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03.08.2020 | COVID-19 | Mobile & Wireless Health Zur Zeit gratis

Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review

verfasst von: Agam Bansal, Rana Prathap Padappayil, Chandan Garg, Anjali Singal, Mohak Gupta, Allan Klein

Erschienen in: Journal of Medical Systems | Ausgabe 9/2020

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Abstract

The term machine learning refers to a collection of tools used for identifying patterns in data. As opposed to traditional methods of pattern identification, machine learning tools relies on artificial intelligence to map out patters from large amounts of data, can self-improve as and when new data becomes available and is quicker in accomplishing these tasks. This review describes various techniques of machine learning that have been used in the past in the prediction, detection and management of infectious diseases, and how these tools are being brought into the battle against COVID-19. In addition, we also discuss their applications in various stages of the pandemic, the advantages, disadvantages and possible pit falls.
Literatur
1.
Zurück zum Zitat Jones KE, Patel NG, Levy MA, Storeygard A, Balk D, Gittleman JL, et al. Global trends in emerging infectious diseases. Nature. 2008;451(7181):990–3.PubMedPubMedCentral Jones KE, Patel NG, Levy MA, Storeygard A, Balk D, Gittleman JL, et al. Global trends in emerging infectious diseases. Nature. 2008;451(7181):990–3.PubMedPubMedCentral
2.
Zurück zum Zitat Wiens J, Shenoy ES. Machine Learning for Healthcare: On the verge of a major shift in healthcare epidemiology. Clin. Infect Dis. Off Publ. Infect Dis. Soc. Am. 2018 Jan 1;66(1):149–53. Wiens J, Shenoy ES. Machine Learning for Healthcare: On the verge of a major shift in healthcare epidemiology. Clin. Infect Dis. Off Publ. Infect Dis. Soc. Am. 2018 Jan 1;66(1):149–53.
5.
Zurück zum Zitat Al-Garadi MA, Khan MS, Varathan KD, Mujtaba G, Al-Kabsi AM. Using online social networks to track a pandemic: A systematic review. J. Biomed. Inform. 2016;62:1–11.PubMed Al-Garadi MA, Khan MS, Varathan KD, Mujtaba G, Al-Kabsi AM. Using online social networks to track a pandemic: A systematic review. J. Biomed. Inform. 2016;62:1–11.PubMed
6.
Zurück zum Zitat McCall B. COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread. Lancet Digit Health. 2020 Apr 1;2(4):e166–7.PubMedPubMedCentral McCall B. COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread. Lancet Digit Health. 2020 Apr 1;2(4):e166–7.PubMedPubMedCentral
8.
Zurück zum Zitat Funk S, Camacho A, Kucharski AJ, Eggo RM, Edmunds WJ. Real-time forecasting of infectious disease dynamics with a stochastic semi-mechanistic model. Epidemics. 2018 Mar;22:56–61.PubMedPubMedCentral Funk S, Camacho A, Kucharski AJ, Eggo RM, Edmunds WJ. Real-time forecasting of infectious disease dynamics with a stochastic semi-mechanistic model. Epidemics. 2018 Mar;22:56–61.PubMedPubMedCentral
9.
Zurück zum Zitat Johansson MA, Apfeldorf KM, Dobson S, Devita J, Buczak AL, Baugher B, et al. An open challenge to advance probabilistic forecasting for dengue epidemics. Proc. Natl. Acad. Sci. 2019 Nov 26;116(48):24268–74.PubMedPubMedCentral Johansson MA, Apfeldorf KM, Dobson S, Devita J, Buczak AL, Baugher B, et al. An open challenge to advance probabilistic forecasting for dengue epidemics. Proc. Natl. Acad. Sci. 2019 Nov 26;116(48):24268–74.PubMedPubMedCentral
10.
Zurück zum Zitat Teng Y, Bi D, Xie G, Jin Y, Huang Y, Lin B, et al. Dynamic Forecasting of Zika Epidemics Using Google Trends. PLoS One 2017 Jan 6;12(1):e0165085.PubMedPubMedCentral Teng Y, Bi D, Xie G, Jin Y, Huang Y, Lin B, et al. Dynamic Forecasting of Zika Epidemics Using Google Trends. PLoS One 2017 Jan 6;12(1):e0165085.PubMedPubMedCentral
12.
