The prediction model of medical expenditure appling machine learning algorithm in cabg patients

Yen Chun Huang, Shao Jung Li, Mingchih Chen, Tian Shyug Lee

研究成果: 雜誌貢獻文章同行評審

1 引文 斯高帕斯(Scopus)

摘要

Most patients face expensive healthcare management after coronary artery bypass grafting (CABG) surgery, which brings a substantial financial burden to the government. The National Health Insurance Research Database (NHIRD) is a complete database containing over 99% of individuals’ medical information in Taiwan. Our research used the latest data that selected patients who accepted their first CABG surgery between January 2014 and December 2017 (n = 12,945) to predict which factors will affect medical expenses, and built the prediction model using different machine learning algorithms. After analysis, our result showed that the surgical expenditure (X4) and 1-year medical expenditure before the CABG operation (X14), and the number of hemodialysis (X15), were the key factors affecting the 1-year medical expenses of CABG patients after discharge. Furthermore, the XGBoost and SVR methods are both the best predictive models. Thus, our research suggests enhancing the healthcare management for patients with kidney-related diseases to avoid costly complications. We provide helpful information for medical management, which may decrease health insurance burdens in the future.
原文英語
文章編號710
期刊Healthcare (Switzerland)
9
發行號6
DOIs
出版狀態已發佈 - 6月 2021

ASJC Scopus subject areas

  • 健康資訊學
  • 健康政策
  • 健康資訊管理
  • 領導和管理

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