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Diagnosing Pulmonary EmbolismDiagnosing pulmonary embolism (PE) involves clinical assessment and advanced imaging tests. The preferred diagnostic tool is the spiral (helical) CT scan or CT angiography (CTA), which uses intravenous contrast media to visualize the pulmonary vasculature and identify emboli.A ventilation-perfusion (V/Q) scan is an alternative for patients unable to receive contrast media. This scan includes both perfusion and ventilation scanning. Perfusion scanning involves...
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Related Experiment Videos

A knowledge-based artificial neural network classifier for pulmonary embolism diagnosis.

G Serpen1, D K Tekkedil, M Orra

  • 1Department of Electrical Engineering and Computer Science, The University of Toledo, Toledo, OH 43606, USA. gserpen@eng.utoledo.edu

Computers in Biology and Medicine
|November 21, 2007
PubMed
Summary

Knowledge-based hybrid learning algorithms, specifically a knowledge-based artificial neural network (KBANN), outperform purely empirical methods for diagnosing pulmonary embolism (PE). This approach effectively leverages existing medical knowledge for improved classification accuracy.

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Area of Science:

  • Artificial Intelligence
  • Medical Informatics
  • Machine Learning

Background:

  • Pulmonary embolism (PE) diagnosis presents a complex classification task.
  • Existing medical knowledge, such as the modified Prospective Investigation of Pulmonary Embolism Diagnosis (PIOPED) criteria, can be formalized.
  • Purely empirical machine learning algorithms may not fully exploit domain-specific knowledge.

Purpose of the Study:

  • To demonstrate the superiority of knowledge-based hybrid learning algorithms over empirical methods for PE diagnosis.
  • To leverage the PIOPED criteria as a knowledge base for a hybrid classifier.
  • To develop and evaluate a knowledge-based artificial neural network (KBANN) for PE diagnosis.

Main Methods:

  • Instantiated a knowledge-based artificial neural network (KBANN) classifier using the modified PIOPED criteria.
  • Captured and refined the PIOPED rule base using specialized domain expertise.
  • Compared the KBANN's performance against several empirical machine learning algorithms (naive Bayes, Bayesian Belief network, multilayer perceptron, C4.5, boosting, bagging) on a dedicated testing dataset.

Main Results:

  • The KBANN successfully modeled and utilized the PIOPED knowledge base and its expert-refined versions.
  • The KBANN demonstrated enhanced classification performance for PE diagnosis.
  • Knowledge-based hybrid learning significantly improved diagnostic accuracy compared to empirical approaches.

Conclusions:

  • Knowledge-based hybrid learning algorithms offer a powerful approach for medical diagnosis by integrating domain knowledge.
  • The KBANN effectively leverages codified medical expertise for improved pulmonary embolism classification.
  • This study validates the benefit of combining explanation-based and empirical learning for complex diagnostic tasks.