Multimodal deep learning models utilizing chest X-ray and electronic health record data for predictive screening of acute heart failure in emergency department.

Journal: Computer methods and programs in biomedicine
PMID: 39126913

Abstract

BACKGROUND AND OBJECTIVES: Ambiguity in diagnosing acute heart failure (AHF) leads to inappropriate treatment and potential side effects of rescue medications. To address this issue, this study aimed to use multimodality deep learning models combining chest X-ray (CXR) and electronic health record (EHR) data to screen patients with abnormal N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels in emergency departments.

Authors

  • Chih-Kuo Lee
    Department of Internal Medicine, National Taiwan University HsinChu Hospital, HsinChu, Taiwan.
  • Ting-Li Chen
    Graduate Program of Data Science, National Taiwan University and Academia Sinica, Taipei, Taiwan.
  • Jeng-En Wu
    Master Program in Statistics, National Taiwan University, 1, Sec. 4 Roosevelt Rd., Taipei 106, Taiwan, ROC.
  • Min-Tsun Liao
    Department of Internal Medicine, National Taiwan University Hospital Hsin-Chu Branch, No. 25, Ln. 442, Sec. 1, Jingguo Rd., Hsinchu 300, Taiwan, ROC.
  • Chiehhung Wang
    Data Science Degree Program, National Taiwan University, 1, Sec. 4 Roosevelt Rd., Taipei 106, Taiwan, ROC.
  • Weichung Wang
    Graduate Program of Data Science, National Taiwan University and Academia Sinica, Taipei, Taiwan.
  • Cheng-Ying Chou
    Department of Biomechatronics Engineering, National Taiwan University, Taipei 10617, Taiwan.

Keywords

External Resources

Popular Topics
Recent Journals