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

被引:16
作者
Ahamad, Md. Martuza [1 ]
Aktar, Sakifa [1 ]
Uddin, Md. Jamal [1 ]
Rashed-Al-Mahfuz, Md. [2 ]
Azad, A. K. M. [3 ]
Uddin, Shahadat [4 ]
Alyami, Salem A. [3 ]
Sarker, Iqbal H. [5 ]
Khan, Asaduzzaman [6 ]
Lio, Pietro [7 ]
Quinn, Julian M. W. [8 ]
Moni, Mohammad Ali [6 ]
机构
[1] Bangabandhu Sheikh Mujibur Rahman Sci & Technol U, Dept Comp Sci & Engn, Gopalganj 8100, Bangladesh
[2] Univ Rajshahi, Dept Comp Sci & Engn, Rajshahi 6205, Bangladesh
[3] Imam Mohammad Ibn Saud Islamic Univ IMSIU, Fac Sci, Dept Math & Stat, Riyadh 13318, Saudi Arabia
[4] Univ Sydney, Fac Engn, Complex Syst Res Grp, Darlington, NSW 2008, Australia
[5] Chittagong Univ Engn & Technol, Dept Comp Sci & Engn, Chittagong 4349, Bangladesh
[6] Univ Queensland, Fac Hlth & Behav Sci, Sch Hlth & Rehabil Sci, St Lucia, Qld 4072, Australia
[7] Univ Cambridge, Comp Lab, 15 JJ Thomson Ave, Cambridge CB3 0FD, England
[8] Garvan Inst Med Res, Hlth Ageing, Darlinghurst, NSW 2010, Australia
关键词
COVID-19; vaccination; adverse reactions; comorbidities; symptoms; machine learning; statistical analysis; CORONAVIRUS VACCINE;
D O I
10.3390/healthcare11010031
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
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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页数:22
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