CFP last date
20 June 2024
Reseach Article

Machine Learning Systems in Epidemics: In the AI of the Storm

by Khalid Khader
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 29
Year of Publication: 2020
Authors: Khalid Khader
10.5120/ijca2020920323

Khalid Khader . Machine Learning Systems in Epidemics: In the AI of the Storm. International Journal of Computer Applications. 176, 29 ( Jun 2020), 29-36. DOI=10.5120/ijca2020920323

@article{ 10.5120/ijca2020920323,
author = { Khalid Khader },
title = { Machine Learning Systems in Epidemics: In the AI of the Storm },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2020 },
volume = { 176 },
number = { 29 },
month = { Jun },
year = { 2020 },
issn = { 0975-8887 },
pages = { 29-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume176/number29/31385-2020920323/ },
doi = { 10.5120/ijca2020920323 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:43:47.686695+05:30
%A Khalid Khader
%T Machine Learning Systems in Epidemics: In the AI of the Storm
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 29
%P 29-36
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

COVID 19 or the novel corona virus has hit the human race in a way which has never been documented before. Flu epidemics and pandemics have happened in the past, but few have had devastating global impact as the COVID pandemic. The pandemic has shown us how unprepared we are to something novel, something as tiny as a RNA strand which has caused havoc in the life and resources of most countries. The economic and social implication of the pandemic has been immense but the most concerning aspect of the epidemic is the vulnerability of the treating community to the disease, which raises very important questions on introspection. Flattening the curve is the strategy to prevent the overwhelming of the healthcare system during any epidemic and not a way to curb the infection itself(1). As the pandemic has progressed, the mortality and morbidity of healthcare workers has been increasingly documented and the implication of this on an already compromised system with infected, isolated or quarantined care providers(2) raises questions on the preparedness of systems for future epidemics. An elusive vaccine is not the answer to the problem of epidemics as most epidemics are novel and the next epidemic seems not far away, as history serves as the best early warning system. Epidemics will happen, but our response to the same needs to be better than the present standards of care which leaves critical care workers at risk. Many innovative ideas ranging from indigenous PPEs to telemedicine have been used as ways around the system, which can only be considered as desperate measures during desperate times. Understanding epidemics with the help of innovative technology opens up doors to novel ways to tackle novel problems. The use of Artificial Intelligence as the first response to epidemics addresses the problem of protecting the most critical and finite resource of Health Care Workers HCW and utilizing them ergonomically in domains where they are irreplaceable(3). Using the Natural Language Processing NLP as the first line of defense against epidemics(4) needs a paradigm shift in the current thinking process as the potential for this simple yet immense resource available at the fingertips of the common man needs to be tapped with caution. The use of NLP in Machine Learning ML, their use in other diseases and the possibility of using it as the first response to an epidemic thereby optimizing care and protecting critical resources will be discussed in this article.