Zurück zum Zitat Lim S, Tucker CS, Kumara S. An unsupervised machine learning model for discovering latent infectious diseases using social media data. J. Biomed. Inform. 2017 Feb 1;66:82–94.PubMed Lim S, Tucker CS, Kumara S. An unsupervised machine learning model for discovering latent infectious diseases using social media data. J. Biomed. Inform. 2017 Feb 1;66:82–94.PubMed
13.
Zurück zum Zitat Choi S, Lee J, Kang M-G, Min H, Chang Y-S, Yoon S. Large-scale machine learning of media outlets for understanding public reactions to nation-wide viral infection outbreaks. Methods San Diego Calif. 2017 01;129:50–9. Choi S, Lee J, Kang M-G, Min H, Chang Y-S, Yoon S. Large-scale machine learning of media outlets for understanding public reactions to nation-wide viral infection outbreaks. Methods San Diego Calif. 2017 01;129:50–9.
14.
Zurück zum Zitat Wirz CD, Xenos MA, Brossard D, Scheufele D, Chung JH, Massarani L. Rethinking social amplification of risk: Social media and Zika in three languages. Risk Anal. Off Publ. Soc. Risk. Anal. 2018;38(12):2599–624. Wirz CD, Xenos MA, Brossard D, Scheufele D, Chung JH, Massarani L. Rethinking social amplification of risk: Social media and Zika in three languages. Risk Anal. Off Publ. Soc. Risk. Anal. 2018;38(12):2599–624.
15.
Zurück zum Zitat Gui X, Wang Y, Kou Y, Reynolds TL, Chen Y, Mei Q, et al. Understanding the Patterns of Health Information Dissemination on Social Media during the Zika Outbreak. AMIA Annu. Symp. Proc. AMIA Symp. 2017;2017:820–9.PubMed Gui X, Wang Y, Kou Y, Reynolds TL, Chen Y, Mei Q, et al. Understanding the Patterns of Health Information Dissemination on Social Media during the Zika Outbreak. AMIA Annu. Symp. Proc. AMIA Symp. 2017;2017:820–9.PubMed
17.
Zurück zum Zitat Venna SR, Tavanaei A, Gottumukkala RN, Raghavan VV, Maida AS, Nichols S. A Novel Data-Driven Model for Real-Time Influenza Forecasting. IEEE Access. 2019;7:7691–701. Venna SR, Tavanaei A, Gottumukkala RN, Raghavan VV, Maida AS, Nichols S. A Novel Data-Driven Model for Real-Time Influenza Forecasting. IEEE Access. 2019;7:7691–701.
18.
Zurück zum Zitat Riad MH, Sekamatte M, Ocom F, Makumbi I, Scoglio CM. Risk assessment of Ebola virus disease spreading in Uganda using a two-layer temporal network. Sci. Rep. 2019 Nov 5;9(1):1–17. Riad MH, Sekamatte M, Ocom F, Makumbi I, Scoglio CM. Risk assessment of Ebola virus disease spreading in Uganda using a two-layer temporal network. Sci. Rep. 2019 Nov 5;9(1):1–17.
20.
Zurück zum Zitat Gambhir M, Bozio C, O’Hagan JJ, Uzicanin A, Johnson LE, Biggerstaff M, et al. Infectious disease modeling methods as tools for informing response to novel influenza viruses of unknown pandemic potential. Clin Infect Dis Off Publ Infect Dis Soc Am. 2015 May 1;60 Suppl 1:S11-19. Gambhir M, Bozio C, O’Hagan JJ, Uzicanin A, Johnson LE, Biggerstaff M, et al. Infectious disease modeling methods as tools for informing response to novel influenza viruses of unknown pandemic potential. Clin Infect Dis Off Publ Infect Dis Soc Am. 2015 May 1;60 Suppl 1:S11-19.
21.
Zurück zum Zitat Rasmussen SA, Redd SC. Using results from infectious disease modeling to improve the response to a potential H7N9 influenza pandemic. Clin. Infect Dis. Off Publ. Infect. Dis. Soc. Am. 2015 May 1;60 Suppl 1:S9-10. Rasmussen SA, Redd SC. Using results from infectious disease modeling to improve the response to a potential H7N9 influenza pandemic. Clin. Infect Dis. Off Publ. Infect. Dis. Soc. Am. 2015 May 1;60 Suppl 1:S9-10.
