The Identification of Prolonged Length of Stay for Surgery Patients

Mao Te Chuang, Ya Han Hu, Chih Fong Tsai, Chia Lun Lo, Wei Chao Lin

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

12 Scopus citations

Abstract

When the hospitalization periods of an unexpectedly high number of patients are extended, the income of a hospital is substantially affected and the rate of hospital bed occupancy increases. Because none of the currently available score systems can be used to evaluate the possibility that patients who require urgent surgery in a single department prolong their length of stay (LOS), this study attempts to build a prolonged LOS prediction model utilizing a number of supervised learning techniques. This study involved analyzing the complete historical medical records and lab data of 897 clinical cases in which surgeries were performed by general surgery physicians. These clinical cases were divided into an urgent operation (UO) group comprising 462 cases and a non-UO group comprising 434 cases to develop a prolonged LOS prediction model by using several supervised learning techniques. The results indicated that the random forest method constituted the most accurate and stable prediction model. This study demonstrated that supervised learning techniques can be used to analyze patient medical records to accurately predict a prolonged LOS, thus, supervised learning techniques can serve as valuable reference tools for patient prognoses. The developed prediction models can facilitate the decision making of physicians when patients require surgery and increase patient safety.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3000-3003
Number of pages4
ISBN (Electronic)9781479986965
DOIs
StatePublished - 12 01 2016
Externally publishedYes
EventIEEE International Conference on Systems, Man, and Cybernetics, SMC 2015 - Kowloon Tong, Hong Kong
Duration: 09 10 201512 10 2015

Publication series

NameProceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015

Conference

ConferenceIEEE International Conference on Systems, Man, and Cybernetics, SMC 2015
Country/TerritoryHong Kong
CityKowloon Tong
Period09/10/1512/10/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

  • Data Mining
  • General Surgery
  • Prolonged Length of Stay
  • Supervised Learning
  • Urgent Surgery

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