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首页> 外文期刊>BMC Medical Informatics and Decision Making >Evidential MACE prediction of acute coronary syndrome using electronic health records
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Evidential MACE prediction of acute coronary syndrome using electronic health records

机译：电子病历对急性冠脉综合征的MACE证据预测

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Major adverse cardiac event (MACE) prediction plays a key role in providing efficient and effective treatment strategies for patients with acute coronary syndrome (ACS) during their hospitalizations. Existing prediction models have limitations to cope with imprecise and ambiguous clinical information such that clinicians cannot reach to reliable MACE prediction results for individuals. To remedy it, this study proposes a hybrid method using Rough Set Theory (RST) and Dempster-Shafer Theory (DST) of evidence. In details, four state-of-the-art models, including one traditional ACS risk scoring model, i.e., GRACE, and three machine learning based models, i.e., Support Vector Machine, L1-Logistic Regression, and Classification and Regression Tree, are employed to generate initial MACE prediction results, and then RST is applied to determine the weights of the four single models. After that, the acquired prediction results are assumed as basic beliefs for the problem propositions and in this way, an evidential prediction result is generated based on DST in an integrative manner. Having applied the proposed method on a clinical dataset consisting of 2930 ACS patient samples, our model achieves 0.715 AUC value with competitive standard deviation, which is the best prediction results comparing with the four single base models and two baseline ensemble models. Facing with the limitations in traditional ACS risk scoring models, machine learning models and the uncertainties of EHR data, we present an ensemble approach via RST and DST to alleviate this problem. The experimental results reveal that our proposed method achieves better performance for the problem of MACE prediction when compared with the single models.
机译：重大不良心脏事件（MACE）预测在为急性冠脉综合征（ACS）患者住院期间提供有效的治疗策略中起着关键作用。现有的预测模型在应付不精确和模棱两可的临床信息方面存在局限性，因此临床医生无法获得个人的可靠MACE预测结果。为了解决这个问题，本研究提出了一种使用证据的粗糙集理论（RST）和Dempster-Shafer理论（DST）的混合方法。详细来说，有四个最新模型，包括一个传统的ACS风险评分模型（即GRACE）和三个基于机器学习的模型，即支持向量机，L1-Logistic回归以及分类和回归树。生成初始MACE预测结果，然后应用RST确定四个单个模型的权重。此后，将获取的预测结果假定为问题命题的基本信念，并以此方式，基于DST以综合方式生成证据预测结果。将所提出的方法应用于包含2930例ACS患者样本的临床数据集后，我们的模型获得具有竞争标准偏差的0.715 AUC值，与四个单一基础模型和两个基线整体模型相比，这是最佳的预测结果。面对传统ACS风险评分模型，机器学习模型和EHR数据不确定性的局限性，我们提出了一种通过RST和DST的集成方法来缓解此问题。实验结果表明，与单个模型相比，我们提出的方法在MACE预测问题上具有更好的性能。

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