References
  1. Anderson RM, Heesterbeek H, Klinkenberg D, Hollingsworth TD. How will country-based mitigation measures influence the course of the COVID-19 epidemic? The Lancet [Internet]. 2020 Mar [cited 2020 Apr 21];395(10228):931–4. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0140673620305675
  2. Madhav N, Oppenheim B, Gallivan M, Mulembakani P, Rubin E, Wolfe N. Pandemics: Risks, Impacts, and Mitigation. In: Jamison DT, Gelband H, Horton S, Jha P, Laxminarayan R, Mock CN, et al., editors. Disease Control Priorities: Improving Health and Reducing Poverty [Internet]. 3rd ed. Washington (DC): The International Bank for Reconstruction and Development / The World Bank; 2017 [cited 2020 Apr 21]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK525302/
  3. McCall B. COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread. Lancet Digit Health [Internet]. 2020 Apr [cited 2020 Apr 21];2(4):e166–7. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2589750020300546
  4. Munroα R, Gunasekaraβ L, Nevinsβ S, Polepeddiβ L, Rosenα E. Tracking Epidemics with Natural Language Processing and Crowdsourcing. 2012;
  5. Bedford J, Enria D, Giesecke J, Heymann DL, Ihekweazu C, Kobinger G, et al. COVID-19: towards controlling of a pandemic. The Lancet [Internet]. 2020 Mar [cited 2020 Apr 21];395(10229):1015–8. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0140673620306735
  6. Prem K, Liu Y, Russell TW, Kucharski AJ, Eggo RM, Davies N, et al. The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study. Lancet Public Health [Internet]. 2020 Mar [cited 2020 Apr 21];S2468266720300736. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2468266720300736
  7. Wilder-Smith A, Freedman DO. Isolation, quarantine, social distancing and community containment: pivotal role for old-style public health measures in the novel coronavirus (2019-nCoV) outbreak. J Travel Med [Internet]. 2020 Mar 13 [cited 2020 Apr 21];27(2):taaa020. Available from: https://academic.oup.com/jtm/article/doi/10.1093/jtm/taaa020/5735321
  8. Daniels N. Resource allocation and priority setting. In: Public health ethics: cases spanning the globe. Springer, Cham; 2016. p. 61–94.
  9. Adams JG, Walls RM. Supporting the Health Care Workforce During the COVID-19 Global Epidemic. JAMA [Internet]. 2020 Apr 21 [cited 2020 Apr 21];323(15):1439–40. Available from: https://doi.org/10.1001/jama.2020.3972
  10. Wang CJ, Ng CY, Brook RH. Response to COVID-19 in Taiwan: Big Data Analytics, New Technology, and Proactive Testing. JAMA [Internet]. 2020 Apr 14 [cited 2020 Apr 21];323(14):1341–2. Available from: https://doi.org/10.1001/jama.2020.3151
  11. Hadaya J, Schumm M, Livingston EH. Testing Individuals for Coronavirus Disease 2019 (COVID-19). JAMA [Internet]. 2020 Apr 1 [cited 2020 Apr 21]; Available from: https://jamanetwork.com/journals/jama/fullarticle/2764238
  12. Parmet WE, Sinha MS. Covid-19 — The Law and Limits of Quarantine. N Engl J Med [Internet]. 2020 Apr 9 [cited 2020 Apr 21];382(15):e28. Available from: http://www.nejm.org/doi/10.1056/NEJMp2004211
  13. Wu Y-C, Chen C-S, Chan Y-J. The outbreak of COVID-19: An overview. J Chin Med Assoc [Internet]. 2020 Mar [cited 2020 Apr 21];83(3):217–20. Available from: http://journals.lww.com/10.1097/JCMA.0000000000000270
  14. COVID-19 National Emergency Response Center, Epidemiology & Case Management Team, Korea Centers for Disease Control & Prevention. Contact Transmission of COVID-19 in South Korea: Novel Investigation Techniques for Tracing Contacts. Osong Public Health Res Perspect [Internet]. 2020 Feb 28 [cited 2020 Apr 21];11(1):60–3. Available from: http://ophrp.org/journal/view.php?doi=10.24171/j.phrp.2020.11.1.09
  15. Shi Y, Yu X, Zhao H, Wang H, Zhao R, Sheng J. Host susceptibility to severe COVID-19 and establishment of a host risk score: findings of 487 cases outside Wuhan. Crit Care [Internet]. 2020 Dec [cited 2020 Apr 21];24(1):108. Available from: https://ccforum.biomedcentral.com/articles/10.1186/s13054-020-2833-7
  16. Khafaie MA, Rahim F. Cross-Country Comparison of Case Fatality Rates of COVID-19/SARS-COV-2. Osong Public Health Res Perspect [Internet]. 2020 Apr 30 [cited 2020 Apr 21];11(2):74–80. Available from: http://ophrp.org/journal/view.php?doi=10.24171/j.phrp.2020.11.2.03
  17. Du R-H, Liang L-R, Yang C-Q, Wang W, Cao T-Z, Li M, et al. Predictors of Mortality for Patients with COVID-19 Pneumonia Caused by SARS-CoV-2: A Prospective Cohort Study. Eur Respir J [Internet]. 2020 Apr 8 [cited 2020 Apr 21];2000524. Available from: http://erj.ersjournals.com/lookup/doi/10.1183/13993003.00524-2020
