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Body Sensor Networks as Emerging Trends of Technology in Health Care System: Challenges and Future

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Efficient Data Handling for Massive Internet of Medical Things

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Abstract

Health informatics becomes a hot topic not only in the scientific community but also in industrial and the business worlds. Innovative technologies such as both information and communication have enormous potential to improve public health care system. Therapeutic and health care coordination systems offer promising new models of human well-being, based on technology that includes internet, bioinformatics, and computing. Currently, multiple artificial intelligence and machine learning-based efforts have been made for deciphering diseases to facilitate predictive diagnosis. One of the objectives of this chapter is to present comprehensive overview on big data, digitization of health records, improved patient care, electronic medical records, and telemedicine. A snapshot of bioinformatics is used to understand its impact on healthcare. The later dimension describes key challenges of technology in public health as technological progress does not guarantee equitable health outcomes. The last section focusses on future of developed technologies into the healthcare sector due to lower costs, increased efficiency, and most importantly, to improve quality of care, which will help the readers to effectively use the information for their research endeavors.

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References

  1. G.N. Reddy, G.J. Reddy, Study of cloud computing in healthcare industry. arXiv preprint arXiv:1402.1841 (2014)

    Google Scholar 

  2. H. Thimbleby, Technology and the future of healthcare. J. Public Health Res. 2(3), e28 (2013)

    Article  Google Scholar 

  3. R. S. Dick, E. B. Steen, D. E. Detmer (eds.), The Computer-Based Patient Record: An Essential Technology for Health Care (National Academies Press, Washington, 1997)

    Google Scholar 

  4. S.S. Gill, S. Tuli, M. Xu, I. Singh, K.V. Singh, D. Lindsay, S. Tuli, D. Smirnova, M. Singh, U. Jain, H. Pervaiz, Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges. Internet Things 8, 100118 (2019)

    Article  Google Scholar 

  5. S. Russell, P. Norvig, Artificial Intelligence: A Modern Approach (Pearson, Hoboken, 2002)

    MATH  Google Scholar 

  6. A. Narayanan, E.C. Keedwell, B. Olsson, Artificial intelligence techniques for bioinformatics. Appl. Bioinforma. 1, 191–222 (2002)

    Google Scholar 

  7. G. Nápoles, I. Grau, R. Bello, R. Grau, Two-steps learning of Fuzzy Cognitive Maps for prediction and knowledge discovery on the HIV-1 drug resistance. Expert Syst. Appl. 41(3), 821–830 (2014)

    Article  Google Scholar 

  8. A. Burkov, M. Lutz, The Hundred-Page Machine Learning Book (Creative Commons, USA, 2019)

    Google Scholar 

  9. C.M. Bishop, Pattern Recognition and Machine Learning (Springer, New York, 2006)

    MATH  Google Scholar 

  10. T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer, New York, 2009)

    Book  MATH  Google Scholar 

  11. E. Alpaydin, Introduction to Machine Learning (MIT press, Cambridge, 2020)

    MATH  Google Scholar 

  12. A. Frolova, M. Obolenska, Integrative approaches for data analysis in systems biology: Current advances, in 2016 II International Young Scientists Forum on Applied Physics and Engineering (YSF), (IEEE, Piscataway, 2016), pp. 194–198

    Chapter  Google Scholar 

  13. I. Arel, D.C. Rose, T.P. Karnowski, Deep machine learning-a new frontier in artificial intelligence research. IEEE Comput. Intell. Mag. 5(4), 13–18 (2010)

    Article  Google Scholar 

  14. Y. Guo, Y. Liu, A. Oerlemans, S. Lao, S. Wu, M.S. Lew, Deep learning for visual understanding: A review. Neurocomputing 187, 27–48 (2016)

    Article  Google Scholar 

  15. B. Alipanahi, A. Delong, M.T. Weirauch, B.J. Frey, Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat. Biotechnol. 33(8), 831–838 (2015)

    Article  Google Scholar 

  16. M.R. Lamprecht, D.M. Sabatini, A.E. Carpenter, CellProfiler™: Free, versatile software for automated biological image analysis. BioTechniques 42(1), 71–75 (2007)

