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Adverse Effects of COVID-19 Vaccination: Machine Learning and Statistical Approach to Identify and Classify Incidences of Morbidity and Postvaccination Reactogenicity

Good vaccine safety and reliability are essential for successfully countering infectious disease spread. A small but significant number of adverse reactions to COVID-19 vaccines have been reported. Here, we aim to identify possible common factors in such adverse reactions to enable strategies that r...

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Autores principales: Ahamad, Md. Martuza, Aktar, Sakifa, Uddin, Md. Jamal, Rashed-Al-Mahfuz, Md., Azad, A. K. M., Uddin, Shahadat, Alyami, Salem A., Sarker, Iqbal H., Khan, Asaduzzaman, Liò, Pietro, Quinn, Julian M. W., Moni, Mohammad Ali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9819062/
https://www.ncbi.nlm.nih.gov/pubmed/36611491
http://dx.doi.org/10.3390/healthcare11010031
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author Ahamad, Md. Martuza
Aktar, Sakifa
Uddin, Md. Jamal
Rashed-Al-Mahfuz, Md.
Azad, A. K. M.
Uddin, Shahadat
Alyami, Salem A.
Sarker, Iqbal H.
Khan, Asaduzzaman
Liò, Pietro
Quinn, Julian M. W.
Moni, Mohammad Ali
author_facet Ahamad, Md. Martuza
Aktar, Sakifa
Uddin, Md. Jamal
Rashed-Al-Mahfuz, Md.
Azad, A. K. M.
Uddin, Shahadat
Alyami, Salem A.
Sarker, Iqbal H.
Khan, Asaduzzaman
Liò, Pietro
Quinn, Julian M. W.
Moni, Mohammad Ali
author_sort Ahamad, Md. Martuza
collection PubMed
description Good vaccine safety and reliability are essential for successfully countering infectious disease spread. A small but significant number of adverse reactions to COVID-19 vaccines have been reported. Here, we aim to identify possible common factors in such adverse reactions to enable strategies that reduce the incidence of such reactions by using patient data to classify and characterise those at risk. We examined patient medical histories and data documenting postvaccination effects and outcomes. The data analyses were conducted using a range of statistical approaches followed by a series of machine learning classification algorithms. In most cases, a group of similar features was significantly associated with poor patient reactions. These included patient prior illnesses, admission to hospitals and SARS-CoV-2 reinfection. The analyses indicated that patient age, gender, taking other medications, type-2 diabetes, hypertension, allergic history and heart disease are the most significant pre-existing factors associated with the risk of poor outcome. In addition, long duration of hospital treatments, dyspnoea, various kinds of pain, headache, cough, asthenia, and physical disability were the most significant clinical predictors. The machine learning classifiers that are trained with medical history were also able to predict patients with complication-free vaccination and have an accuracy score above 90%. Our study identifies profiles of individuals that may need extra monitoring and care (e.g., vaccination at a location with access to comprehensive clinical support) to reduce negative outcomes through classification approaches.
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spelling pubmed-98190622023-01-07 Adverse Effects of COVID-19 Vaccination: Machine Learning and Statistical Approach to Identify and Classify Incidences of Morbidity and Postvaccination Reactogenicity Ahamad, Md. Martuza Aktar, Sakifa Uddin, Md. Jamal Rashed-Al-Mahfuz, Md. Azad, A. K. M. Uddin, Shahadat Alyami, Salem A. Sarker, Iqbal H. Khan, Asaduzzaman Liò, Pietro Quinn, Julian M. W. Moni, Mohammad Ali Healthcare (Basel) Article Good vaccine safety and reliability are essential for successfully countering infectious disease spread. A small but significant number of adverse reactions to COVID-19 vaccines have been reported. Here, we aim to identify possible common factors in such adverse reactions to enable strategies that reduce the incidence of such reactions by using patient data to classify and characterise those at risk. We examined patient medical histories and data documenting postvaccination effects and outcomes. The data analyses were conducted using a range of statistical approaches followed by a series of machine learning classification algorithms. In most cases, a group of similar features was significantly associated with poor patient reactions. These included patient prior illnesses, admission to hospitals and SARS-CoV-2 reinfection. The analyses indicated that patient age, gender, taking other medications, type-2 diabetes, hypertension, allergic history and heart disease are the most significant pre-existing factors associated with the risk of poor outcome. In addition, long duration of hospital treatments, dyspnoea, various kinds of pain, headache, cough, asthenia, and physical disability were the most significant clinical predictors. The machine learning classifiers that are trained with medical history were also able to predict patients with complication-free vaccination and have an accuracy score above 90%. Our study identifies profiles of individuals that may need extra monitoring and care (e.g., vaccination at a location with access to comprehensive clinical support) to reduce negative outcomes through classification approaches. MDPI 2022-12-22 /pmc/articles/PMC9819062/ /pubmed/36611491 http://dx.doi.org/10.3390/healthcare11010031 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ahamad, Md. Martuza
Aktar, Sakifa
Uddin, Md. Jamal
Rashed-Al-Mahfuz, Md.
Azad, A. K. M.
Uddin, Shahadat
Alyami, Salem A.
Sarker, Iqbal H.
Khan, Asaduzzaman
Liò, Pietro
Quinn, Julian M. W.
Moni, Mohammad Ali
Adverse Effects of COVID-19 Vaccination: Machine Learning and Statistical Approach to Identify and Classify Incidences of Morbidity and Postvaccination Reactogenicity
title Adverse Effects of COVID-19 Vaccination: Machine Learning and Statistical Approach to Identify and Classify Incidences of Morbidity and Postvaccination Reactogenicity
title_full Adverse Effects of COVID-19 Vaccination: Machine Learning and Statistical Approach to Identify and Classify Incidences of Morbidity and Postvaccination Reactogenicity
title_fullStr Adverse Effects of COVID-19 Vaccination: Machine Learning and Statistical Approach to Identify and Classify Incidences of Morbidity and Postvaccination Reactogenicity
title_full_unstemmed Adverse Effects of COVID-19 Vaccination: Machine Learning and Statistical Approach to Identify and Classify Incidences of Morbidity and Postvaccination Reactogenicity
title_short Adverse Effects of COVID-19 Vaccination: Machine Learning and Statistical Approach to Identify and Classify Incidences of Morbidity and Postvaccination Reactogenicity
title_sort adverse effects of covid-19 vaccination: machine learning and statistical approach to identify and classify incidences of morbidity and postvaccination reactogenicity
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9819062/
https://www.ncbi.nlm.nih.gov/pubmed/36611491
http://dx.doi.org/10.3390/healthcare11010031
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