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Systems biology as a conceptual framework for research in family medicine; use in predicting response to influenza vaccination

机译:系统生物学作为家庭医学研究的概念框架;用于预测对流感疫苗接种的反应

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Aim To introduce systems biology as a conceptual framework for research in family medicine, based on empirical data from a case study on the prediction of influenza vaccination outcomes. This concept is primarily oriented towards planning preventive interventions and includes systematic data recording, a multi-step research protocol and predictive modelling.Background Factors known to affect responses to influenza vaccination include older age, past exposure to influenza viruses, and chronic diseases; however, constructing useful prediction models remains a challenge, because of the need to identify health parameters that are appropriate for general use in modelling patients’ responses.Methods The sample consisted of 93 patients aged 50–89 years (median 69), with multiple medical conditions, who were vaccinated against influenza. Literature searches identified potentially predictive health-related parameters, including age, gender, diagnoses of the main chronic ageing diseases, anthropometric measures, and haematological and biochemical tests. By applying data mining algorithms, patterns were identified in the data set. Candidate health parameters, selected in this way, were then combined with information on past influenza virus exposure to build the prediction model using logistic regression.Findings A highly significant prediction model was obtained, indicating that by using a systems biology approach it is possible to answer unresolved complex medical uncertainties. Adopting this systems biology approach can be expected to be useful in identifying the most appropriate target groups for other preventive programmes.
机译:目的基于对流感疫苗接种结果预测的案例研究得出的经验数据,将系统生物学作为家庭医学研究的概念框架。该概念主要针对预防性干预计划,包括系统的数据记录,多步骤研究方案和预测性建模。已知影响流感疫苗接种反应的背景因素包括年龄较大,过往接触过流感病毒和慢性病;然而,由于需要确定适合普遍用于模拟患者反应的健康参数,因此构建有用的预测模型仍然是一个挑战。方法该样本由93名年龄在50-89岁(中位数为69岁)的患者组成,并接受了多种医学检查条件,接种流感疫苗的人。文献检索确定了与健康相关的潜在预测参数,包括年龄,性别,主要慢性衰老疾病的诊断,人体测量学以及血液学和生化测试。通过应用数据挖掘算法,可以在数据集中识别出模式。然后,将以此方式选择的候选健康参数与过去流感病毒暴露的信息相结合,以使用逻辑回归建立预测模型。结果获得了非常重要的预测模型,表明使用系统生物学方法可以回答未解决的复杂医学不确定性。预期采用这种系统生物学方法将有助于为其他预防计划确定最合适的目标人群。

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