Machine Learning Model to Guide Empirical Antimicrobial Therapy in Febrile Neutropenic Patients With Hematologic Malignancies
This pilot study introduces a novel method for constructing predictive models tailored to specific patient groups, potentially supporting antimicrobial stewardship.
Authors
- Kenta Murotani (ORCID: https://orcid.org/0000-0003-0623-9365)
- Takuya Sawabe (ORCID: https://orcid.org/0009-0000-6578-9688)
- Yuji Yufu (ORCID: https://orcid.org/0009-0003-5549-0932)
- Junichi Kiyasu (ORCID: https://orcid.org/0000-0001-7596-0821)
- Kosuke Hoashi
- Motoaki Shiratsuchi (ORCID: https://orcid.org/0000-0002-0403-0122)
- Oyama Makoto
- Eriko Fujioka
- Mariko Tsuda (ORCID: https://orcid.org/0000-0002-4177-3654)
- Kazuhide Matsumoto (ORCID: https://orcid.org/0000-0003-0842-6500)
- Akiko Takamatsu
Institutions
- Kurume University (JP)
- Kyushu University (JP)
- Kurume University Medical Center (JP)
- Aso Iizuka Hospital (JP)
Publication Details
- Journal
- Anticancer Research
- Published
- 2025-05-27
- DOI
- https://doi.org/10.21873/anticanres.17634
- Primary Topic
- Neutropenia and Cancer Infections
- Type
- article
- Field-Weighted Citation Impact
- 0.00