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      • Department of Electrical and Electronics Engineering
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      Adaptive ensemble learning with confidence bounds for personalized diagnosis

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      Author
      Tekin, Cem
      Yoon, J.
      Van Der Schaar, M.
      Date
      2016
      Source Title
      Proceedings of the 30th AAAI Conference on Artificial Intelligence, 2016
      Publisher
      AAAI Press
      Volume
      WS-16-08
      Pages
      462 - 468
      Language
      English
      Type
      Conference Paper
      Item Usage Stats
      233
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      38
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      Abstract
      With the advances in the field of medical informatics, automated clinical decision support systems are becoming the de facto standard in personalized diagnosis. In order to establish high accuracy and confidence in personalized diagnosis, massive amounts of distributed, heterogeneous, correlated and high-dimensional patient data from different sources such as wearable sensors, mobile applications, Electronic Health Record (EHR) databases etc. need to be processed. This requires learning both locally and globally due to privacy constraints and/or distributed nature of the multimodal medical data. In the last decade, a large number of meta-learning techniques have been proposed in which local learners make online predictions based on their locally-collected data instances, and feed these predictions to an ensemble learner, which fuses them and issues a global prediction. However, most of these works do not provide performance guarantees or, when they do, these guarantees are asymptotic. None of these existing works provide confidence estimates about the issued predictions or rate of learning guarantees for the ensemble learner. In this paper, we provide a systematic ensemble learning method called Hedged Bandits, which comes with both long run (asymptotic) and short run (rate of learning) performance guarantees. Moreover, we show that our proposed method outperforms all existing ensemble learning techniques, even in the presence of concept drift.
      Keywords
      Artificial intelligence
      Big data
      Cognitive systems
      Computer games
      Computer programming
      Computer systems programming
      Data mining
      Decision support systems
      Machine learning
      Ensemble learning
      Online learning
      Hospital data processing
      Hybrid systems
      Learning algorithms
      Learning systems
      Population statistics
      Clinical decision support systems
      Electronic health record
      Global predictions
      Medical informatics
      Meta-learning techniques
      Mobile applications
      Performance guarantees
      Privacy constraints
      Confidence bounds
      Permalink
      http://hdl.handle.net/11693/37524
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      • Department of Electrical and Electronics Engineering 3637
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