The role of Artificial Intelligence in Management of Critical COVID-19 patients
Journal of Cellular & Molecular Anesthesia,
Vol. 5 No. 1 (2020),
Background: the COVID-19 outbreak has created a great challenge for the healthcare system worldwide. One of the most critical points of this challenge is the management of COVID-19 patients needing acute and/or critical respiratory care. This study was performed to discover an AI based model to improve the critical care of the COVID-19 patients.
Material and methods: in a descriptive study, all the published research available in PubMed, Web of Science, Google scholar and other databases were retrieved. Based on these studies, a three stage model of input, process and output was created.
Results: the three stage model of AI application in ICU was completed. Input included Clinical, Paraclinical, Personalized Medicine (OMICS) and Epidemiologic data. The process included Artificial Intelligence (i.e. Artificial Neural Network, Machine Learning, Deep Learning and Expert Systems). The output which was ICU Decision Making included Diagnosis, Treatment, Risk Stratification, Prognosis and Management.
Conclusion: the efforts of the healthcare system to defeat COVID-19 could be supported by an AI-based decision-making system which would double them up and help manage these patients much more efficiently, especially those in COVID-19 ICU
- artificial intelligence
- critical care.
How to Cite
Hui DS, E IA, Madani TA, Ntoumi F, Kock R, Dar O, et al. The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health - The latest 2019 novel coronavirus outbreak in Wuhan, China. Int J Infect Dis. 2020;91:264-6.
Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al. A Novel Coronavirus from Patients with Pneumonia in China, 2019. N Engl J Med. 2020;382(8):727-33.
Wu F, Zhao S, Yu B, Chen YM, Wang W, Song ZG, et al. A new coronavirus associated with human respiratory disease in China. Nature. 2020;579(7798):265-9.
Paules CI, Marston HD, Fauci AS. Coronavirus Infections-More Than Just the Common Cold. JAMA. 2020.
Takian A, Raoofi A, Kazempour-Ardebili S. COVID-19 battle during the toughest sanctions against Iran. Lancet. 2020:1.
Namendys-Silva SA. Respiratory support for patients with COVID-19 infection. Lancet Respir Med. 2020.
Keuning BE, Kaufmann T, Wiersema R, Granholm A, Pettilä V, Møller M, et al. Mortality prediction models in the adult critically ill: A scoping review. Acta Anaesthesiol Scand. 2020;64(4):424-42.
Alimadadi A, Aryal S, Manandhar I, Munroe PB, Joe B, Cheng X. Artificial Intelligence and Machine Learning to Fight COVID-19. Physiol Genomics. 2020.
Zhang DH, Wu KL, Zhang X, Deng SQ, Peng B. In silico screening of Chinese herbal medicines with the potential to directly inhibit 2019 novel coronavirus. J Integr Med. 2020;18(2):152-8.
Rao A, Vazquez J. Identification of COVID-19 Can be Quicker through Artificial Intelligence framework using a Mobile Phone-Based Survey in the Populations when Cities/Towns Are Under Quarantine. Infect Control Hosp Epidemiol. 2020:1-18.
Alexis Ruiz A, Wyszynska PK, Laudanski K. Narrative Review of Decision-Making Processes in Critical Care. Anesth Analg. 2019;128(5):962-70.
Mathur P, Burns ML. Artificial Intelligence in Critical Care. Int Anesthesiol Clin. 2019;57(2):89-102.
Chapalain X, Huet O. Is artificial intelligence (AI) at the doorstep of Intensive Care Units (ICU) and operating room (OR)? Anaesth Crit Care Pain Med. 2019;38(4):337-8.
Gutierrez G. Artificial Intelligence in the Intensive Care Unit. Crit Care. 2020;24(1):101.
Mupparapu M, Wu CW, Chen YC. Artificial intelligence, machine learning, neural networks, and deep learning: Futuristic concepts for new dental diagnosis. Quintessence Int. 2018;49(9):687-8.
Bini SA. Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Computing: What Do These Terms Mean and How Will They Impact Health Care? J Arthroplasty. 2018;33(8):2358-61.
Lin E, Tsai SJ. Machine Learning in Neural Networks. Adv Exp Med Biol. 2019;1192:127-37.
Hashimoto DA, Witkowski E, Gao L, Meireles O, Rosman G. Artificial Intelligence in Anesthesiology: Current Techniques, Clinical Applications, and Limitations. Anesthesiology. 2020;132(2):379-94.
Ghassemi M, Celi LA, Stone DJ. State of the art review: the data revolution in critical care. Crit Care. 2015;19:118.
Cosgriff CV, Celi LA, Stone DJ. Critical Care, Critical Data. Biomed Eng Comput Biol. 2019;10:1179597219856564.
Pino Pena I, Cheplygina V, Paschaloudi S, Vuust M, Carl J, Weinreich UM, et al. Automatic emphysema detection using weakly labeled HRCT lung images. PLoS One. 2018;13(10):e0205397.
Badnjevic A, Gurbeta L, Custovic E. An Expert Diagnostic System to Automatically Identify Asthma and Chronic Obstructive Pulmonary Disease in Clinical Settings. Sci Rep. 2018;8(1):11645.
Cismondi F, Celi LA, Fialho AS, Vieira SM, Reti SR, Sousa JM, et al. Reducing unnecessary lab testing in the ICU with artificial intelligence. Int J Med Inform. 2013;82(5):345-58.
Robson B. Computers and viral diseases. Preliminary bioinformatics studies on the design of a synthetic vaccine and a preventative peptidomimetic antagonist against the SARS-CoV-2 (2019-nCoV, COVID-19) coronavirus. Comput Biol Med. 2020;119:103670.
