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

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)

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, 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.

Original languageEnglish
Pages (from-to)1809-1820
Number of pages12
JournalJournal of Medical Systems
Volume36
Issue number3
DOIs
Publication statusPublished - Jun 1 2012
Externally publishedYes

Keywords

  • Endovascular aneurysm repair (EVAR)
  • Ensemble model
  • Machine learning
  • Markov blanket
  • Postoperative morbidity

ASJC Scopus subject areas

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

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