Intelligent Postoperative Morbidity Prediction of Heart Disease Using Artificial Intelligence Techniques View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2012-06

AUTHORS

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

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. More... »

PAGES

1809-1820

References to SciGraph publications

  • 2001. MAMBO: Discovering Association Rules Based on Conditional Independencies in ADVANCES IN INTELLIGENT DATA ANALYSIS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10916-010-9640-7

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    http://dx.doi.org/10.1007/s10916-010-9640-7

    DIMENSIONS

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    PUBMED

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