

WHO, March 11, “announces COVID-19 outbreak a pandemic”. Nature, 2020.

Identifying SARS-CoV-2-related coronaviruses in Malayan pangolins / T. T-Y. Lam, et al. // Nature. 2020. Vol. 583, no. 7815. Pp.282-285.

Mahdy M. A. A., Younis W., Ewaida Z. An Overview of SARS-CoV-2 and Animal Infection // Frontiers in Veterinary Science. 2020. Vol. 7. Article number 596391.

Transmission of SARS-CoV-2 Lineage B.1.1.7 in England: insights from linking epidemiological and genetic data / E. Volz, et al. // Medrxiv. 2021

Adaptation of SARS-CoV-2 in BALB/c Mice for Testing Vaccine Efficacy / H. Gu, et al. // Science. 2020. Vol. 369,

Iss. 6511. Pp.1603-1607.

Deep mutational scanning of SARS-CoV-2 receptor binding domain reveals constraints on folding and ACE2 binding / T. N. Starr, et al. // Cell. 2020. Vol. 182, Iss. 5. Pp.1295-1310.

Major new lineages of SARS-CoV-2 emerge and spread in South Africa during lockdown / H. Tegally, et al. // Medrxiv. 2020.

Introduction of Brazilian SARS-CoV-2 484K.V2 related variants into the UK / O. T. R. Toovey, et al. // Journal of Infection. 2021. Vol. 82, Iss. 5. Pp. 23-24.

New delta variant studies show the pandemic is far from over / T. H. Saey, et al. SCIENCE NEWS, 2021.

How Dangerous Is the Delta Variant (B.1.617.2)? American Society for Microbiology, 2021.

Banks H. T., Tran H. T. Mathematical and Experimental Modeling of Physical and Biological Processes. Textbooks in Mathematics. 1st Edition. New York: CRC Publication, 2009. 298 p.

Cessenat M. Mathematical Modelling of Physical Systems. 1st Edition. Springer, 2018. 505 p.

Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment / M. Jamshidi, et al. // IEEE Access. 2020. Vol. 8. Pp. 109581-109595.

Application of Machine Learn-ing in Diagnosis of COVID-19 Through X-Ray and CT Images: A Scoping Review / H. Mohammad-Rahimi, et al. // Front. Cardiovasc. Med. 2021. Vol. 8. Article number 638011.

Tayarani N. M. H. Applications of artificial intelligence in battling against covid-19: A literature review // Chaos, Solitons and Fractals. 2021. Vol. 142. Article number 110338.

Kosko B. Fuzzy cognitive maps // International journal of man-machine studies. 1986. Vol. 24, Iss. 1. Pp. 65-75.

Groumpos P. P. Fuzzy Cognitive Maps: Basic Theories and Their application to Complex Systems / M. Glykas (ed.) // Fuzzy Cognitive Maps: Advances in Theory, Methodologies, Tools and Applications. Springer, 2010. Vol. 247. Pp. 1-22.

Groumpos P. P. Fuzzy cognitive maps: basic theories and their applications in medical problems // Proc. 19th Mediter-ranean Conference on Control & Automation (MED). 2011. Pp. 1490-1497.

Cardiovascular implications of fatal outcomes of patients with coronavirus disease 2019 (COVID-19) / T. Guoet, et al. // JAMA Cardiology. 2020. Vol. 5, no. 7. Pp. 811-818.

Granger C. W. J. Investigating Causal Relations by Economet-ric Models and Cross-spectral Methods // Econometrica. 1969. Vol. 37, no. 3. Pp. 424-438.

Rohrer J. M. Thinking Clearly About Correlations and Causa-tion: Graphical Causal Models for Observational Data // Advances in Methods and Practices in Psychological Sci-ence. 2018. Vol. 1, Iss. 1. Pp. 27-42. DOI: 10.1177/251524591774562 9.

Rodgers J. L., Nicewander W. A. Thirteen Ways to Look at the Correlation Coefficient // The American Statistician. 1988. Vol. 42, Iss. 1. Pp. 59-66.

Sheldon M. Ross Introduction to Probability and Statistics for Engineers and Scientists. 5th Edition. Elsevier: Academic Press, 2014. 60 p.

Cook T. D., Campbell D. T. Quasi Experimentation. Boston: Houghton Mifflin, 1979.

Groumpos P. P., Stylios C. D. Modeling supervisory control systems using fuzzy cognitive maps // Chaos Solitons & Fractals. 2000. Vol. 11, Iss. 1-3. Pp. 329-336.

Papageorgiou E. I., Stylios C. D., Groumpos P. P. Fuzzy Cognitive Map Learning based on Non-Linear Hebbian Rule // Advances in Artificial Intelligence Lecture Notes in Computer Science. 2003. Vol. 2903. Pp. 256-268.

Papageorgiou E. I., Stylios C. D., Groumpos P. P. Active Hebbian learning algorithm to train fuzzy cognitive maps // International Journal of Approximate Reasoning. 2004. Vol. 37, Iss. 3. Pp. 219-249.

Groumpos P. P. A new Mathematical Modell for COVID-19: A Fuzzy Cognitive Map Approach for Coronavirus Diseases // 11th International Conference on Information, Intelligence, Systems and Applications, (IISA, July 2020). 2020. Pp. 1-6.