    Article  Google Scholar 

  17. S. Zhang, H. Hu, T. Jiang, L. Zhang, J. Zeng, TITER: Predicting translation initiation sites by deep learning. Bioinformatics 33(14), i234–i242 (2017)

    Article  Google Scholar 

  18. C. Angermueller, H.J. Lee, W. Reik, O. Stegle, DeepCpG: Accurate prediction of single-cell DNA methylation states using deep learning. Genome Biol. 18(1), 1–3 (2017)

    Google Scholar 

  19. G. Chuai, H. Ma, J. Yan, M. Chen, N. Hong, D. Xue, C. Zhou, C. Zhu, K. Chen, B. Duan, F. Gu, DeepCRISPR: optimized CRISPR guide RNA design by deep learning. Genome biology, 19(1), 1–18 (2018)

    Google Scholar 

  20. J.J. Almagro Armenteros, C.K. Sønderby, S.K. Sønderby, H. Nielsen, O. Winther, DeepLoc: Prediction of protein subcellular localization using deep learning. Bioinformatics 33(21), 3387–3395 (2017)

    Article  Google Scholar 

  21. C. Yang, L. Yang, M. Zhou, H. Xie, C. Zhang, M.D. Wang, H. Zhu, LncADeep: an ab initio lncRNA identification and functional annotation tool based on deep learning. Bioinformatics, 33(22), 3825–3834 (2018)

    Google Scholar 

  22. Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  23. F. Piccialli, V. Di Somma, F. Giampaolo, S. Cuomo, G. Fortino, A survey on deep learning in medicine: Why, how and when? Inf. Fusion 66, 111–137

    Google Scholar 

  24. R. Cuocolo, M. Caruso, T. Perillo, L. Ugga, M. Petretta, Machine Learning in oncology: A clinical appraisal. Cancer Lett. 481, 55–62 (2020)

    Article  Google Scholar 

  25. A.R. Ali, Deep Learning in Oncology–Applications in Fighting Cancer (2017)

    Google Scholar 

  26. K.K. Wong, G. Fortino, D. Abbott, Deep learning-based cardiovascular image diagnosis: A promising challenge. Futur. Gener. Comput. Syst. 110, 802–811 (2020)

    Article  Google Scholar 

  27. A.A.A. Valliani, D. Ranti, E.K. Oermann, Deep learning and neurology: A systematic review. Neurol. Ther. 8(2), 351–365 (2019)

    Article  Google Scholar 

  28. J. Luo, M. Wu, D. Gopukumar, Y. Zhao, Big data application in biomedical research and health care: A literature review. Biomed Inf Insights 8, BII–S31559 (2016)

    Google Scholar 

  29. T.R. Rao, P. Mitra, R. Bhatt, A. Goswami, The big data system, components, tools, and technologies: A survey. Knowl. Inf. Syst. 60(3), 1–81 (2019)

    Article  Google Scholar 

  30. R. Tripathi, P. Sharma, P. Chakraborty, P.K. Varadwaj, Next-generation sequencing revolution through big data analytics. Front. Life Sci. 9(2), 119–149 (2016)

    Article  Google Scholar 

  31. R. Pastorino, C. De Vito, G. Migliara, K. Glocker, I. Binenbaum, W. Ricciardi, S. Boccia, Benefits and challenges of Big Data in healthcare: An overview of the European initiatives. Eur. J. Pub. Health 29(Supplement_3), 23–27 (2019)

    Article  Google Scholar 

  32. H.J. Yoon, Blockchain technology and healthcare. Healthcare Inf. Res. 25(2), 59–60 (2019)

    Article  Google Scholar 

  33. T.T. Kuo, H.E. Kim, L. Ohno-Machado, Blockchain-distributed ledger technologies for biomedical and health care applications. J. Am. Med. Inform. Assoc. 24(6), 1211–1220 (2017)

    Article  Google Scholar 

  34. W.J. Gordon, C. Catalini, Blockchain technology for healthcare: Facilitating the transition to patient-driven interoperability. Comput. Struct. Biotechnol. J. 16, 224–230 (2018)

    Article  Google Scholar 

  35. Y.I.N. Yuehong, Y. Zeng, X. Chen, Y. Fan, The internet of things in healthcare: An overview. J. Ind. Inf. Integr. 1, 3–13 (2016)

