tetano
Editor, Senior Moderator
Heliyon
. 2022 Dec 30;e12753.
doi: 10.1016/j.heliyon.2022.e12753. Online ahead of print.
Personalized predictions of adverse side effects of the COVID-19 vaccines
Elham Jamshidi[SUP] 1 [/SUP], Amirhossein Asgary[SUP] 2 [/SUP], Ali Yazdizadeh Kharrazi[SUP] 2 [/SUP], Nader Tavakoli[SUP] 3 [/SUP], Alireza Zali[SUP] 1 [/SUP], Maryam Mehrazi[SUP] 3 [/SUP], Masoud Jamshidi[SUP] 4 [/SUP], Babak Farrokhi[SUP] 5 [/SUP], Ali Maher[SUP] 6 [/SUP], Christophe von Garnier[SUP] 7 [/SUP], Sahand Jamal Rahi[SUP] 8 [/SUP], Nahal Mansouri[SUP] 7 9 10 [/SUP]
Affiliations
Abstract
Background: Misconceptions about adverse side effects are thought to influence public acceptance of the Coronavirus disease 2019 (COVID-19) vaccines negatively. To address such perceived disadvantages of vaccines, a novel machine learning (ML) approach was designed to generate personalized predictions of the most common adverse side effects following injection of six different COVID-19 vaccines based on personal and health-related characteristics.
Methods: Prospective data of adverse side effects following COVID-19 vaccination in 19943 participants from Iran and Switzerland was utilized. Six vaccines were studied: The AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and the mRNA-1273 vaccine. The eight side effects were considered as the model output: fever, fatigue, headache, nausea, chills, joint pain, muscle pain, and injection site reactions. The total input parameters for the first and second dose predictions were 46 and 54 features, respectively, including age, gender, lifestyle variables, and medical history. The performances of multiple ML models were compared using Area Under the Receiver Operating Characteristic Curve (ROC-AUC).
Results: The total number of people receiving the first dose of the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and mRNA-1273 were 6022, 7290, 5279, 802, 277, and 273, respectively. For the second dose, the numbers were 2851, 5587, 3841, 599, 242 and 228. The Logistic Regression model for predicting different side effects of the first dose achieved ROC-AUCs of 0.620-0.686, 0.685-0.716, 0.632-0.727, 0.527-0.598, 0.548-0.655, 0.545-0.712 for the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2 and mRNA-1273 vaccines, respectively. The second dose models yielded ROC-AUCs of 0.777-0.867, 0.795-0.848, 0.857-0.906, 0.788-0.875, 0.683-0.850, and 0.486-0.680, respectively.
Conclusions: Using a large cohort of recipients vaccinated with COVID-19 vaccines, a novel and personalized strategy was established to predict the occurrence of the most common adverse side effects with high accuracy. This technique can serve as a tool to inform COVID-19 vaccine selection and generate personalized factsheets to curb concerns about adverse side effects.
Keywords: AI, artificial intelligence; AZD1222; Adverse side effects; Artificial intelligence; AstraZeneca; COVID-19; COVID-19, coronavirus disease of 2019; Covaxin; KNN, K Nearest Neighbors; LR, logistic regression; ML, machine learning; MLP, Multi-Layer Perceptron; Machine learning; Moderna; Pfizer; RF, random forest; ROC, receiver operating characteristic; Sinopharm; Sputnik V; Symptom; Vaccine.
. 2022 Dec 30;e12753.
doi: 10.1016/j.heliyon.2022.e12753. Online ahead of print.
Personalized predictions of adverse side effects of the COVID-19 vaccines
Elham Jamshidi[SUP] 1 [/SUP], Amirhossein Asgary[SUP] 2 [/SUP], Ali Yazdizadeh Kharrazi[SUP] 2 [/SUP], Nader Tavakoli[SUP] 3 [/SUP], Alireza Zali[SUP] 1 [/SUP], Maryam Mehrazi[SUP] 3 [/SUP], Masoud Jamshidi[SUP] 4 [/SUP], Babak Farrokhi[SUP] 5 [/SUP], Ali Maher[SUP] 6 [/SUP], Christophe von Garnier[SUP] 7 [/SUP], Sahand Jamal Rahi[SUP] 8 [/SUP], Nahal Mansouri[SUP] 7 9 10 [/SUP]
Affiliations
- PMID: 36597482
- PMCID: PMC9800018
- DOI: 10.1016/j.heliyon.2022.e12753
Abstract
Background: Misconceptions about adverse side effects are thought to influence public acceptance of the Coronavirus disease 2019 (COVID-19) vaccines negatively. To address such perceived disadvantages of vaccines, a novel machine learning (ML) approach was designed to generate personalized predictions of the most common adverse side effects following injection of six different COVID-19 vaccines based on personal and health-related characteristics.
Methods: Prospective data of adverse side effects following COVID-19 vaccination in 19943 participants from Iran and Switzerland was utilized. Six vaccines were studied: The AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and the mRNA-1273 vaccine. The eight side effects were considered as the model output: fever, fatigue, headache, nausea, chills, joint pain, muscle pain, and injection site reactions. The total input parameters for the first and second dose predictions were 46 and 54 features, respectively, including age, gender, lifestyle variables, and medical history. The performances of multiple ML models were compared using Area Under the Receiver Operating Characteristic Curve (ROC-AUC).
Results: The total number of people receiving the first dose of the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and mRNA-1273 were 6022, 7290, 5279, 802, 277, and 273, respectively. For the second dose, the numbers were 2851, 5587, 3841, 599, 242 and 228. The Logistic Regression model for predicting different side effects of the first dose achieved ROC-AUCs of 0.620-0.686, 0.685-0.716, 0.632-0.727, 0.527-0.598, 0.548-0.655, 0.545-0.712 for the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2 and mRNA-1273 vaccines, respectively. The second dose models yielded ROC-AUCs of 0.777-0.867, 0.795-0.848, 0.857-0.906, 0.788-0.875, 0.683-0.850, and 0.486-0.680, respectively.
Conclusions: Using a large cohort of recipients vaccinated with COVID-19 vaccines, a novel and personalized strategy was established to predict the occurrence of the most common adverse side effects with high accuracy. This technique can serve as a tool to inform COVID-19 vaccine selection and generate personalized factsheets to curb concerns about adverse side effects.
Keywords: AI, artificial intelligence; AZD1222; Adverse side effects; Artificial intelligence; AstraZeneca; COVID-19; COVID-19, coronavirus disease of 2019; Covaxin; KNN, K Nearest Neighbors; LR, logistic regression; ML, machine learning; MLP, Multi-Layer Perceptron; Machine learning; Moderna; Pfizer; RF, random forest; ROC, receiver operating characteristic; Sinopharm; Sputnik V; Symptom; Vaccine.