22.
Zurück zum Zitat Koo JR, Cook AR, Park M, Sun Y, Sun H, Lim JT, et al. Interventions to mitigate early spread of SARS-CoV-2 in Singapore: a modelling study. Lancet Infect. Dis. 2020 Mar 23; Koo JR, Cook AR, Park M, Sun Y, Sun H, Lim JT, et al. Interventions to mitigate early spread of SARS-CoV-2 in Singapore: a modelling study. Lancet Infect. Dis. 2020 Mar 23;
23.
Zurück zum Zitat Binti Hamzah F, Lau C, Nizri H, Ligot D, Lee G, Tan C, et al. CoronaTracker: Worldwide COVID-19 Outbreak Data Analysis and Prediction. Bull. World Health Organ. 2020 Mar 19; Binti Hamzah F, Lau C, Nizri H, Ligot D, Lee G, Tan C, et al. CoronaTracker: Worldwide COVID-19 Outbreak Data Analysis and Prediction. Bull. World Health Organ. 2020 Mar 19;
24.
Zurück zum Zitat Wu JT, Leung K, Leung GM. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. Lancet 2020 Feb 29;395(10225):689–97.PubMedPubMedCentral Wu JT, Leung K, Leung GM. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. Lancet 2020 Feb 29;395(10225):689–97.PubMedPubMedCentral
25.
Zurück zum Zitat Zhao S, Musa SS, Lin Q, Ran J, Yang G, Wang W, et al. Estimating the Unreported Number of Novel Coronavirus (2019-nCoV) Cases in China in the First Half of January 2020: A Data-Driven Modelling Analysis of the Early Outbreak. J. Clin. Med. 2020 Feb;9(2):388.PubMedCentral Zhao S, Musa SS, Lin Q, Ran J, Yang G, Wang W, et al. Estimating the Unreported Number of Novel Coronavirus (2019-nCoV) Cases in China in the First Half of January 2020: A Data-Driven Modelling Analysis of the Early Outbreak. J. Clin. Med. 2020 Feb;9(2):388.PubMedCentral
26.
Zurück zum Zitat Karako K, Song P, Chen Y, Tang W. Analysis of COVID-19 infection spread in Japan based on stochastic transition model. Biosci. Trends. 2020;advpub. Karako K, Song P, Chen Y, Tang W. Analysis of COVID-19 infection spread in Japan based on stochastic transition model. Biosci. Trends. 2020;advpub.
27.
Zurück zum Zitat Zhang P, Chen B, Ma L, Li Z, Song Z, Duan W, et al. The large scale machine learning in an artificial society: Prediction of the ebola outbreak in beijing [Internet]. Vol. 2015, Computational Intelligence and Neuroscience. Hindawi; 2015 [cited 2020 Mar 29]. p. e531650. Available from: https://www.hindawi.com/journals/cin/2015/531650/ Zhang P, Chen B, Ma L, Li Z, Song Z, Duan W, et al. The large scale machine learning in an artificial society: Prediction of the ebola outbreak in beijing [Internet]. Vol. 2015, Computational Intelligence and Neuroscience. Hindawi; 2015 [cited 2020 Mar 29]. p. e531650. Available from: https://​www.​hindawi.​com/​journals/​cin/​2015/​531650/​
28.
Zurück zum Zitat Akhtar M, Kraemer MUG, Gardner LM. A dynamic neural network model for predicting risk of Zika in real time. BMC Med. 2019 Sep 2;17(1):171.PubMedPubMedCentral Akhtar M, Kraemer MUG, Gardner LM. A dynamic neural network model for predicting risk of Zika in real time. BMC Med. 2019 Sep 2;17(1):171.PubMedPubMedCentral
29.
Zurück zum Zitat Jiang D, Hao M, Ding F, Fu J, Li M. Mapping the transmission risk of Zika virus using machine learning models. Acta Trop. 2018 Sep;185:391–9.PubMed Jiang D, Hao M, Ding F, Fu J, Li M. Mapping the transmission risk of Zika virus using machine learning models. Acta Trop. 2018 Sep;185:391–9.PubMed
30.