    Google Scholar 

  36. R. Karjagi, M. Jindal, IoT applications in healthcare (2020), https://www.wipro.com/en-IN/business-process/what-can-iot-do-for-healthcare. Accessed 30 July 2020

  37. M. Masrom, A. Rahimli, A review of cloud computing technology solution for healthcare system. Res. J. Appl. Sci. Eng. Technol. 8(20), 2150–2153 (2014)

    Article  Google Scholar 

  38. H.A. Aziz, A. Guled, Cloud Computing and Healthcare Services (CRC Press, Boca Raton, 2016)

    Google Scholar 

  39. B. Mesko, Future of healthcare: 10 ways technology is changing healthcare (2020), https://medicalfuturist.com/ten-ways-technology-changing-healthcare. Accessed 25 July 2020

  40. F. Jiang, Y. Jiang, H. Zhi, Y. Dong, H. Li, S. Ma, Y. Wang, Q. Dong, H. Shen, Y. Wang, Artificial intelligence in healthcare: Past, present and future. Stroke Vasc. Neurol. 2(4), 230–243 (2017)

    Article  Google Scholar 

  41. S.M. McKinney, M. Sieniek, V. Godbole, J. Godwin, N. Antropova, H. Ashrafian, T. Back, M. Chesus, G.C. Corrado, A. Darzi, M. Etemadi, International evaluation of an AI system for breast cancer screening. Nature 577(7788), 89–94 (2020)

    Article  Google Scholar 

  42. S. Angraal, H.M. Krumholz, W.L. Schulz, Blockchain technology: Applications in health care. Circ. Cardiovasc. Qual. Outcomes 10(9) (2017)

    Google Scholar 

  43. A. Azaria, A. Ekblaw, T. Vieira, A. Lippman, Medrec: Using blockchain for medical data access and permission management, in 2016 2nd International Conference on Open and Big Data (OBD), (IEEE, Piscataway, 2016), pp. 25–30

    Chapter  Google Scholar 

  44. D. Dojchinovski, A. Ilievski, M. Gusev, Interactive home healthcare system with integrated voice assistant, in 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), (IEEE, Piscataway, 2019), pp. 284–288

    Chapter  Google Scholar 

  45. M.S. Hossain, G. Muhammad, A. Alamri, Smart healthcare monitoring: A voice pathology detection paradigm for smart cities. Multimedia Syst. 25(5), 565–575 (2019)

    Article  Google Scholar 

  46. S. Asimakopoulos, G. Asimakopoulos, F. Spillers, Motivation and user engagement in fitness tracking: Heuristics for mobile healthcare wearables, in Informatics, vol. 4, (Multidisciplinary Digital Publishing Institute, Basel, 2017), p. 5

    Google Scholar 

  47. S.Y. Lee, K. Lee, Factors that influence an individual’s intention to adopt a wearable healthcare device: The case of a wearable fitness tracker. Technol. Forecast. Soc. Chang. 129, 154–163 (2018)

    Article  Google Scholar 

  48. S. Valtolina, B.R. Barricelli, S. Di Gaetano, Communicability of traditional interfaces VS chatbots in healthcare and smart home domains. Behav. Inform. Technol. 39(1), 108–132 (2020)

    Article  Google Scholar 

  49. F. Amato, S. Marrone, V. Moscato, G. Piantadosi, A. Picariello, C. Sansone, HOLMeS: EHealth in the big data and deep learning era. Information 10(2), 34 (2019)

    Article  Google Scholar 

  50. L.R. Valmaggia, L. Latif, M.J. Kempton, M. Rus-Calafell, Virtual reality in the psychological treatment for mental health problems: A systematic review of recent evidence. Psychiatry Res. 236, 189–195 (2016)

    Article  Google Scholar 

  51. C.S. Lányi, Virtual reality in healthcare, in Intelligent Paradigms for Assistive and Preventive Healthcare, (Springer, Berlin, Heidelberg, 2006), pp. 87–116

    Chapter  Google Scholar 

  52. W. M. Carroll (ed.), Emerging Technologies for Nurses: Implications for Practice (Springer Publishing Company, New York, 2020)

