Intelligent postoperative morbidity prediction of heart disease using artificial intelligence techniques

Nan Chen Hsieh, Lun Ping Hung, Chun Che Shih, Huan Chao Keh, Chien Hui Chan

研究成果: 雜誌貢獻文章同行評審

21 引文 斯高帕斯(Scopus)

摘要

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, comorbidities, 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.
原文英語
頁(從 - 到)1809-1820
頁數12
期刊Journal of Medical Systems
36
發行號3
DOIs
出版狀態已發佈 - 六月 1 2012
對外發佈Yes

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Information Systems
  • Health Informatics
  • Health Information Management

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