Groumpos P. P. Modelling COVID-19 using Fuzzy Cognitive Maps (FCM) // EAI Endorsed on BEBI Transactions. 2021. Vol. 1, Iss. 2. Pp. 1-13.

Groumpos P. P. Intelligence and Fuzzy Cognitive Maps: Scientific Issues, Challenges and Opportunities // Studies in Informatics and Control. 2018. Vol. 27 (3). Pp. 247-264.

Groumpos P. P. Using Fuzzy Cognitive Maps in Analyzing and Studying International Economic and Political Stability // TECIS 2019, IFAC-PapersOnLine. 2019. Vol. 52, Iss. 25. Pp. 23-28.

Nguyen T. N. Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Direc-tions // TechRxiv. 2020. DOI: 10.13140/RG.2.2.36491.23846/1.

COVID-19 Prediction and Detection using Deep Learning / M. Alazab, et. al. // International Journal of Computer Information Systems and Industrial Management Applications. 2020. Vol. 12. Pp. 168-181.

Abdulkareem M., Petersen S. E. The Promise of AI in Detection, Diagnosis and Epidemiology for Combating COVID-19: Beyond the Hype // Frontiers in Artificial Intelligence. 2021. Vol. 4. Article number 652669.

Groumpos P. P. Deep Learning vs. Wise Learning: A Critical and Challenging Overview // IFAC-PapersOnLine. 2016. Vol. 49, Iss. 29. Pp. 180-189.

Wohl W. COVID-19 and Digital Transformation – Developing an Open Experimental Testbed for Sustainable and Innovative Environments (ETSIE) using Fuzzy Cognitive Maps // ArXiv preprint arXiv:2101.07509. 2021.

Multi-faceted impact and outcome of COVID-19 on smallholder agricultural systems: Integrating qualitative research and fuzzy cognitive mapping to explore resilient strategies / R. Goswami, et. al. // Agricultural Systems. 2021. Vol. 189. Article number 103051.

Abdulkareem M., Petersen S. E. The Promise of AI in Detection, Diagnosis and Epidemiology for Combating COVID-19: Beyond the Hype // Frontiers in Artificial Intelligence. 2021. Vol. 4. Article number 652669.

A review of fuzzy cognitive maps in medicine: Taxonomy, methods, and applications / A. Amirkhani, et al. // Computer methods and programs in biomedicine. 2017. Vol. 142. Pp. 129-145.

Alon I., Farrell M., Li S. Regime type and COVID‐19 response // FIIB Business Review. 2020. Vol. 9, Iss. 3. Pp. 152-160.

A New Approach of Dynamic Fuzzy Cognitive Knowledge Networks in Modelling Diagnosing Process of Meniscus Injury / A. P. Anninou, et al. // Elsevier Journal, IFAC-PapersOnLine. 2017. Vol. 50, Iss. 1. Pp. 5861-5866.

Apostolopoulos I. D., Mpesiana T. A. Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks // Phys. Eng. Sci. Med. 2020. Vol. 43, Iss. 2. Pp. 635-640.

Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus in Wuhan, China: A descriptive study / N. Chen, et al. // Lancet. 2020. Vol. 395, Iss. 10223. Pp. 507-513.

SARS and MERS: recent insights into emerging coronaviruses / E. de Wit, et al. // Nature Reviews Microbiology. 2016. Vol. 14. Pp. 523-527.

Hamet P., Tremblay J. Artificial intelligence in medi-cine // Metabolism. 2017. Vol. 69. Pp. S36-S40.

Online health survey research during COVID-19 / T. G. Hlatshwako, et. al. // The Lancet Digital Health. 2021. Vol. 3, Iss. 2. Pp. e76-e77.

Mintz Y., Brodie R. Introduction to artificial intelligence in medicine // Minimally Invasive Therapy Allied Technology. 2019. Vol. 28, Iss. 2. Pp. 73-81.

Mpelogianni V. G., Groumpos P. P. Re-approaching fuzzy cognitive maps to increase the knowledge of a system // AI & Society. 2018. Vol. 33, Iss. 2. Pp. 175-188.

Naudé W. Artificial Intelligence against COVID-19: An early review // IZA Inst. Labor Econ. 2020. No. 13110. 14 p.

Papageorgiou E. I. A new methodology for decisions in medical informatics using fuzzy cognitive maps based on fuzzy rule-extraction techniques // Applied Soft Computing. 2011. Vol. 11, Iss. 1. Pp. 500-513.

Raikov A. N., Avdeeva Z., Ermakov A. Big Data Refining on the Base of Cognitive Modeling // Proceedings of the 1st IFAC Conference on Cyber-Physical &Human-Systems, (Florianopolis, Brazil, Dec. 7-9). 2016. Pp. 147-152.

Raikov A., Ermakov A., Merkulov A. Self-organizing cognitive model synthesis with deep learning support // International Journal of Engineering &Technology (IJET). Special issue on Computing, Engineering and Information Technologies. 2018. Vol. 7, no. 2.28. Pp. 168-172.

Raikov A. Cognitive Semantics of Artificial Intelligence: A New Perspective. Topics: Computational Intelligence XVII. Singapore: Springer, 2021. 128 p.

Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China / D. Wang, et al. // Jama. 2020. Vol. 323, no. 11. Pp. 1061-1069.

Yan F., Robert M., Li Y. Statistical methods and common problems in medical or biomedical science research // International Journal of physiology, pathophysiology and pharmacology. 2017. Vol. 9 (5). Pp. 157-163.


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