A probabilistic algorithm with user feedback loop for decision making during the hospital triage process

Dimitrios Zikos, Ismail Vandeliwala, Philip Makedon

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

Abstract

In this paper, we describe a probabilistic algorithm with user feedback loop, which can be used for decision making during the patient triage process. Given an R{x, y} the method relies on the user defining a set of x values (i.e. symptoms) and the algorithm returns a collection of y values as a hidden layer (possible diseases), taking into consideration a possible false negative user reporting, by looking into candidate values of y and identifying x values (symptoms) which have not been initially provided by the user. The user can specify parameters such as the minimum probability ratio of the final output, the minimum probability ratio of the y values for which the non-user given x values will be re-evaluated, and the maximum number of user feedback loops. In order to validate the method, we use a comprehensive 2012 Medicare Claims dataset with 15 million cases.

Original languageEnglish
Title of host publicationProceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2014
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450327466
DOIs
StatePublished - May 27 2014
Externally publishedYes
Event7th ACM International Conference on Pervasive Technologies Related to Assistive Environments, PETRA 2014 - Rhodes, Greece
Duration: May 27 2014May 30 2014

Publication series

NameACM International Conference Proceeding Series
Volume2014-May

Conference

Conference7th ACM International Conference on Pervasive Technologies Related to Assistive Environments, PETRA 2014
Country/TerritoryGreece
CityRhodes
Period05/27/1405/30/14

Keywords

  • Healthcare
  • Machine learning
  • Probabilistic algorithm
  • Triage

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