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  • 标题:A Term-based Approach to Asyndromic Determination of Significant Case Clusters
  • 本地全文:下载
  • 作者:Howard Burkom ; Yevgeniy Elbert ; Christine Piatko
  • 期刊名称:Online Journal of Public Health Informatics
  • 电子版ISSN:1947-2579
  • 出版年度:2015
  • 卷号:7
  • 期号:1
  • 语种:English
  • 出版社:University of Illinois at Chicago
  • 摘要:The presented method is a practical, understandable way to monitor single care facilities for chief complaint clusters of concern based on unusually high occurrence of rare or common terms that need not be related to syndromes. Routine implementation requires a human monitor to inspect the relevant CCs make follow-up decisions. Using 7 years of patient records from 15 hospitals, our approach pools CCs into contiguous time blocks and uses a statistical hypothesis test to seek current terms that are anomalous relative to their occurrence in a large sliding baseline. Sets of anomalous terms are then presented for further investigation.
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