    Google Scholar 

  53. M. Danciu, M. Gordan, A. Vlaicu, A. Antone, A survey of augmented reality in health care. Acta Technica Napocensis 52(1), 13 (2011)

    Google Scholar 

  54. G.J. Joyia, R.M. Liaqat, A. Farooq, S. Rehman, Internet of Medical Things (IOMT): Applications, benefits and future challenges in healthcare domain. J. Commun. 12(4), 240–247 (2017)

    Google Scholar 

  55. G. Matar, J.M. Lina, J. Carrier, A. Riley, G. Kaddoum, Internet of things in sleep monitoring: An application for posture recognition using supervised learning, in 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom), (IEEE, Piscataway, 2016), pp. 1–6

    Google Scholar 

  56. K. Häyrinen, K. Saranto, P. Nykänen, Definition, structure, content, use and impacts of electronic health records: A review of the research literature. Int. J. Med. Inform. 77(5), 291–304 (2008)

    Article  Google Scholar 

  57. H.M. Krumholz, Big data and new knowledge in medicine: The thinking, training, and tools needed for a learning health system. Health Aff. 33(7), 1163–1170 (2014)

    Article  Google Scholar 

  58. C. Chakraborty, B. Gupta, S. K. Ghosh, A Review on Telemedicine-Based WBAN Framework for Patient Monitoring, Int. Journal of Telemedicine and e-Health, Mary Ann Libert inc., 19(8), 619-626 (2013)

    Google Scholar 

  59. E.R. Dorsey, E.J. Topol, State of telehealth. N. Engl. J. Med. 375(2), 154–161 (2016)

    Article  Google Scholar 

  60. R.S. Weinstein, E.A. Krupinski, C.R. Doarn, Clinical examination component of telemedicine, telehealth, mhealth, and connected health medical practices. Med. Clin. 102(3), 533–544 (2018)

    Google Scholar 

  61. R.S. Weinstein, A.M. Lopez, B.A. Joseph, K.A. Erps, M. Holcomb, G.P. Barker, E.A. Krupinski, Telemedicine, telehealth, and mobile health applications that work: Opportunities and barriers. Am. J. Med. 127(3), 183–187 (2014)

    Article  Google Scholar 

  62. B.G. Celler, N.H. Lovell, D.K. Chan, The potential impact of home telecare on clinical practice. Med. J. Aust. 171(10), 518–521 (1999)

    Article  Google Scholar 

  63. C. Chakraborty, B. Gupta, S. K. Ghosh, D. Das, C. Chakraborty, Telemedicine Supported Chronic Wound Tissue Prediction Using Different Classification Approach, Journal of Medical Systems, 40(3), 1–12 (2016)

    Google Scholar 

  64. A. Huang, C. Chen, K. Bian, X. Duan, M. Chen, H. Gao, C. Meng, Q. Zheng, Y. Zhang, B. Jiao, L. Xie, WE-CARE: An intelligent mobile telecardiology system to enable mHealth applications. IEEE J. Biomed. Health Inform. 18(2), 693–702 (2013)

    Article  Google Scholar 

  65. C.L. Bentley, O. Otesile, R. Bacigalupo, J. Elliott, H. Noble, M.S. Hawley, E.A. Williams, P. Cudd, Feasibility study of portable technology for weight loss and HbA1c control in type 2 diabetes. BMC Med. Inform. Decis. Mak. 16(1), 92 (2016)

    Article  Google Scholar 

  66. C. Logan, Portable health care history information system. U.S. Patent 7,039,628 (2006)

    Google Scholar 

  67. Health Quality Ontario, Portable ultraviolet light surface-disinfecting devices for prevention of hospital-acquired infections: A health technology assessment. Ont Health Technol Assess Ser 18(1), 1 (2018)

    Google Scholar 

  68. H. Takyi, V. Watzlaf, J.T. Matthews, L. Zhou, D. DeAlmeida, Privacy and security in multi-user health kiosks. Int. J. Telerehabilitation 9(1), 3 (2017)

    Article  Google Scholar 

  69. Y. Lyu, C.J. Vincent, Y. Chen, Y. Shi, Y. Tang, W. Wang, W. Liu, S. Zhang, K. Fang, J. Ding, Designing and optimizing a healthcare kiosk for the community. Appl. Ergon. 47, 157–169 (2015)

