Acta Scientific Women's Health (ASWH)(ISSN: 2582-3205)

Short Communication Volume 4 Issue 8

Role of Clinical Decision Support System in Identifying Women Health Issue After COVID Infection

Anurag Sahu and Sachidanand Singh*

Department of Biotechnology, Vignan’s Foundation for Science, Technology and Research, Vadalmudi, Guntur, Andhra Pradesh, India

*Corresponding Author: Sachidanand Singh, Associate Professor, Department of Biotechnology, Vignan’s Foundation for Science, Technology and Research, Vadlamudi, Guntur, Andhra Pradesh, India.

Received: January 06, 2022; Published: July 29, 2022

Abstract

Like other pandemics with in ~four months after the first case reported as covid, World Health Organisation (WHO) declared COVID-19 as a pandemic in March 2020. As per the records reported by WHO, Women’s health has suffered greatly because of this pandemic. The Covid-19 pandemic is putting a continuous pressure on healthcare systems around the world, with far-reaching health repercussions [1]. Women around the globe are still facing specific health risks and outcomes even after getting recovered from covid infections. Hence, considering the above said objective, the goal of this narrative review is to show how these disparities are amplified in the Covid-19 [2].

Keywords:Health Informatics; Clinical Decision Support System; Women’s Health

References

  1. Catrin Sohrabi., et al. “World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19)”. International Journal of Surgery (2020): 71-76.
  2. Claudia Carmassi., et al. “PTSD symptoms in healthcare workers facing the three coronavirus outbreaks: What can we expect after the COVID-19 pandemic”. Psychiatry Research 292 (2020).
  3. Kazmi SSH., et al. “COVID-19 and lockdwon: a study on the impact on mental health” (2020).
  4. Raju Vaishya., et al. “Artificial Intelligence (AI) applications for COVID-19 pandemic”. Diabetes and Metabolic Syndrome: Clinical Research and Reviews4 (2020): 337-339.
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  7. Singh S., et al. “Diagnosis of arthritis through fuzzy inference system”. Journal of Medical Systems3 (2012): 1459-1468.
  8. Kumar A., et al. “SVMRFE based approach for prediction of most discriminatory gene target for type II diabetes”. Genomics Data 12 (2017): 28-37.
  9. Brigit SA., et al. “Detection of obstructive jaundice through artificial intelligence”. Journal of Bioinformatics and Intelligent Control2 (2012): 121-128.
  10. Bhardwaj M., et al. “Health Informatics-A Vital Strategy to Tackle Pandemic Diseases”. Biomedical Journal of Scientific and Technical Research4 (2021): 31497-31498.

Citation

Citation: Anurag Sahu and Sachidanand Singh. “Role of Clinical Decision Support System in Identifying Women Health Issue After COVID Infection". Acta Scientific Women's Health 4.8 (2022): 09-10.

Copyright

Copyright: © 2022 Anurag Sahu and Sachidanand Singh. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




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