Personalized predictions of adverse side effects of the COVID-19 vaccines

被引：4

作者：

Jamshidi, Elham ^{[1
]}

Asgary, Amirhossein ^{[2
]}

Kharrazi, Ali Yazdizadeh ^{[2
]}

Tavakoli, Nader ^{[3
]}

Zali, Alireza ^{[1
]}

Mehrazi, Maryam ^{[3
]}

Jamshidi, Masoud ^{[4
]}

Farrokhi, Babak ^{[5
]}

Maher, Ali ^{[6
]}

von Garnier, Christophe ^{[7
]}

Rahi, Sahand Jamal ^{[8
]}

Mansouri, Nahal ^{[7
,9
,10
]}

机构：

[1] Shahid Beheshti Univ Med Sci, Funct Neurosurg Res Ctr, Shohada Tajrish Comprehens Neurosurg Ctr Excellenc, Tehran, Iran

[2] Univ Tehran, Coll Sci, Dept Biotechnol, Tehran, Iran

[3] Iran Univ Med Sci, Trauma & Injury Res Ctr, Tehran, Iran

[4] Univ Tehran, Dept Exercise Physiol, Tehran, Iran

[5] Minist Hlth & Med Educ, Hlth Network Adm Ctr, Undersecretary Hlth Affairs, Tehran, Iran

[6] Shahid Beheshti Univ Med Sci, Sch Management & Med Educ, Tehran, Iran

[7] Univ Lausanne UNIL, Lausanne Univ Hosp CHUV, Dept Med, Div Pulm Med, Lausanne, Switzerland

[8] Ecole Polytech Fed Lausanne EPFL, Inst Phys, Lab Phys Biol Syst, Lausanne, Switzerland

[9] Ecole Polytech Fed Lausanne EPFL, Swiss Inst Expt Canc Res ISREC, Sch Life Sci, Lausanne, Switzerland

[10] Lausanne Univ Hosp CHUV, Div Pulm Med, Res Grp Artificial Intelligence Pulm Med, Lausanne, Switzerland

来源：

HELIYON | 2023年 / 9卷 / 01期

关键词：

COVID-19; Artificial intelligence; Machine learning; Symptom; Vaccine; Adverse side effects; Sputnik V; AZD1222; AstraZeneca; Sinopharm; Moderna;

D O I：

10.1016/j.heliyon.2022.e12753

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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 ef-fects 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 se-lection and generate personalized factsheets to curb concerns about adverse side effects.

引用

页数：13