  18. Bao L, Deng W, Gao H, Xiao C, Liu J, Xue J, et al. Reinfection could not occur in SARS-CoV-2 infected rhesus macaques [Internet]. Microbiology; 2020 Mar [cited 2020 Apr 21]. Available from: http://biorxiv.org/lookup/doi/10.1101/2020.03.13.990226
  19. Lin Q, Zhu L, Ni Z, Meng H, You L. Duration of serum neutralizing antibodies for SARS-CoV-2: Lessons from SARS-CoV infection. J Microbiol Immunol Infect [Internet]. 2020 Mar [cited 2020 Apr 21];S168411822030075X. Available from: https://linkinghub.elsevier.com/retrieve/pii/S168411822030075X
  20. Syal K. COVID-19: Herd Immunity and Convalescent Plasma Transfer Therapy. J Med Virol [Internet]. 2020 Apr 13 [cited 2020 Apr 21]; Available from: http://doi.wiley.com/10.1002/jmv.25870
  21. The Lancet. COVID-19: protecting health-care workers. The Lancet [Internet]. 2020 Mar [cited 2020 Apr 21];395(10228):922. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0140673620306449
  22. Grubaugh ND, Ladner JT, Lemey P, Pybus OG, Rambaut A, Holmes EC, et al. Tracking virus outbreaks in the twenty-first century. Nat Microbiol [Internet]. 2019 Jan [cited 2020 Apr 21];4(1):10–9. Available from: http://www.nature.com/articles/s41564-018-0296-2
  23. Delamater PL, Street EJ, Leslie TF, Yang YT, Jacobsen KH. Complexity of the Basic Reproduction Number (R 0 ). Emerg Infect Dis [Internet]. 2019 Jan [cited 2020 May 24];25(1):1–4. Available from: http://wwwnc.cdc.gov/eid/article/25/1/17-1901_article.htm
  24. Randolph HE, Barreiro LB. Herd Immunity: Understanding COVID-19. Immunity [Internet]. 2020 May [cited 2020 May 24];52(5):737–41. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1074761320301709
  25. Viceconte G, Petrosillo N. COVID-19 R0: Magic number or conundrum? Infect Dis Rep [Internet]. 2020 Feb 24 [cited 2020 Apr 21];12(1). Available from: https://www.pagepress.org/journals/index.php/idr/article/view/8516
  26. Sanche S, Lin YT, Xu C, Romero-Severson E, Hengartner N, Ke R. The Novel Coronavirus, 2019-nCoV, is Highly Contagious and More Infectious Than Initially Estimated [Internet]. Epidemiology; 2020 Feb [cited 2020 May 24]. Available from: http://medrxiv.org/lookup/doi/10.1101/2020.02.07.20021154
  27. Elsevier. How to survive a plague – why AI is key to fighting the next major pandemic [Internet]. Elsevier Connect. [cited 2020 Apr 21]. Available from: https://www.elsevier.com/connect/how-to-survive-a-plague-why-ai-is-key-to-fighting-the-next-major-pandemic
  28. Wittbold KA, Carroll C, Iansiti M, Zhang HM, Landman AB. How Hospitals Are Using AI to Battle Covid-19 [Internet]. Harvard Business Review. 2020 [cited 2020 Apr 22]. Available from: https://hbr.org/2020/04/how-hospitals-are-using-ai-to-battle-covid-19
  29. Lysaght T, Lim HY, Xafis V, Ngiam KY. AI-Assisted Decision-making in Healthcare: The Application of an Ethics Framework for Big Data in Health and Research. Asian Bioeth Rev [Internet]. 2019 Sep [cited 2020 Apr 22];11(3):299–314. Available from: http://link.springer.com/10.1007/s41649-019-00096-0
  30. Hollander JE, Carr BG. Virtually Perfect? Telemedicine for Covid-19. N Engl J Med [Internet]. 2020 Apr 30 [cited 2020 May 16];382(18):1679–81. Available from: http://www.nejm.org/doi/10.1056/NEJMp2003539
  31. Forte JC, van der Horst ICC. Comorbidities and medical history essential for mortality prediction in critically ill patients. Lancet Digit Health [Internet]. 2019 Jun [cited 2020 Apr 22];1(2):e48–9. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2589750019300305
  32. Meskó B, Hetényi G, Győrffy Z. Will artificial intelligence solve the human resource crisis in healthcare? BMC Health Serv Res [Internet]. 2018 Dec [cited 2020 Apr 22];18(1):545. Available from: https://bmchealthservres.biomedcentral.com/articles/10.1186/s12913-018-3359-4
  33. Chouvarda IG, Goulis DG, Lambrinoudaki I, Maglaveras N. Connected health and integrated care: Toward new models for chronic disease management. Maturitas [Internet]. 2015 Sep [cited 2020 Apr 22];82(1):22–7. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0378512215006052
  34. Benke K, Benke G. Artificial Intelligence and Big Data in Public Health. Int J Environ Res Public Health [Internet]. 2018 Dec 10 [cited 2020 Apr 22];15(12):2796. Available from: http://www.mdpi.com/1660-4601/15/12/2796
  35. Macaulay T. Researchers want your voice to train coronavirus-detecting AI [Internet]. The Next Web. 2020 [cited 2020 Apr 22]. Available from: https://thenextweb.com/artificial-intelligence/2020/04/07/researchers-want-your-voice-to-train-coronavirus-detecting-ai/
  36. Shi Y, Liu H, Wang Y, Cai M, Xu W. Theory and Application of Audio-Based Assessment of Cough. J Sens [Internet]. 2018 [cited 2020 Apr 22];2018:1–10. Available from: https://www.hindawi.com/journals/js/2018/9845321/
