Prediction of Antimicrobial Resistance for Disease-Causing Agents Using Machine Learning

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  • Additional Information
    • Publication Information:
      IEEE
    • Publication Date:
      2018
    • Abstract:
      Antimicrobial resistance (AMR) occurs when disease-causing microorganisms are resistant towards prescribed drugs, nullifying its effect. As a consequence, there is a delay in recovery which worsens the patient's health. Antimicrobial resistance is identified as a global threat by the medical fraternity and various government bodies. Objective of the proposed system is to integrate technology with the field of bio-medical, in context with AMR. We applied various machine learning algorithms on datasets, to identify patterns and use them to predict resistance towards various drugs. This model would help in closing the gap between Doctors and Labs. In this model, we used ML and data mining techniques to predict AMR for individual patients based on trends identified from datasets. For building the model we use results of Patients undergoing antibiotic susceptibility test as datasets.
    • Contents Note:
      Conference Acronym: ICICCS
    • Author Affiliations:
      Dept. of Comput. Eng., Int. Inst. of Inf. Technol., Pune, India
      Dept. of Microbiol., B.J. Med. Coll., Pune, India
    • ISBN:
      978-1-5386-2842-3
      978-1-5386-2841-6
    • Relation:
      2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS)
    • Accession Number:
      10.1109/ICCONS.2018.8663043
    • Rights:
      Copyright 2018, IEEE
    • AMSID:
      8663043
    • Conference Acronym:
      ICICCS
    • Date of Current Version:
      2018
    • Document Subtype:
      IEEE Conference
    • Notes:
      Conference Location: Madurai, India, India

      Conference Start Date: 14 June 2018

      Conference End Date: 15 June 2018
    • Accession Number:
      edseee.8663043
  • Citations
    • ABNT:
      KULSHRESTHA, S. et al. Prediction of Antimicrobial Resistance for Disease-Causing Agents Using Machine Learning. 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Intelligent Computing and Control Systems (ICICCS), 2018 Second International Conference on, [s. l.], p. 972–975, 2018. DOI 10.1109/ICCONS.2018.8663043. Disponível em: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edseee&AN=edseee.8663043. Acesso em: 3 ago. 2020.
    • AMA:
      Kulshrestha S, Panda S, Nayar D, Dohe V, Jarali A. Prediction of Antimicrobial Resistance for Disease-Causing Agents Using Machine Learning. 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Intelligent Computing and Control Systems (ICICCS), 2018 Second International Conference on. June 2018:972-975. doi:10.1109/ICCONS.2018.8663043.
    • AMA11:
      Kulshrestha S, Panda S, Nayar D, Dohe V, Jarali A. Prediction of Antimicrobial Resistance for Disease-Causing Agents Using Machine Learning. 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Intelligent Computing and Control Systems (ICICCS), 2018 Second International Conference on. June 2018:972-975. doi:10.1109/ICCONS.2018.8663043
    • APA:
      Kulshrestha, S., Panda, S., Nayar, D., Dohe, V., & Jarali, A. (2018). Prediction of Antimicrobial Resistance for Disease-Causing Agents Using Machine Learning. 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Intelligent Computing and Control Systems (ICICCS), 2018 Second International Conference On, 972–975. https://doi.org/10.1109/ICCONS.2018.8663043
    • Chicago/Turabian: Author-Date:
      Kulshrestha, S., S. Panda, D. Nayar, V. Dohe, and A. Jarali. 2018. “Prediction of Antimicrobial Resistance for Disease-Causing Agents Using Machine Learning.” 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Intelligent Computing and Control Systems (ICICCS), 2018 Second International Conference On, June, 972–75. doi:10.1109/ICCONS.2018.8663043.
    • Harvard:
      Kulshrestha, S. et al. (2018) ‘Prediction of Antimicrobial Resistance for Disease-Causing Agents Using Machine Learning’, 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Intelligent Computing and Control Systems (ICICCS), 2018 Second International Conference on, pp. 972–975. doi: 10.1109/ICCONS.2018.8663043.
    • Harvard: Australian:
      Kulshrestha, S, Panda, S, Nayar, D, Dohe, V & Jarali, A 2018, ‘Prediction of Antimicrobial Resistance for Disease-Causing Agents Using Machine Learning’, 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Intelligent Computing and Control Systems (ICICCS), 2018 Second International Conference on, pp. 972–975, viewed 3 August 2020, .
    • MLA:
      Kulshrestha, S., et al. “Prediction of Antimicrobial Resistance for Disease-Causing Agents Using Machine Learning.” 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Intelligent Computing and Control Systems (ICICCS), 2018 Second International Conference On, June 2018, pp. 972–975. EBSCOhost, doi:10.1109/ICCONS.2018.8663043.
    • Chicago/Turabian: Humanities:
      Kulshrestha, S., S. Panda, D. Nayar, V. Dohe, and A. Jarali. “Prediction of Antimicrobial Resistance for Disease-Causing Agents Using Machine Learning.” 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Intelligent Computing and Control Systems (ICICCS), 2018 Second International Conference On, June 1, 2018, 972–75. doi:10.1109/ICCONS.2018.8663043.
    • Vancouver/ICMJE:
      Kulshrestha S, Panda S, Nayar D, Dohe V, Jarali A. Prediction of Antimicrobial Resistance for Disease-Causing Agents Using Machine Learning. 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Intelligent Computing and Control Systems (ICICCS), 2018 Second International Conference on [Internet]. 2018 Jun 1 [cited 2020 Aug 3];972–5. Available from: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edseee&AN=edseee.8663043