A Machine Learning System to Improve Heart Failure Patient Assistance
A Machine Learning System to Improve Heart Failure Patient Assistance
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DOI：
10.1109/jbhi.2014.2337752
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发表时间：
2014-11-01
影响因子：
7.7
通讯作者：
Iadanza, Ernesto
中科院分区：
文献类型：
--
作者：
Guidi, Gabriele;Pettenati, Maria Chiara;Iadanza, Ernesto
在本文中，我们提出了一个临床决策支持系统（CDSS），用于分析心力衰竭（HF）患者，提供各种输出，如HF严重程度评估，HF类型预测，以及比较不同患者随访的管理界面。整个系统由智能核心和HF专用管理工具组成，并提供人工智能训练和使用的接口功能。为了实现智能功能，我们采用了机器学习的方法。在本文中，我们比较了神经网络（NN）、支持向量机、遗传产生的模糊规则系统、分类回归树及其直接进化即随机森林在分析数据库中的性能。随机森林算法在高频严重程度评价和高频类型预测函数上均具有最佳性能。管理工具允许心脏病专家在他或她的定期门诊咨询期间填充适合机器学习的“监督数据库”。这个想法来自于这样一个事实，即在文献中有一些这种类型的数据库，它们不能扩展到我们的案例中。
In this paper, we present a clinical decision support system (CDSS) for the analysis of heart failure (HF) patients, providing various outputs such as an HF severity evaluation, HF-type prediction, as well as a management interface that compares the different patients' follow-ups. The whole system is composed of a part of intelligent core and of an HF special-purpose management tool also providing the function to act as interface for the artificial intelligence training and use. To implement the smart intelligent functions, we adopted a machine learning approach. In this paper, we compare the performance of a neural network (NN), a support vector machine, a system with fuzzy rules genetically produced, and a classification and regression tree and its direct evolution, which is the random forest, in analyzing our database. Best performances in both HF severity evaluation and HF-type prediction functions are obtained by using the random forest algorithm. The management tool allows the cardiologist to populate a "supervised database" suitable for machine learning during his or her regular outpatient consultations. The idea comes from the fact that in literature there are a few databases of this type, and they are not scalable to our case.