Paredes S, Rocha T, de Carvalho P, Henriques J, Rasteiro D, Morals J, Ferreira J, Mendes M
Instituto Politécnico de Coimbra, Departamento de Engenharia Informática e de Sistemas, Rua Pedro Nunes, 3030-199 Coimbra.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:872-5. doi: 10.1109/IEMBS.2011.6090227.
Several risk score models are available in literature to predict death/myocardial infarction event for coronary artery disease (CAD) patients, within a short period of time. However, the choice of the most adequate model is not straightforward since there might not be a consensus about the best model to use in clinical practice Moreover, individually, these models present some weaknesses, such as the inability to deal with missing information. This work addresses these problems, proposing a Bayesian classifier strategy enabling the simultaneous use of several models (models' fusion). Thus, a higher number of risk factors can be used in the common model, while it can deal with missing information. The validation of the strategy is carried out through the combination of three current risk score models (GRACE, TIMI, PURSUIT). Results were obtained based on a dataset that comprises 460 consecutive patients admitted to the Cardiology Department of Santa Cruz Hospital, Lisbon, from 1999 to 2001. A comparison with the voting scheme, which considers exclusively the outputs of models to combine (models output combination) is also carried out. The proposed Bayesian approach had very satisfactory results, confirming the potential of its application to the clinical practice.
文献中有几种风险评分模型可用于在短时间内预测冠状动脉疾病（CAD）患者的死亡/心肌梗死事件。然而，选择最合适的模型并非易事，因为在临床实践中使用哪种最佳模型可能没有共识。此外，这些模型各自存在一些弱点，例如无法处理缺失信息。这项工作解决了这些问题，提出了一种贝叶斯分类器策略，能够同时使用多个模型（模型融合）。因此，在通用模型中可以使用更多的风险因素，同时它可以处理缺失信息。该策略通过结合三种当前的风险评分模型（GRACE、TIMI、PURSUIT）进行验证。结果是基于一个数据集获得的，该数据集包含1999年至2001年期间连续入住里斯本圣克鲁斯医院心脏病科的460名患者。还与仅考虑要组合的模型输出（模型输出组合）的投票方案进行了比较。所提出的贝叶斯方法取得了非常令人满意的结果，证实了其在临床实践中的应用潜力。
