Machine learning-based radiotherapy time prediction and treatment scheduling management.

Journal: Journal of applied clinical medical physics
Published Date:

Abstract

PURPOSE: The utility efficiency of medical devices is important, especially for countries such as China, which have a large population and shortage of medical care resources. Radiotherapy devices are among the most valuable and specialized equipment categories and carry enormous treatment loads. In this study, a novel method is proposed to improve the efficiency of a radiotherapy device (linac). Although scheduling management with accurate prediction of the entire treatment time included in each appointment, arrange a reasonable time duration for appointments and save time between patient shifts effectively. Tasks belonging to the treatment and non-treatment groups can be assigned more flexibly based on the availability of time.

Authors

  • Lisiqi Xie
    School of Information Science and Engineering, Yunnan University, Kunming, China.
  • Dan Xu
    Department of Orthodontics, The Affiliated Stomatological Hospital of Southwest Medical University, Luzhou, China.
  • Kangjian He
    School of Information Science and Engineering, Yunnan University, Kunming, China.
  • Xin Tian
    Cancer Hospital Chinese Academy of Medical Sciences (Shenzhen Hospital), Shenzhen, 518000, China. Electronic address: 947952187@qq.com.