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Explaining artificial neural network ensembles: A case study with electrocardiograms from chest pain patients

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Author

Editor

  • Milos Hauskrecht

Summary, in English

Artificial neural networks is one of the most commonly used machine learning algorithms in medical applications. However, they are still not used in practice in the clinics partly due to their lack of explanatory capacity. We compare two case-based explanation methods to two trained physicians on analysis of electrocardiogram (ECG) data from patients with a suspected acute coronary syndrome (ACS). The median overlaps of the top 5 selected features between the two physicians, and a given physician and a method, were initially low. Using a correlation analysis of the features the median overlap increased to values typically in the range 3-4. In conclusion, both our case-based methods generate explanations similar to those of trained expert physicians on the problem of diagnosing ACS from ECG data.

Publishing year

2008

Language

English

Publication/Series

Proceedings of the ICML/UAI/COLT 2008 Workshop on Machine Learning for Health-Care Applications

Document type

Conference paper

Topic

  • Medical and Health Sciences

Keywords

  • acute coronary syndrome
  • case-based explanation
  • rule extraction
  • neural network ensembles

Conference name

International Conference on Machine Learning

Conference date

2008-07-05 - 2008-07-09

Conference place

Helsinki, Finland

Status

Published

Project

  • AIR Lund Chest pain - More efficient and equal emergency care with advanced medical decision support tools

Research group

  • Nuclear medicine, Malmö