  37. Project Coswara: #COVID19 Diagnostic Tool by Indian Institute of Science: Submit Your Voice Samples! [Internet]. NoticeBard. 2020 [cited 2020 Apr 22]. Available from: https://www.noticebard.com/coswara-voice-samples-collection/
  38. Amisha, Malik P, Pathania M, Rathaur V. Overview of artificial intelligence in medicine. J Fam Med Prim Care [Internet]. 2019 [cited 2020 Apr 15];8(7):2328. Available from: http://www.jfmpc.com/text.asp?2019/8/7/2328/263820
  39. Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med [Internet]. 2019 Dec [cited 2020 Apr 22];17(1):195. Available from: https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-019-1426-2
  40. Liu B. Lifelong machine learning: a paradigm for continuous learning. Front Comput Sci [Internet]. 2017 Jun [cited 2020 Apr 22];11(3):359–61. Available from: http://link.springer.com/10.1007/s11704-016-6903-6
  41. Ahmed Z, Mohamed K, Zeeshan S, Dong X. Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database [Internet]. 2020 Jan 1 [cited 2020 Apr 22];2020:baaa010. Available from: https://academic.oup.com/database/article/doi/10.1093/database/baaa010/5809229
  42. Webb GI, Sammut C, Perlich C, Horváth T, Wrobel S, Korb KB, et al. Learning Curves in Machine Learning. In: Sammut C, Webb GI, editors. Encyclopedia of Machine Learning [Internet]. Boston, MA: Springer US; 2011 [cited 2020 Apr 22]. p. 577–80. Available from: http://link.springer.com/10.1007/978-0-387-30164-8_452
  43. Jing L, Tian Y. Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey. ArXiv190206162 Cs [Internet]. 2019 Feb 16 [cited 2020 Apr 9]; Available from: http://arxiv.org/abs/1902.06162
  44. AI runs smack up against a big data problem in COVID-19 diagnosis | ZDNet [Internet]. [cited 2020 Apr 22]. Available from: https://www.zdnet.com/article/ai-runs-smack-up-against-a-big-data-problem-in-covid-19-diagnosis/
  45. Big data versus COVID-19: opportunities and privacy challenges | Bruegel [Internet]. [cited 2020 Apr 22]. Available from: https://www.bruegel.org/2020/03/big-data-versus-covid-19-opportunities-and-privacy-challenges/
  46. Panesar A. Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes. Berkeley, CA: Apress Imprint,Apress; 2019.
  47. Parikh RB, Teeple S, Navathe AS. Addressing Bias in Artificial Intelligence in Health Care. JAMA [Internet]. 2019 Dec 24 [cited 2020 Apr 22];322(24):2377. Available from: https://jamanetwork.com/journals/jama/fullarticle/2756196
  48. Esteban C, Moraza J, Esteban C, Sancho F, Aburto M, Aramburu A, et al. Machine learning for COPD exacerbation prediction. In: 12 Rehabilitation and Chronic Care [Internet]. European Respiratory Society; 2015 [cited 2020 Apr 22]. p. OA3282. Available from: http://erj.ersjournals.com/lookup/doi/10.1183/13993003.congress-2015.OA3282
  49. Medeiros J. Virtual Assistants Can Detect Your Bad Mood And Do Something About It [Internet]. [cited 2020 Apr 22]. Available from: https://www.voicesummit.ai/blog/virtual-assistants-can-detect-your-bad-mood-and-do-something-about-it
  50. AI Helps Identify People at Risk for Suicide - WSJ [Internet]. [cited 2020 Apr 22]. Available from: https://www.wsj.com/articles/ai-helps-identify-people-at-risk-for-suicide-1519400853
  51. Virtual health care: Health consumer and physicians reaction | Deloitte Insights [Internet]. [cited 2020 Apr 22]. Available from: https://www2.deloitte.com/us/en/insights/industry/health-care/virtual-health-care-health-consumer-and-physician-surveys.html
  52. Dias D, Paulo Silva Cunha J. Wearable Health Devices—Vital Sign Monitoring, Systems and Technologies. Sensors [Internet]. 2018 Jul 25 [cited 2020 Apr 22];18(8):2414. Available from: http://www.mdpi.com/1424-8220/18/8/2414
  53. Nordyke RJ, Appelbaum K, Berman MA. Estimating the Impact of Novel Digital Therapeutics in Type 2 Diabetes and Hypertension: Health Economic Analysis. J Med Internet Res [Internet]. 2019 Oct 9 [cited 2020 Apr 22];21(10):e15814. Available from: https://www.jmir.org/2019/10/e15814
  54. Breen G-M, Matusitz J. An Evolutionary Examination of Telemedicine: A Health and Computer-Mediated Communication Perspective. Soc Work Public Health [Internet]. 2010 Jan [cited 2020 Apr 22];25(1):59–71. Availablefrom:https://www.tandfonline.com/doi/full/10.1080/19371910902911206
  55. Practice Guidance for COVID-19 [Internet]. [cited 2020 Apr 22]. Available from: https://www.psychiatry.org/psychiatrists/covid-19-coronavirus/practice-guidance-for-covid-19
Index Terms

Computer Science
Information Sciences

Keywords

Artificial intelligence Machine Learning systems Epidemics First Responders Healthcare workers comorbidities COVID pandemic Connected devices NLP Personalized medicine AI in healthcare AI in healthcare quality.