Intelligent Postoperative Morbidity Prediction of Heart Disease Using Artificial Intelligence Techniques
Intelligent Postoperative Morbidity Prediction of Heart Disease Using Artificial Intelligence Techniques
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DOI：
10.1007/s10916-010-9640-7
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发表时间：
2012-06-01
影响因子：
5.3
通讯作者：
Chan, Chien-Hui
中科院分区：
文献类型：
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作者：
Hsieh, Nan-Chen;Hung, Lun-Ping;Chan, Chien-Hui
腹主动脉瘤腔内修复术（EVAR）是一种先进的微创手术技术，有助于减少患者的恢复时间、术后发病率和死亡率。本研究提出了一种预测腹主动脉瘤腔内修复术后发病率的整体模型。使用2000年至2009年期间接受腹主动脉瘤腔内修复术的连续患者的训练集开发整体模型。前瞻性收集预测建模所需的所有数据，包括患者人口统计学、术前、合并症和并发症作为结局变量，并输入临床数据库。一个离散化的方法被用来分类到信息的特征空间的数值。然后，采用贝叶斯网络（BN）、人工神经网络（ANN）和支持向量机（SVM）作为基本模型，并将多个模型叠加在一起。研究结果包括预测腹主动脉瘤腔内修复术后发病率的整体模型、前瞻性记录的术后并发症发生率以及采用马尔可夫毯概念的BN的因果效应知识。
Endovascular aneurysm repair (EVAR) is an advanced minimally invasive surgical technology that is helpful for reducing patients' recovery time, postoperative morbidity and mortality. This study proposes an ensemble model to predict postoperative morbidity after EVAR. The ensemble model was developed using a training set of consecutive patients who underwent EVAR between 2000 and 2009. All data required for prediction modeling, including patient demographics, preoperative, co-morbidities, and complication as outcome variables, was collected prospectively and entered into a clinical database. A discretization approach was used to categorize numerical values into informative feature space. Then, the Bayesian network (BN), artificial neural network (ANN), and support vector machine (SVM) were adopted as base models, and stacking combined multiple models. The research outcomes consisted of an ensemble model to predict postoperative morbidity after EVAR, the occurrence of postoperative complications prospectively recorded, and the causal effect knowledge by BNs with Markov blanket concept.