Abstract
Healthcare systems are now shaping up by utilising digital technology advancements that help us transform unsustainable healthcare to sustainable ones. Digital technologies like artificial intelligence, VR/AR, 3D printing, robotics, and nanotechnology play a vital role for faster and effective solutions to many diseases. COVID-19 alias coronavirus is an infectious disease that had become ubiquitous and originated from the newly uncovered coronavirus started at Wuhan in China in December 2019 and later started spreading worldwide. COVID-19 belongs to the family of RNA viruses which cause respiratory tract infection, which cause mild to fatal results. Most people who experienced COVID-19 have mild to moderate symptoms and lead to deadly results in few cases based on their health condition. Symptoms of COVID-19 are quite similar to normal flu. It is a very tough task to differentiate the actual COVID-19 victims from normal flu patients.
Our study focuses on COVID-19 victim’s symptoms and clinical reports. The most time-consuming task lies in the confirmation of the disease, and sometimes to get the proper confirmation of the disease, the samples are given in two labs concurrently. Partially, this has an impact on the rise of cases day by day. Healthcare systems are now in need of decision-making techniques to control the virus from its rapid spread worldwide. Artificial intelligence plays a skilful way, like human intelligence. This paper will apply artificial intelligence techniques to analyse, prevent, and fight against the COVID-19. Records of various COVID-19-suspected patient’s data have been collected from the ICMR issued by the government district hospital, Anakapalle, Visakhapatnam. Datasets are trained based upon the collected records. Unstructured data like clinical records are applied to natural language processing (NLP) techniques, and structured data like chest X-ray images are applied to artificial neural network (ANN). Our study can help in making the correct decisions by utilising the best artificial intelligence techniques. Results obtained are simulated to identify the condition of COVID-19 patients, thereby diminishing the severity and death cases of COVID-19.
Methods: Clinical records data has been given a natural language processing technique to convert human written language to machine language processing technique. It analyses the data, and chest X-ray images are given to the artificial neural network. Both the techniques are used to predict the condition of the patient based on the records.
Findings: At the time of writing this paper, the total number of confirmed cases worldwide is nearly 7.82 million, and the total number of deaths occurred is 432,000. The total number of cases in India is 343,000, and the total number of deaths occurred is 9900. This count is up to mid-July, and this may increase or decrease in future depending upon the conditions.
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References
S.J. Fong, N. Dey, J. Chaki, An introduction to COVID-19, in Artificial Intelligence for Corona Virus Outbreak, Springer Briefs in Applied Sciences and Technology, (Springer, Singapore, 2022). https://doi.org/10.1007/978-981-15-5936-5_1
Chinese citizens push to abolish wildlife trade as corona virus persists, https://www.nationalgeographic.com/animals/2020/01/china-bans-wildlife-trade-after corona virus-outbreak/
D. Cucinotta, M. Vanelli, WHO declares COVID-19 a pandemic. Acta Bio medica Atenei Parmensis 91(1), 157–160 (2020). https://doi.org/10.23750/abm.v91i1.9397
D.D. Luxton, Chapter 1 - An introduction to artificial intelligence in behavioral and mental health care, in Artificial Intelligence in Behavioral and Mental HealthCare, ed. by D. D. Luxton, (Academic Press, Cambridge, 2016), pp. 1–26. https://doi.org/10.1016/B9780124202481.000015. ISBN 9780124202481. http://www.sciencedirect.com/science/article/pii/B9780124202481000015