Zurück zum Zitat Zlojutro A, Rey D, Gardner L. A decision-support framework to optimize border control for global outbreak mitigation. Sci. Rep. 2019 Feb 18;9(1):1–14. Zlojutro A, Rey D, Gardner L. A decision-support framework to optimize border control for global outbreak mitigation. Sci. Rep. 2019 Feb 18;9(1):1–14.
33.
Zurück zum Zitat Modjarrad K, Moorthy VS, Millett P, Gsell P-S, Roth C, Kieny M-P. Developing Global Norms for Sharing Data and Results during Public Health Emergencies. PLoS Med. 2016 Jan 5;13(1):e1001935.PubMedPubMedCentral Modjarrad K, Moorthy VS, Millett P, Gsell P-S, Roth C, Kieny M-P. Developing Global Norms for Sharing Data and Results during Public Health Emergencies. PLoS Med. 2016 Jan 5;13(1):e1001935.PubMedPubMedCentral
34.
Zurück zum Zitat Zhang GL, Khan AM, Srinivasan KN, August JT, Brusic V. Neural models for predicting viral vaccine targets. J. Bioinforma. Comput. Biol. 2005 Oct 1;03(05):1207–25. Zhang GL, Khan AM, Srinivasan KN, August JT, Brusic V. Neural models for predicting viral vaccine targets. J. Bioinforma. Comput. Biol. 2005 Oct 1;03(05):1207–25.
35.
Zurück zum Zitat Soam SS, Bhasker B, Mishra BN. Improved prediction of MHC class I binders/non-binders peptides through artificial neural network using variable learning rate: SARS corona virus, a case study. Adv. Exp. Med. Biol. 2011;696:223–9.PubMed Soam SS, Bhasker B, Mishra BN. Improved prediction of MHC class I binders/non-binders peptides through artificial neural network using variable learning rate: SARS corona virus, a case study. Adv. Exp. Med. Biol. 2011;696:223–9.PubMed
36.
Zurück zum Zitat Ong E, Wong MU, Huffman A, He Y. COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning. bioRxiv. 2020 Mar 21;2020.03.20.000141. Ong E, Wong MU, Huffman A, He Y. COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning. bioRxiv. 2020 Mar 21;2020.03.20.000141.
37.
Zurück zum Zitat Hayati M, Biller P, Colijn C. Predicting the short-term success of human influenza virus variants with machine learning. Proc. Biol. Sci. 2020 Apr 8;287(1924):20200319.PubMedPubMedCentral Hayati M, Biller P, Colijn C. Predicting the short-term success of human influenza virus variants with machine learning. Proc. Biol. Sci. 2020 Apr 8;287(1924):20200319.PubMedPubMedCentral
38.
Zurück zum Zitat Li H, Sun F. Comparative studies of alignment, alignment-free and SVM based approaches for predicting the hosts of viruses based on viral sequences. Sci. Rep. 2018 Jul 3;8(1):1–9. Li H, Sun F. Comparative studies of alignment, alignment-free and SVM based approaches for predicting the hosts of viruses based on viral sequences. Sci. Rep. 2018 Jul 3;8(1):1–9.
39.
Zurück zum Zitat Rao ASRS, Vazquez JA. 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 Mar 3;1–18. Rao ASRS, Vazquez JA. 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 Mar 3;1–18.
41.
Zurück zum Zitat Paolotti D, Carnahan A, Colizza V, Eames K, Edmunds J, Gomes G, et al. Web-based participatory surveillance of infectious diseases: the Influenzanet participatory surveillance experience. Clin. Microbiol Infect Off Publ. Eur. Soc. Clin. Microbiol. Infect. Dis. 2014 Jan;20(1):17–21. Paolotti D, Carnahan A, Colizza V, Eames K, Edmunds J, Gomes G, et al. Web-based participatory surveillance of infectious diseases: the Influenzanet participatory surveillance experience. Clin. Microbiol Infect Off Publ. Eur. Soc. Clin. Microbiol. Infect. Dis. 2014 Jan;20(1):17–21.
42.
Zurück zum Zitat Insel TR. Digital phenotyping: Technology for a new science of behavior. JAMA. 2017 Oct 3;318(13):1215–6.PubMed Insel TR. Digital phenotyping: Technology for a new science of behavior. JAMA. 2017 Oct 3;318(13):1215–6.PubMed
43.
