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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...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9819062 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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