D. Douglas Miller, E.W. Brown, Artificial intelligence in medical practice: The question to the answer? Am. J. Med. 131(2), 129–133 (2018). https://doi.org/10.1016/j.amjmed.2017.10.03. ISSN 00029343. http://www.sciencedirect.com/science/article/pii/S0002934317311178
M.P. Amisha, M. Pathania, V.K. Rathaur, Overview of artificial intelligence in medicine. J. Family Med. Prim. Care 8(7), 2328–2331 (2019). https://doi.org/10.4103/jfmpc.jfmpc_440_19
K. Kristian, Machine learning and artificial intelligence: Two fellow travelers on the quest for intelligent behavior in machines. Front. Big Data 1 (2018). https://doi.org/10.3389/fdata.2018.00006. ISSN 2624-909X. https://www.frontiersin.org/article/10.3389/fdata.2018.00006
S.L. Goldenberg, G. Nir, S.E. Salcudean, A new era: Artificial intelligence and machine learning in prostate cancer. Nat. Rev. Urol. 16, 391–403 (2019). https://doi.org/10.1038/s41585-019-0193-3
S.H. Chen, A.J. Jakeman, J.P. Norton, Artificial intelligence techniques: An introduction to their use for modelling environmental systems. Math. Comput. Simul. 78(2–3), 379–400 (2008). https://doi.org/10.1016/j.matcom.2008.01.028. ISSN 0378-4754. http://www.sciencedirect.com/science/article/pii/S0378475408000505
A.J. Schaefer, M.D. Bailey, S.M. Shechter, M.S. Roberts, Modeling medical treatment using Markov decision processes, in Operations Research and Health Care, International Series in Operations Research & Management Science, ed. by M. L. Brandeau, F. Sainfort, W. P. Pierskalla, vol. 70, (Springer, Boston, MA, 2005). https://doi.org/10.1007/1-4020-8066-2_23
M. Chary, S. Parikh, A.F. Manini, E.W. Boyer, M. Radeos, A review of natural language processing in medical education. West. J. Emerg. Med. 20(1), 78–86 (2019). https://doi.org/10.5811/westjem.2018.11.39725
R. Yamashita, M. Nishio, R. Do, K. Togluenashi, Convolutional neural networks: An overview and application in radiology. Insights Imag. 9(4), 611–629 (2018). https://doi.org/10.1007/s13244-018-0639-9
S.S. Yadav, S.M. Jadhav, Deep convolutional neural network based medical image classification for disease diagnosis. J. Big Data (2019). https://doi.org/10.1186/s40537-019-0276-2
M. Topaz, K. Lai, D. Dowding, V.J. Lei, A. Zisberg, K.H. Bowles, L. Zhou, Automated identification of wound information in clinical notes of patients with heart diseases: Developing and validating a natural language processing application. Int. J. Nurs. Stud. 64, 25–31 (2016). https://doi.org/10.1016/j.ijnurstu.2016.09.013. ISSN 0020-7489. http://www.sciencedirect.com/science/article/pii/S0020748916301602
X. Li et al., Clinical characteristics of 25 death cases with COVID-19: A retrospective review of medical records in a single medical center, Wuhan, China. Int. J. Infect. Dis 94, 128–132 (2020). https://doi.org/10.1016/j.ijid.2020.03.053
A. Jacobi, M. Chung, A. Bernheim, C. Eber, Portable chest X-ray in coronavirus disease-19 (COVID-19): A pictorial review. Clin. Imag. 64, 35–42 (2020). https://doi.org/10.1016/j.clinimag.2020.04.001
M. Luengo-Oroz, K. Hoffmann Pham, J. Bullock, et al., Artificial intelligence cooperation to support the global response to COVID-19. Nat. Mach. Intell. 2, 295–297 (2020). https://doi.org/10.1038/s42256-020-0184-3
S. Meystre, P.J. Haug, Natural language processing to extract medical problems from electronic clinical documents: Performance evaluation. J. Biomed. Informatics 39(6), 589–599 (2006). https://doi.org/10.1016/j.jbi.2005.11.004. ISSN 1532-0464. http://www.sciencedirect.com/science/article/pii/S1532046405001140
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Srinivas, P., Chakkravarthy, D.M., Battacharyya, D. (2022). Artificial Intelligence Approaches for the COVID-19 Pandemic. In: Garg, L., Chakraborty, C., Mahmoudi, S., Sohmen, V.S. (eds) Healthcare Informatics for Fighting COVID-19 and Future Epidemics. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-72752-9_13
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