    Article  Google Scholar 

  70. G. Boriani, A. Da Costa, A. Quesada, R.P. Ricci, S. Favale, G. Boscolo, N. Clementy, V. Amori, S. Mangoni, L. Stefano, H. Burri, MORE-CARE Study Investigators, Effects of remote monitoring on clinical outcomes and use of healthcare resources in heart failure patients with biventricular defibrillators: Results of the MORE-CARE multicentre randomized controlled trial. Eur. J. Heart Fail. 19(3), 416–425 (2017)

    Article  Google Scholar 

  71. M. Landolina, G.B. Perego, M. Lunati, A. Curnis, G. Guenzati, A. Vicentini, G. Parati, G. Borghi, P. Zanaboni, S. Valsecchi, M. Marzegalli, Remote monitoring reduces healthcare use and improves quality of care in heart failure patients with implantable defibrillators: The evolution of management strategies of heart failure patients with implantable defibrillators (EVOLVO) study. Circulation 125(24), 2985–2992 (2012)

    Article  Google Scholar 

  72. M. Singh, S. Singh, S. Prasad, I.S. Gambhir, Nanotechnology in medicine and antibacterial effect of silver nanoparticles. Dig. J. Nanomater. Biostruct. 3(3), 115–122 (2008)

    Google Scholar 

  73. J.R. Adler Jr., S.D. Chang, M.J. Murphy, J. Doty, P. Geis, S.L. Hancock, The Cyberknife: A frameless robotic system for radiosurgery. Stereotact. Funct. Neurosurg. 69(1–4), 124–128 (1997)

    Article  Google Scholar 

  74. H. Dodziuk, Applications of 3D printing in healthcare. Pol. J. Cardiothorac. Surg. 13(3), 283 (2016)

    Google Scholar 

  75. J. Chen, J. Zheng, Q. Gao, J. Zhang, J. Zhang, O.M. Omisore, L. Wang, H. Li, Polydimethylsiloxane (PDMS)-based flexible resistive strain sensors for wearable applications. Appl. Sci. 8(3), 345 (2018)

    Article  Google Scholar 

  76. T. Shany, S.J. Redmond, M.R. Narayanan, N.H. Lovell, Sensors-based wearable systems for monitoring of human movement and falls. IEEE Sensors J. 12(3), 658–670 (2011)

    Article  Google Scholar 

  77. S. Roy, M. David-Pur, Y. Hanein, Carbon nanotube-based ion selective sensors for wearable applications. ACS Appl. Mater. Interfaces 9(40), 35169–35177 (2017)

    Article  Google Scholar 

  78. X. Shen, J. Misic, N. Kato, P. Langenörfer, X. Lin, Emerging technologies and applications of wireless communication in healthcare. J. Commun. Networks 13(2), 81–85 (2011)

    Article  Google Scholar 

  79. S. Jiang, Y. Cao, S. Iyengar, P. Kuryloski, R. Jafari, Y. Xue, R. Bajcsy, S.B. Wicker, CareNet: An integrated wireless sensor networking environment for remote healthcare, in BODYNETS, (2008), p. 9

    Google Scholar 

  80. C. Gavriel, K.H. Parker, A.A. Faisal, Smartphone as an ultra-low-cost medical tricorder for real-time cardiological measurements via ballistocardiography, in 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), (IEEE, Piscataway, 2015), pp. 1–6

    Google Scholar 

  81. M.B. Kamel, G. Berry, Real-time locating systems (RTLS) in healthcare: A condensed primer. Int. J. Health Geogr. 11, 25–25 (2012)

    Article  Google Scholar 

  82. Q.V. Pham, D.C. Nguyen, W.J. Hwang, P.N. Pathirana, Artificial intelligence (AI) and big data for coronavirus (COVID-19) pandemic: A survey on the state-of-the-arts. IEEE Access 8, 130820–130839 (2020)

    Article  Google Scholar 

  83. G. Lalit, C. Emeka, N. Nasser, C. Chinmay, G. Garg, Anonymity preserving IoT-based COVID-19 and other infectious disease contact tracing model. IEEE Access 8, 159402–159414 (2020). https://doi.org/10.1109/ACCESS.2020.3020513, ISSN: 2169-3536

