English  |  正體中文  |  简体中文  |  Items with full text/Total items : 6024/14565 (41%)
Visitors : 13764499      Online Users : 129
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: https://ir.fy.edu.tw:8080/ir/handle/987654321/17061

    Title: Predicting the Prolonged Length of Stay of General Surgery Patients: A Supervised Learning Approach
    Authors: Chuang, Mao-Te;Hu, Ya-han;Lo, Chia-Lun
    Contributors: 輔英科技大學 健康事業管理系
    Date: 2016-06-01
    Issue Date: 2016-11-23 16:57:17 (UTC+8)
    Abstract: Determining the likelihood of a prolonged length of stay (LOS) for surgery patients can improve medical resource management. This study was aimed at developing predictive models for determining whether patient LOS is within the standard LOS after surgery. This study analyzed the complete historical medical records and lab data of 896 clinical cases involving surgeries performed by general surgery physicians. The cases were divided into urgent operation (UO) and non-UO groups to develop a prolonged LOS prediction model using several supervised learning techniques. Several critical factors for the two groups were identified using the gain ratio technique. The results indicated that the random forest method yielded the most accurate and stable prediction model. Additionally, comorbidity, body temperature, blood sugar, and creatinine were the most influential variables for prolonged LOS in the UO group, whereas blood transfusion, blood pressure, comorbidity, and the number of ICU admissions were the most influential variables in the non-UO group. This study shows that supervised learning techniques are suitable for analyzing patient medical records in accurately predicting a prolonged LOS; thus, the clinical decision support system developed based on the prediction models may serve as reference tools for communicating with patients before surgery. The system may also assist physicians when making decisions regarding whether patients require more clinical care, thereby improving patient safety.
    Relation: International Transactions in Operational Research
    Appears in Collections:[健康事業管理系] 期刊論文

    Files in This Item:

    There are no files associated with this item.

    All items in FYIR are protected by copyright, with all rights reserved.


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback