GoldenEye++: A Closer Look into the Black Box

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• 作者：Andreas Henelius (7)
  Kai Puolam盲ki (7)
  Isak Karlsson (8)
  Jing Zhao (8)
  Lars Asker (8)
  Henrik Bostr枚m (8)
  Panagiotis Papapetrou (8)
  
  7. Finnish Institute of Occupational Health ; PO Box 40 ; 00251 ; Helsinki ; Finland
  8. Department of Computer and Systems Sciences ; Stockholm University ; Forum 100 ; 164 40 ; Kista ; Sweden
  
• 关键词：Classifiers ; Randomization ; Adverse drug events.
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9047
• 期：1
• 页码：96-105
• 全文大小：176 KB
• 参考文献：1. Henelius, A, Puolam盲ki, K, Bostr枚m, H, Asker, L, Papapetrou, P (2014) A peek into the black box: exploring classifiers by randomization. Data Mining and Knowledge Discovery 28: pp. 1503-1529 CrossRef
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  8. Chawla, N.V.: Data mining for imbalanced datasets: an overview. In: Data mining and Knowledge Discovery Handbook, pp. 853鈥?67. Springer (2005)
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  13. R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2014)
  
• 作者单位：Statistical Learning and Data Sciences
• 丛书名：978-3-319-17090-9
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Models with high predictive performance are often opaque, i.e., they do not allow for direct interpretation, and are hence of limited value when the goal is to understand the reasoning behind predictions. A recently proposed algorithm, GoldenEye, allows detection of groups of interacting variables exploited by a model. We employed this technique in conjunction with random forests generated from data obtained from electronic patient records for the task of detecting adverse drug events (ADEs). We propose a refined version of the GoldenEye algorithm, called GoldenEye++, utilizing a more sensitive grouping metric. An empirical investigation comparing the two algorithms on 27 datasets related to detecting ADEs shows that the new version of the algorithm in several cases finds groups of medically relevant interacting attributes, corresponding to prescribed drugs, undetected by the previous version. This suggests that the GoldenEye++ algorithm can be a useful tool for finding novel (adverse) drug interactions.