    Article  Google Scholar 

  84. D. Nguyen, M. Ding, P.N. Pathirana, A. Seneviratne, Blockchain and AI-based solutions to combat coronavirus (COVID-19)-like epidemics: A survey (2020)

    Google Scholar 

  85. B.R. Beck, B. Shin, Y. Choi, S. Park, K. Kang, Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model. Comput. Struct. Biotechnol. J. 18, 784–790 (2020)

    Article  Google Scholar 

  86. S. Chae, S. Kwon, D. Lee, Predicting infectious disease using deep learning and big data. Int. J. Environ. Res. Public Health 15(8), 1596 (2018)

    Article  Google Scholar 

  87. M. Eisenstein, Infection forecasts powered by big data. Nature 555(7695), S2–S4 (2018)

    Article  Google Scholar 

  88. S. Gilgore “GWU hospital tackles COVID-19 with new testing site, telemedicine and outreach on D.C.’s east side” (2020), https://www.bizjournals.com/washington/news/2020/04/08/gwu-hospital-tac%kles-covid-19-withnew-testing.html. Accessed 28 July 2020

  89. M. Shah, A. Tosto, “Industry voices-how rush University medical center’s virtual investments became central to its COVID19 response” (2020), https://www.fiercehealthcare.com/hospitals-health-systems/industryvoic%es-how-rush-university-system-for-health-s-virtual. Accessed 28 July 2020

  90. B. Marr, Coronavirus: How artificial intelligence, data science and technology is used to fight the pandemic (2020). Retrieved 30th March.

    Google Scholar 

  91. S. Tuli, S. Tuli, R. Tuli, S.S. Gill, Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing. Internet Things, 100222 (2020)

    Google Scholar 

  92. The Hindu BusinessLine, Covid-19: AP launches telemedicine facility [Online] (2020). Available: https://www.thehindubusinessline.com/news/national/covid-19-ap-launches%-telemedicine-facility/article31332943.ece

  93. A. Chakraborty, “Assam: Telemedicine, video monitoring for COVID19 home-quarantined people in Dhemaji” (2020), https://nenow.in/health/assam-telemedicine-videomonitoring-for-covid-1%9-home-quarantined-people-in-dhemaji.html Accessed 28 July 2020

  94. V. Chauhan, S. Galwankar, B. Arquilla, M. Garg, S. Di Somma, A. El-Menyar, V. Krishnan, J. Gerber, R. Holland, S.P. Stawicki, Novel coronavirus (COVID-19): Leveraging telemedicine to optimize care while minimizing exposures and viral transmission. J. Emerg. Trauma Shock 13(1), 20 (2020)

    Article  Google Scholar 

  95. J. Comstock, “Israel’s Sheba hospital turns to telehealth to treat incoming coronavirus-exposed patients” (2020), https://www.mobihealthnews.com/news/europe/israels-shebahospital-turns%-telehealth-treat-incoming-coronavirus-exposed-patients. Accessed 28 July 2020

  96. H.J. Ho, Z.X. Zhang, Z. Huang, A.H. Aung, W.Y. Lim, A. Chow, Use of a real-time locating system for contact tracing of health care workers during the COVID-19 pandemic at an infectious disease center in Singapore: Validation study. J. Med. Internet Res. 22(5), e19437 (2020)

    Article  Google Scholar 

  97. D.R. Seshadri, E.V. Davies, E.R. Harlow, J.J. Hsu, S.C. Knighton, T.A. Walker, J.E. Voos, C.K. Drummond, Wearable sensors for COVID-19: A call to action to harness our digital infrastructure for remote patient monitoring and virtual assessments. Front. Digital Health 2, 8–16 (2020)

    Article  Google Scholar 

  98. J.P. Navis, L. Leelarathna, W. Mubita, A. Urwin, M.K. Rutter, J. Schofield, H. Thabit, Impact of COVID-19 lockdown on flash and real-time glucose sensor users with type 1 diabetes in England. Acta Diabetol., 1–7 (2020)

    Google Scholar 

  99. A. Haghanifar, M.M. Majdabadi, S. Ko, Covid-cxnet: Detecting covid-19 in frontal chest x-ray images using deep learning. arXiv preprint arXiv:2006.13807 (2020)

