未经证实:对不良副作用的误解被认为会对公众对2019年冠状病毒病(COVID-19)疫苗的接受度产生负面影响。为了解决疫苗的这些缺点,设计了一种新的机器学习(ML)方法,根据个人和健康相关特征,对注射6种不同COVID-19疫苗后最常见的不良副作用进行个性化预测.
UNASSIGNED:使用了19943名来自伊朗和瑞士的参与者,这些参与者在COVID-19疫苗接种后出现不良副作用。研究了六种疫苗:AZD1222,SputnikV,BBIBP-CorV,COVAXIN,BNT162b2和mRNA-1273疫苗。八个副作用被认为是模型输出:发烧,疲劳,头痛,恶心,发冷,关节痛,肌肉疼痛,和注射部位反应。第一和第二剂量预测的总输入参数为46和54个特征,分别,包括年龄,性别,生活方式变量,和病史。使用接收器工作特征曲线下面积(ROC-AUC)比较了多种ML模型的性能。
未经批准:接受首次剂量的AZD1222SputnikV的总人数,BBIBP-CorV,COVAXIN,BNT162b2和mRNA-1273分别为6022、7290、5279、802、277和273。对于第二剂,数字分别为2851、5587、3841、599、242和228。预测第一剂不同副作用的Logistic回归模型对AZD1222,SputnikV,ROC-AUC为0.620-0.686,0.685-0.716,0.632-0.727,0.527-0.598,0.548-0.655,0.545-0.712,BBIBP-CorV,COVAXIN,BNT162b2和mRNA-1273疫苗,分别。第二剂量模型产生的ROC-AUC分别为0.777-0.867、0.795-0.848、0.857-0.906、0.788-0.875、0.683-0.850和0.486-0.680。
未经批准:使用大量接种COVID-19疫苗的接种者,建立了一种新颖的个性化策略,以高精度预测最常见不良副作用的发生。这项技术可以作为一种工具,为COVID-19疫苗的选择提供信息,并生成个性化的情况说明书,以遏制对不良副作用的担忧。
UNASSIGNED: 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.
UNASSIGNED: 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).
UNASSIGNED: 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.
UNASSIGNED: 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.