Intelligent Postoperative Morbidity Prediction of Heart Disease Using Artificial Intelligence Techniques

doi:10.1007/s10916-010-9640-7

機構典藏 > College of Engineering > Graduate Institute & Department of Computer Science and Information Engineering > Journal Article > Item 987654321/98387

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/98387

Title:	Intelligent Postoperative Morbidity Prediction of Heart Disease Using Artificial Intelligence Techniques
Authors:	Hsieh, Nan-Chen;Hung, Lun-Ping;Shih, Chun-Che;Keh, Huan-Chao;Chan, Chien-Hui
Contributors:	淡江大學資訊工程學系
Keywords:	Endovascular aneurysm repair (EVAR);Postoperative morbidity;Ensemble model;Machine learning;Markov blanket
Date:	2010-12-24
Issue Date:	2014-07-24 14:06:34 (UTC+8)
Publisher:	New York: Springer New York LLC
Abstract:	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.
Relation:	Journal of Medical Systems 36(3), pp.1809-1820
DOI:	10.1007/s10916-010-9640-7
Appears in Collections:	[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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