An active learning framework for efficient condition severity classification

Nir Nissim, Mary Regina Boland, Robert Moskovitch, Nicholas P. Tatonetti, Yuval Elovici, Yuval Shahar, George Hripcsak

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

20 Scopus citations


Understanding condition severity, as extracted from Electronic Health Records (EHRs), is important for many public health purposes. Methods requiring physicians to annotate condition severity are time-consuming and costly. Previously, a passive learning algorithm called CAESAR was developed to capture severity in EHRs. This approach required physicians to label conditions manually, an exhaustive process. We developed a framework that uses two Active Learning (AL) methods (Exploitation and Combination_XA) to decrease manual labeling efforts by selecting only the most informative conditions for training. We call our approach CAESAR-Active Learning Enhancement (CAESAR-ALE). As compared to passive methods, CAESAR-ALE’s first AL method, Exploitation, reduced labeling efforts by 64% and achieved an equivalent true positive rate, while CAESARALE’s second AL method, Combination_XA, reduced labeling efforts by 48% and achieved equivalent accuracy. In addition, both these AL methods outperformed the traditional AL method (SVM-Margin). These results demonstrate the potential of AL methods for decreasing the labeling efforts of medical experts, while achieving greater accuracy and lower costs.

Original languageEnglish
Title of host publicationArtificial Intelligence in Medicine - 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Proceedings
EditorsRiccardo Bellazzi, Lucia Sacchi, John H. Holmes, Niels Peek
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783319195506
StatePublished - 1 Jan 2015
Event15th Conference on Artificial Intelligence in Medicine, AIME 2015 - Pavia, Italy
Duration: 17 Jun 201520 Jun 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th Conference on Artificial Intelligence in Medicine, AIME 2015


  • Active-learning
  • Condition
  • Electronic health records
  • Phenotyping

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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