Zurück zum Zitat Onnela J-P, Rauch SL. Harnessing smartphone-based digital phenotyping to enhance behavioral and mental health. Neuropsychopharmacology. 2016 Jun;41(7):1691–6.PubMedPubMedCentral Onnela J-P, Rauch SL. Harnessing smartphone-based digital phenotyping to enhance behavioral and mental health. Neuropsychopharmacology. 2016 Jun;41(7):1691–6.PubMedPubMedCentral
47.
Zurück zum Zitat Wang S, Kang B, Ma J, Zeng X, Xiao M, Guo J, et al. A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). medRxiv. 2020 Mar 11;2020.02.14.20023028. Wang S, Kang B, Ma J, Zeng X, Xiao M, Guo J, et al. A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). medRxiv. 2020 Mar 11;2020.02.14.20023028.
48.
Zurück zum Zitat 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 Feb 26;200642. 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 Feb 26;200642.
49.
Zurück zum Zitat Gozes O, Frid-Adar M, Greenspan H, Browning PD, Zhang H, Ji W, et al. Rapid ai development cycle for the coronavirus (COVID-19) pandemic: Initial results for automated detection & patient monitoring using deep learning CT image analysis. ArXiv200305037 Cs [Internet]. 2020 Mar 24 [cited 2020 Mar 29]; Available from: http://arxiv.org/abs/2003.05037 Gozes O, Frid-Adar M, Greenspan H, Browning PD, Zhang H, Ji W, et al. Rapid ai development cycle for the coronavirus (COVID-19) pandemic: Initial results for automated detection & patient monitoring using deep learning CT image analysis. ArXiv200305037 Cs [Internet]. 2020 Mar 24 [cited 2020 Mar 29]; Available from: http://​arxiv.​org/​abs/​2003.​05037
50.
Zurück zum Zitat John M, Shaiba H. Main factors influencing recovery in MERS Co-V patients using machine learning. J. Infect. Public Health. 2019 Oct;12(5):700–4.PubMedPubMedCentral John M, Shaiba H. Main factors influencing recovery in MERS Co-V patients using machine learning. J. Infect. Public Health. 2019 Oct;12(5):700–4.PubMedPubMedCentral
51.
Zurück zum Zitat Goh KJ, Choong MC, Cheong EH, Kalimuddin S, Duu Wen S, Phua GC, et al. Rapid progression to acute respiratory distress syndrome: Review of current understanding of critical illness from COVID-19 Infection. Ann. Acad. Med. Singap. 2020 Jan;49(1):1–9. Goh KJ, Choong MC, Cheong EH, Kalimuddin S, Duu Wen S, Phua GC, et al. Rapid progression to acute respiratory distress syndrome: Review of current understanding of critical illness from COVID-19 Infection. Ann. Acad. Med. Singap. 2020 Jan;49(1):1–9.
54.
56.
Zurück zum Zitat Réda C, Kaufmann E, Delahaye-Duriez A. Machine learning applications in drug development. Comput Struct Biotechnol J. 2020 Jan 1;18:241–52.PubMed Réda C, Kaufmann E, Delahaye-Duriez A. Machine learning applications in drug development. Comput Struct Biotechnol J. 2020 Jan 1;18:241–52.PubMed
57.
Zurück zum Zitat Anantpadma M, Lane T, Zorn KM, Lingerfelt MA, Clark AM, Freundlich JS, et al. Ebola Virus Bayesian Machine Learning Models Enable New in Vitro Leads. ACS Omega. 2019 Jan 30;4(1):2353–61.PubMedPubMedCentral Anantpadma M, Lane T, Zorn KM, Lingerfelt MA, Clark AM, Freundlich JS, et al. Ebola Virus Bayesian Machine Learning Models Enable New in Vitro Leads. ACS Omega. 2019 Jan 30;4(1):2353–61.PubMedPubMedCentral
Metadaten
Titel
Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review
verfasst von
Agam Bansal
Rana Prathap Padappayil
Chandan Garg
Anjali Singal
Mohak Gupta
Allan Klein
Publikationsdatum
03.08.2020
Verlag
Springer US
Schlagwort
COVID-19
Erschienen in
Journal of Medical Systems / Ausgabe 9/2020
Print ISSN: 0148-5598
Elektronische ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-020-01617-3