Tuite AR, Bogoch, II, Sherbo R, Watts A, Fisman D, Khan K. Estimation of Coronavirus Disease 2019 (COVID-19) Burden and Potential for International Dissemination of Infection From Iran. Ann Intern Med. 2020.
Bogoch, II, Watts A, Thomas-Bachli A, Huber C, Kraemer MUG, Khan K. Potential for global spread of a novel coronavirus from China. J Travel Med. 2020;27(2).
Fraser C, Donnelly CA, Cauchemez S, Hanage WP, Van Kerkhove MD, Hollingsworth TD, et al. Pandemic potential of a strain of influenza A (H1N1): early findings. Science. 2009;324(5934):1557-61.
Santosh KC. AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data. J Med Syst. 2020;44(5):93.
Husnayain A, Fuad A, Su EC. Applications of google search trends for risk communication in infectious disease management: A case study of COVID-19 outbreak in Taiwan. Int J Infect Dis. 2020.
Tarnok A. Machine Learning, COVID-19 (2019-nCoV), and multi-OMICS. Cytometry A. 2020;97(3):215-6.
Grapov D, Fahrmann J, Wanichthanarak K, Khoomrung S. Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine. Omics. 2018;22(10):630-6.
Ahmed Z, Mohamed K, Zeeshan S, Dong X. Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database (Oxford). 2020;2020.
Perakakis N, Yazdani A, Karniadakis GE, Mantzoros C. Omics, big data and machine learning as tools to propel understanding of biological mechanisms and to discover novel diagnostics and therapeutics. Metabolism. 2018;87:A1-a9.
Olivier M, Asmis R, Hawkins G, Howard T, Cox L. The Need for Multi-Omics Biomarker Signatures in Precision Medicine. Int J Mol Sci. 2019;20(19).
Guo YR, Cao QD, Hong ZS, Tan YY, Chen SD, Jin HJ, et al. The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak - an update on the status. Mil Med Res. 2020;7(1):11.
Lauer SA, Grantz KH, Bi Q, Jones FK, Zheng Q, Meredith HR, et al. The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Ann Intern Med. 2020.
Davoudi A, Malhotra KR, Shickel B, Siegel S, Williams S, Ruppert M, et al. Intelligent ICU for Autonomous Patient Monitoring Using Pervasive Sensing and Deep Learning. Sci Rep. 2019;9(1):8020.
Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W, et al. Correlation of Chest CT and RT-PCR Testing in Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases. Radiology. 2020:200642.
Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, et al. Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT. Radiology. 2020:200905.
Wells A, Heckerman D, Torkamani A, Yin L, Sebat J, Ren B, et al. Ranking of non-coding pathogenic variants and putative essential regions of the human genome. Nat Commun. 2019;10(1):5241.
Parreco J, Hidalgo A, Parks JJ, Kozol R, Rattan R. Using artificial intelligence to predict prolonged mechanical ventilation and tracheostomy placement. J Surg Res. 2018;228:179-87.
Connor CW. Artificial Intelligence and Machine Learning in Anesthesiology. Anesthesiology. 2019;131(6):1346-59.
Shafaf N, Malek H. Applications of Machine Learning Approaches in Emergency Medicine; a Review Article. Arch Acad Emerg Med. 2019;7(1):34.
Matava C, Pankiv E, Ahumada L, Weingarten B, Simpao A. Artificial intelligence, machine learning and the pediatric airway. Paediatr Anaesth. 2019.
Moustafa M, El-Metainy S, Mahar K, Mahmoud Abdel-magied E. Defining difficult laryngoscopy findings by using multiple parameters: A machine learning approach. Egypt J Anaesth. 2017;33(2):153-8.
Zali H, Golchin A, Farahani M, Yazdani M, Ranjbard Mm, Dabbagh A. FDA approved drugs repurposing of Toll-like receptor4 (TLR4) candidate for neuropathy. Iran J Pharm Res. 2019:-.
Ortega JT, Serrano ML, Pujol FH, Rangel HR. Role of changes in SARS-CoV-2 spike protein in the interaction with the human ACE2 receptor: An in silico analysis. Excli J. 2020;19:410-7.
He Y, Xiang Z, Mobley HL. Vaxign: the first web-based vaccine design program for reverse vaccinology and applications for vaccine development. J Biomed Biotechnol. 2010;2010:297505.
He Y, Xiang Z. Databases and in silico tools for vaccine design. Methods Mol Biol. 2013;993:115-27.
Ong E, Wong M, Huffman A, He Y. COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning. bioRxiv. 2020;March 23, 2020:2020.03.20.000141.
Das N, Topalovic M, Janssens W. Artificial intelligence in diagnosis of obstructive lung disease: current status and future potential. Curr Opin Pulm Med. 2018;24(2):117-23.
Mekov E, Miravitlles M, Petkov R. Artificial intelligence and machine learning in respiratory medicine. Expert Rev Respir Med. 2020:1-6.
Wong ZSY, Zhou J, Zhang Q. Artificial Intelligence for infectious disease Big Data Analytics. Infect Dis Health. 2019;24(1):44-8.
Malafeev A, Laptev D, Bauer S, Omlin X, Wierzbicka A, Wichniak A, et al. Automatic Human Sleep Stage Scoring Using Deep Neural Networks. Front Neurosci. 2018;12:781.
Abiodun OI, Jantan A, Omolara AE, Dada KV, Mohamed NA, Arshad H. State-of-the-art in artificial neural network applications: A survey. Heliyon. 2018;4(11):e00938.
Correa M, Zimic M, Barrientos F, Barrientos R, Roman-Gonzalez A, Pajuelo MJ, et al. Automatic classification of pediatric pneumonia based on lung ultrasound pattern recognition. PLoS One. 2018;13(12):e0206410.
- Abstract Viewed: 2727 times
- PDF Downloaded: 1388 times