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Athula K Polonowita, Sunil J Wimalawansa
doi:10.5281/zenodo.10435649
This manuscript explores the rapid realization of benefits through integrating vitamin D big data into artificial intelligence/machine learning, offering a paradigm for expedited medical decision-policy and making formulation. Leveraging advanced deep learning techniques, we propose synthesizing extensive data sets, enabling the approval of repurposed, cost-effective compounds. This would facilitate economically sustainable policies and algorithms that directly benefit patients. This innovative approach improves clinical
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doi:10.3389/fcimb.2022.931061{ 'type': 'article-journal',
'id': 'https://doi.org/10.5281/zenodo.10435649',
'author': [{'family': 'Athula K. Polonowita', 'given': 'Sunil J. Wimalawansa'}],
'issued': {'date-parts': [[2023, 12, 25]]},
'abstract': '10.5281/zenodo.10435649\n'
'\n'
'ABSTRACTThere are unique relations between statistics and logic operating in neural networks '
'in the human brain. Like\xa0machine learning, big data and artificial intelligence (AI) '
'techniques could emulate human brain functions rapidly and assist healthcare decisions, '
'especially in emergencies like COVID-19. We explore published positive\xa0modulatory effects '
'of vitamin D on the human immune system from randomized control clinical trials (RCTs) and '
'meta-analyses taken as an example. The analysis confirmed a robust negative correlation '
'between serum 25(OH)D concentration with increased susceptibility to symptomatic SARS-CoV-2, '
'complications, hospitalization, and deaths. Instead of using available such data in mid-2020, '
'regulators relied upon RCTs from big pharma. Utilizing advanced Machine Learning paradigms '
'using cleaned data would have expedited proper decision-making, better guidance on patient '
'management, and approval of cost-effective early therapies like vitamin D and calcifediol, '
'reducing the cost of care and millions of hospitalizations and deaths from SARS-CoV-2. Using '
'Catuskoti logic in AI, broader than Boolean logic would have enhanced unbiased '
'decision-making and developed data-driven algorithms to control outbreaks. Scientific '
'evidence existed in mid-2020 that vitamin D and calcifediol rapidly boost the immune system, '
'thus preventing SARS-CoV-2-related complications and deaths. However, statistical\xa0'
'misconceptions, lack of broader vision, and failure to use big data analysis and machine '
'learning approaches prevented using generic agents as prophylactic and adjunct therapies. We '
'present the argument for hypothesis generation in conjunction with innovation in machine '
'learning, using Catuskoti-based XOR and XNOR circuits as a solution to expedite the '
'categorization of vulnerability, developing practical algorithms, and rapid approvals of '
'repurposed drugs for future pandemics. Such data-driven analyses through machine learning '
'programs minimize conflicts of interest and expedite decision-making. In the future, big data '
'can be analyzed using desktop computers, facilitating prompt and proper decision-making and '
'expedited drug approvals, especially for generics. This approach will better manage future '
'epidemics and pandemics—cost-effective early therapeutic interventions, preventing '
'complications, hospitalizations, and deaths, and reducing healthcare burden and cost.',
'container-title': 'Zenodo',
'DOI': '10.5281/ZENODO.10435649',
'publisher': 'Zenodo',
'title': 'MOLECULAR QUANTUM AND LOGIC PROCESS OF CONSCIOUSNESS—VITAMIN D BIG-DATA IN COVID-19—A CASE FOR '
'INCORPORATING MACHINE LEARNING IN MEDICINE',
'URL': 'https://zenodo.org/doi/10.5281/zenodo.10435649',
'copyright': 'Creative Commons Attribution 4.0 International'}