    Google Scholar 

  100. S. Lalmuanawma, J. Hussain, L. Chhakchhuak, Applications of Machine Learning and Artificial Intelligence for Covid-19 (SARS-CoV-2) Pandemic: A Review (Chaos, Solitons & Fractals, 2020), pp. 110059–110065

    Google Scholar 

  101. C.J.C. Nicomedes, R.M.A. Avila, An analysis on the panic during COVID-19 pandemic through an online form. J. Affect. Disord. 276, 14–22 (2020)

    Article  Google Scholar 

  102. H. Wang, T. Li, S. Gauthier, E. Yu, Y. Tang, P. Barbarino, X. Yu, Coronavirus epidemic and geriatric mental healthcare in China: How a coordinated response by professional organizations helped older adults during an unprecedented crisis. Int. Psychogeriatr. 32(10), 1117–1120 (2020)

    Article  Google Scholar 

  103. V. Balachandar, I. Mahalaxmi, S.M. Devi, J. Kaavya, N.S. Kumar, G. Laldinmawii, N. Arul, S.J.K. Reddy, P. Sivaprakash, S. Kanchana, G. Vivekanandhan, Follow-up studies in COVID-19 recovered patients-is it mandatory? Sci. Total Environ. 729, 139021–139030 (2020)

    Article  Google Scholar 

  104. Y.M. Zhao, Y.M. Shang, W.B. Song, Q.Q. Li, H. Xie, Q.F. Xu, J.L. Jia, L.M. Li, H.L. Mao, X.M. Zhou, H. Luo, Follow-up study of the pulmonary function and related physiological characteristics of COVID-19 survivors three months after recovery. EClinicalMedicine 25, 100463–100472 (2020)

    Article  Google Scholar 

  105. K. Govindan, H. Mina, B. Alavi, A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19). Transp. Res. Part E Logist. Transp. Rev. 138, 101967–101981 (2020)

    Article  Google Scholar 

  106. A. Amini, W. Chen, G. Fortino, Y. Li, Y. Pan, M.D. Wang, Editorial special issue on “AI-driven informatics, sensing, imaging and big data analytics for fighting the COVID-19 pandemic”. IEEE J. Biomed. Health Inform. 24(10), 2731–2732 (2020)

    Article  Google Scholar 

  107. V. Majhi, S. Paul, R. Jain, Bioinformatics for healthcare applications, in 2019 Amity International Conference on Artificial Intelligence (AICAI), (IEEE, Piscataway, 2019), pp. 204–207

    Chapter  Google Scholar 

  108. Executive office of the president and council of economic advisers, economic report of the president (2008)

    Google Scholar 

  109. T.W. Shi, W.S. Kah, M.S. Mohamad, K. Moorthy, S. Deris, M.F. Sjaugi, S. Omatu, J.M. Corchado, S. Kasim, A review of gene selection tools in classifying cancer microarray data. Curr. Bioinforma. 12(3), 202–212 (2017)

    Article  Google Scholar 

  110. A. Serra, P. Galdi, R. Tagliaferri, Machine learning for bioinformatics and neuroimaging. WIREs Data Min. Knowl. Discovery 8(5), e1248 (2018)

    Google Scholar 

  111. Z. Yin, H. Lan, G. Tan, M. Lu, A.V. Vasilakos, W. Liu, Computing platforms for big biological data analytics: Perspectives and challenges. Comput. Struct. Biotechnol. J. 15, 403–411 (2017)

    Article  Google Scholar 

  112. M.C. Schatz, CloudBurst: Highly sensitive read mapping with MapReduce. Bioinformatics 25(11), 1363–1369 (2009)

    Article  Google Scholar 

  113. L. Chen, D. Zheng, B. Liu, J. Yang, Q. Jin, VFDB 2016: Hierarchical and refined dataset for big data analysis—10 years on. Nucleic Acids Res. 44(D1), D694–D697 (2016)

    Article  Google Scholar 

  114. M. Leclercq, B. Vittrant, M.L. Martin-Magniette, M.P. Scott Boyer, O. Perin, A. Bergeron, Y. Fradet, A. Droit, Large-scale automatic feature selection for biomarker discovery in high-dimensional OMICs data. Front. Genet. 10, 452 (2019)

