Page 18 - AIH-1-2

Artificial Intelligence in Health AI in the battle against COVID-19

32. Burbidge R, Trotter M, Buxton B, Holden S. Drug design Health. 2020;17:3176.
by machine learning: Support vector machines for doi: 10.3390/ijerph17093176
pharmaceutical data analysis. Comput Chem. 2001;26:5-14.
43. Dong D, Tang Z, Wang S, et al. The role of imaging in the
doi: 10.1016/S0097-8485(01)00094-8
detection and management of COVID-19: A review. IEEE
33. Zernov VV, Balakin KV, Ivaschenko AA, Savchuk NP, Rev Biomed Eng. 2020;14:16-29.
Pletnev IV. Drug discovery using support vector machines. doi: 10.1109/RBME.2020.2990959
The case studies of drug-likeness, agrochemical-likeness,
and enzyme inhibition predictions. J Chem Inf Comput Sci. 44. Wang L, Lin ZQ, Wong A. Covid-net: A tailored deep
2003;43:2048-2056. convolutional neural network design for detection of covid-
19 cases from chest x-ray images. Sci Rep. 2020;10:19549.
doi: 10.1021/ci0341161
doi: 10.1038/s41598-020-76550-z
34. Collins FS, Morgan M, Patrinos A. The human genome
project: Lessons from large-scale biology. Science. 45. Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O,
2003;300:286-290. Acharya UR. Automated detection of COVID-19 cases
using deep neural networks with X-ray images. Comput Biol
doi: 10.1126/science.1084564
Med. 2020;121:103792.
35. Wallach I, Dzamba M, Heifets A. AtomNet: A deep doi: 10.1016/j.compbiomed.2020.103792
convolutional neural network for bioactivity prediction
in structure-based drug discovery. arXiv preprint 46. Li L, Qin L, Xu Z, et al. Artificial intelligence distinguishes
arXiv:1510.02855. 2015. COVID-19 from community acquired pneumonia on chest
CT. Radiology. 2020;296:E65-E71.
36. Piwek L, Ellis DA, Andrews S, Joinson A. The rise of
consumer health wearables: Promises and barriers. PLoS doi: 10.1148/radiol.2020200905
Med. 2016;13:e1001953. 47. Shi F, Xia L, Shan F, et al. Large-scale screening to distinguish
doi: 10.1371/journal.pmed.1001953 between COVID-19 and community-acquired pneumonia
using infection size-aware classification. Phys Med Biol.
37. Kaur G. Pandemic management via technology: A review.
Management. 2011;40:181-187. 2021;66:065031.
doi: 10.1088/1361-6560/abe838
doi: 10.1016/j.indmarman.2010.06.026
48. Zhang K, Liu X, Shen J, et al. Clinically applicable AI
38. Sadilek A, Kautz H, Silenzio V. Predicting Disease
Transmission from Geo-Tagged Micro-Blog Data. Vol. 26. system for accurate diagnosis, quantitative measurements,
In: Proceedings of the AAAI Conference on Artificial and prognosis of COVID-19 pneumonia using computed
tomography. Cell. 2020;181:1423-1433.e11.
Intelligence. 2012. p. 136-142.
doi: 10.1016/j.cell.2020.04.045
doi: 10.1609/aaai.v26i1.8103
49. Jiang X, Coffee M, Bari A, et al. Towards an artificial
39. Raza Abidi SS, Goh A. Applying Knowledge Discovery
to Predict Infectious Disease Epidemics. In: Pacific Rim intelligence framework for data-driven prediction of
International Conference on Artificial Intelligence. Berlin: coronavirus clinical severity. Comput Mater Continua.
2020;63:537-551.
Springer; 1998. p. 170-181.
doi: 10.32604/cmc.2020.010691
doi: 10.1007/BFb0095267
50. Ahamad MM, Aktar S, Rashed-Al-Mahfuz M, et al.
40. Lampos V, Cristianini N. Tracking the Flu Pandemic by
Monitoring the Social Web. In: 2010 2 International A machine learning model to identify early stage symptoms
nd
of SARS-Cov-2 infected patients. Expert Syst Appl.
Workshop on Cognitive Information Processing. IEEE; 2010.
p. 411-416. 2020;160:113661.
doi: 10.1016/j.eswa.2020.113661
doi: 10.1109/CIP.2010.5604088
51. Zoabi Y, Deri-Rozov S, Shomron N. Machine learning-based
41. Choi S, Lee J, Kang MG, Min H, Chang YS, Yoon S. Large- prediction of COVID-19 diagnosis based on symptoms. NPJ
scale machine learning of media outlets for understanding
public reactions to nation-wide viral infection outbreaks. Digit Med. 2021;4:3.
Methods. 2017;129:50-59. doi: 10.1038/s41746-020-00372-6
doi: 10.1016/j.ymeth.2017.04.004 52. Menni C, Valdes AM, Freydin MB, et al. Real-time tracking
of self-reported symptoms to predict potential COVID-19.
42. Bragazzi NL, Dai H, Damiani G, Behzadifar M, Martini M,
Wu J. How big data and artificial intelligence can help better Nat Med. 2020;26:1037-1040.
manage the COVID-19 pandemic. Int J Environ Res Public doi: 10.1038/s41591-020-0916-2

Volume 1 Issue 2 (2024) 12 doi: 10.36922/aih.2401