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Hybrid predictive modelling: Thyrotoxic atrial fibrillation case
Journal of Computational Science ( IF 3.3 ) Pub Date : 2021-04-05 , DOI: 10.1016/j.jocs.2021.101365
Ilia V. Derevitskii , Daria A. Savitskaya , Alina Yu. Babenko , Sergey V. Kovalchuk

In this work, we propose a new approach to predictive modelling of disease complications development. This approach is based on hybrid methods that have several advantages in comparison with classic methods. The main advantage is the inclusion of the complex information about the dynamics of a patient’s conditions using pathways analysis and graph-based predictive modelling method. Hybrid approaches integrate results of classic machine learning (ML) models and dynamic analysis methods for better modelling and prediction.

We present this method’s application to the practical case of predictive modelling of Thyrotoxicosis Atrial Fibrillation (TAF) development. Medical specialists need tools to estimate the level of risk of developing TAF. Using the proposed predictive modelling method, our team developed such a tool. The method was validated using common ML metrics and expert evaluation and can be used as part of a decision support system for medical staff who work with thyrotoxicosis patients.

This manuscript presents an extended version of the work described in the paper [1]. In this work, we proposed several methods for calculating the probability of TAF development. Our methods include arterial fibrillation risk questionnaire for use in practical diagnostic tasks and tools for analyzing TAF dynamic. The extended study presents further development of the approach within the hybrid modelling approach.



中文翻译:

混合预测建模:甲状腺毒性房颤病例

在这项工作中,我们提出了一种疾病并发症发展的预测模型的新方法。该方法基于混合方法,与传统方法相比,该方法具有多个优点。主要优点是可以使用路径分析和基于图形的预测建模方法来包含有关患者病情动态的复杂信息。混合方法整合了经典机器学习(ML)模型和动态分析方法的结果,以实现更好的建模和预测。

我们介绍这种方法在甲状腺毒症心房颤动(TAF)发展的预测模型的实际案例中的应用。医学专家需要工具来估计发展TAF的风险水平。使用建议的预测建模方法,我们的团队开发了这样的工具。该方法已使用常见的ML指标和专家评估进行了验证,并且可以用作与甲状腺毒症患者一起工作的医务人员的决策支持系统的一部分。

该手稿介绍了论文[1]中描述的工作的扩展版本。在这项工作中,我们提出了几种计算TAF发生概率的方法。我们的方法包括用于实际诊断任务的动脉颤动风险问卷和用于分析TAF动态的工具。扩展的研究提出了在混合建模方法中该方法的进一步发展。

更新日期:2021-04-11
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