Machine Learning Model to Guide Empirical Antimicrobial Therapy in Febrile Neutropenic Patients With Hematologic Malignancies.

Journal: Anticancer research
Published Date: Jun 1, 2025

Abstract

BACKGROUND/AIM: Optimal antimicrobial selection for patients with febrile neutropenia (FN) may differ depending on the underlying mechanisms. We aimed to develop a model for predicting the severity of bacteremia in patients with FN and hematologic malignancies (HMs) to help clinicians select appropriate antimicrobials using a machine-learning approach.

Authors

  • Kosuke Hoashi
    Department of Hematology, Iizuka Hospital, Iizuka, Japan; khoashih1@aih-net.com.
  • Koutarou Matsumoto
    Saiseikai Kumamoto Hospital, Kumamoto, Japan.
  • Junichi Kiyasu
    Department of Hematology, Iizuka Hospital, Iizuka, Japan.
  • Taro Sawabe
    Department of Hematology, Iizuka Hospital, Iizuka, Japan.
  • Makoto Oyama
    Department of Hematology, Iizuka Hospital, Iizuka, Japan.
  • Mariko Tsuda
    Department of Hematology, Iizuka Hospital, Iizuka, Japan.
  • Akiko Takamatsu
    Department of Hematology, Iizuka Hospital, Iizuka, Japan.
  • Eriko Fujioka
    Department of Hematology, Iizuka Hospital, Iizuka, Japan.
  • Yuji Yufu
    Department of Hematology, Iizuka Hospital, Iizuka, Japan.
  • Motoaki Shiratsuchi
    Department of Hematology, Iizuka Hospital, Iizuka, Japan.
  • Kenta Murotani
    Biostatistics Center, Kurume University, Fukuoka, Japan.

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