    Article  Google Scholar 

  115. R. Nambiar, R. Bhardwaj, A. Sethi, R. Vargheese, A look at challenges and opportunities of big data analytics in healthcare, in 2013 IEEE International Conference on Big Data, (IEEE, Piscataway, 2013), pp. 17–22

    Chapter  Google Scholar 

  116. H. Fröhlich, R. Balling, N. Beerenwinkel, O. Kohlbacher, S. Kumar, T. Lengauer, M.H. Maathuis, Y. Moreau, S.A. Murphy, T.M. Przytycka, M. Rebhan, From hype to reality: Data science enabling personalized medicine. BMC Med. 16(1), 150 (2018)

    Article  Google Scholar 

  117. R. Bhardwaj, A. Sethi, R. Nambiar, Big data in genomics: An overview, in 2014 IEEE International Conference on Big Data (Big Data), (IEEE, Piscataway, 2014), pp. 45–49

    Chapter  Google Scholar 

  118. F.C. Navarro, H. Mohsen, C. Yan, S. Li, M. Gu, W. Meyerson, M. Gerstein, Genomics and data science: An application within an umbrella. Genome Biol. 20(1), 109 (2019)

    Article  Google Scholar 

  119. S.S. Ortega, L.C.L. Cara, M.K. Salvador, In silico pharmacology for a multidisciplinary drug discovery process. Drug Metab. Pers. Ther. 27(4), 199–207 (2012)

    Google Scholar 

  120. V.S. Rao, K. Srinivas, Modern drug discovery process: An in silico approach. J. Bioinf. Sequence Anal. 3(5), 89–94 (2011)

    Google Scholar 

  121. M.K. Hassan, A.I. El Desouky, S.M. Elghamrawy, A.M. Sarhan, Big data challenges and opportunities in healthcare informatics and smart hospitals, in Security in Smart Cities: Models, Applications, and Challenges, (Springer, Cham, 2019), pp. 3–26

    Chapter  Google Scholar 

  122. H. Liyanage, S.T. Liaw, J. Jonnagaddala, R. Schreiber, C. Kuziemsky, A.L. Terry, S. de Lusignan, Artificial intelligence in primary health care: Perceptions, issues, and challenges: Primary health care informatics working group contribution to the yearbook of medical informatics 2019. Yearb. Med. Inform. 28(1), 41 (2019)

    Article  Google Scholar 

  123. M.A. Winker, A. Flanagin, B. Chi-Lum, J. White, K. Andrews, R.L. Kennett, C.D. DeAngelis, R.A. Musacchio, Guidelines for medical and health information sites on the internet: Principles governing AMA web sites. JAMA 283(12), 1600–1606 (2000)

    Article  Google Scholar 

  124. R.A. Meinhardt, New “E-sign” law enables electronic prescriptions. Drug Benefit Trends 12(9), 23–49 (2000)

    Google Scholar 

  125. A.C. Norris, J.M. Brittain, Education, training and the development of healthcare informatics. Health Informatics J. 6(4), 189–195 (2000)

    Article  Google Scholar 

  126. K.W. Goodman, Ethics, Computing, and Medicine: Informatics and the Transformation of Health Care (Cambridge University Press, Cambridge, 1998)

    Google Scholar 

  127. S. Johnson, Pathways of care: What and how? J. Managed Care 1(1), 15–17 (1997)

    Article  Google Scholar 

  128. H. Heathfield, D. Pitty, R. Hanka, Evaluating information technology in health care: Barriers and challenges. BMJ 316(7149), 1959 (1998)

    Article  Google Scholar 

  129. R. Haux, Medical informatics: Past, present, future. Int. J. Med. Inform. 79(9), 599–610 (2010)

    Article  Google Scholar 

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Jaya Lakshmi, N., Jabalia, N. (2021). Body Sensor Networks as Emerging Trends of Technology in Health Care System: Challenges and Future. In: Chakraborty, C., Ghosh, U., Ravi, V., Shelke, Y. (eds) Efficient Data Handling for Massive Internet of Medical Things. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-66633-0_6

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