Surgical Duration Estimation via Data Mining and Predictive Modeling

A Case Study

N. Hosseini, Mustafa Sir, C. J. Jankowski, Kalyan S Pasupathy

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Operating rooms (ORs) are one of the most expensive and profitable resources within a hospital system. OR managers strive to utilize these resources in the best possible manner. Traditionally, surgery durations are estimated using a moving average adjusted by the scheduler (adjusted system prediction or ASP). Other methods based on distributions, regression and data mining have also been proposed. To overcome difficulties with numerous procedure types and lack of sufficient sample size, and avoid distributional assumptions, the main objective is to develop a hybrid method of duration prediction and demonstrate using a case study.

Original languageEnglish (US)
Pages (from-to)640-648
Number of pages9
JournalAMIA ... Annual Symposium proceedings. AMIA Symposium
Volume2015
StatePublished - Jan 1 2015

Fingerprint

Data Mining
Operating Rooms
Sample Size

Keywords

  • Classification
  • hybrid method
  • prediction
  • regression
  • surgery times

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Surgical Duration Estimation via Data Mining and Predictive Modeling : A Case Study. / Hosseini, N.; Sir, Mustafa; Jankowski, C. J.; Pasupathy, Kalyan S.

In: AMIA ... Annual Symposium proceedings. AMIA Symposium, Vol. 2015, 01.01.2015, p. 640-648.

Research output: Contribution to journalArticle

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