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首页> 外文会议>IEEE International Conference on Machine Learning and Applications >Validation of a Federation of Collaborative Rational Agents for the Diagnosis Of Acute Coronary Syndromes in a Population with High Probability
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Validation of a Federation of Collaborative Rational Agents for the Diagnosis Of Acute Coronary Syndromes in a Population with High Probability

机译：验证具有高概率群体中急性冠状动脉综合征的急性冠状动脉综合征的核算联合

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Acute myocardial infarction is the main cause of death worldwide; it is part of the acute coronary syndromes (ACS) which are characterized by an acute obstruction of the blood flow in the arteries of the heart. ACS diagnosis poses a highly complex problem where the use of intelligent systems represents an opportunity for the optimization of the diagnosis. The objective of the present work is to perform a cross validation of a federation of collaborative rational agents for the diagnosis of ACS in a population with high probability exhibiting chest pain. A study of diagnostic tests was performed, the diagnostic standard criterion was the third redefinition of infarction or some strategy for coronary stratification. The index test was the result of a system based on a federation of collaborative rational agents based on the assembly of neural networks by means of a weighted voting system in accordance with positive likelihood ratios. A sample of 108 patients was calculated and a contingency table was built in order to calculate the operational characteristics. 148 patients were taken into consideration, ACS was discarded in 29,2%, 51,7 exhibited acute infarction, and 19,1% exhibited unstable angina. The federation system reached a precision of 79%, sensibility of 97,1%, specificity of 36,4%, and AUC of 0,672. It is concluded that a multi-agent system based on the assembly of neural networks attained an acceptable performance for the diagnosis of ACS in a population with high probability.
机译：急性心肌梗死是全世界死亡的主要原因;它是急性冠状动脉综合征（ACS）的一部分，其特征在于心脏动脉中的血流急性阻塞。 ACS诊断构成了一个高度复杂的问题，智能系统代表了优化诊断的机会。本作工作的目的是对合作理性剂联合的交叉验证，用于诊断患有胸痛的高概率的群体中的AC。进行了对诊断测试的研究，诊断标准标准是第三次重新定义梗死或冠状动脉分层策略。该指数测试是基于基于基于主网络组装的协作理性剂联合的系统的结果，通过根据阳性似然比基于神经网络的组装。计算了108名患者的样本，并建造了应急表以计算操作特性。考虑了148例患者，ACS丢弃29,2％，51,7表现出急性梗塞，19,1％表现出不稳定的心绞痛。联邦系统达到79％，敏感性为97,1％，特异性为36,4％，AUC为0,672。得出结论是，基于神经网络组装的多种子体系统实现了具有高概率群体中ACS的可接受的性能。

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