Machine Learning‐Based Prediction of Life‐Threatening Complications During Hemodialysis in Hospitalized Patients With Poor General Conditions
Our model enables the early identification of high-risk inpatients receiving hemodialysis using pre-dialysis data, thereby supporting timely clinical interventions, optimized resource allocation, and improved patient safety.
Authors
- Kazuyoshi Hirota (ORCID: https://orcid.org/0000-0001-6936-0985)
- Kazunori Naganuma
- Shingo Hatakeyama (ORCID: https://orcid.org/0000-0002-0026-4079)
- Yoshihiro Sasaki
- Tomoyuki Ohira
- Hirotaka Kinoshita (ORCID: https://orcid.org/0000-0003-4490-3941)
- Junko Ogasawara
- Chikara Οhyama (ORCID: https://orcid.org/0000-0003-1550-8379)
- Takeshi Goto (ORCID: https://orcid.org/0000-0001-6975-8248)
- Ken Kurokawa (ORCID: https://orcid.org/0000-0001-8662-9958)
- Naotaka Kato
- Chikako Ohminato
Institutions
- Hirosaki University (JP)
- Hirosaki University Hospital (JP)
Publication Details
- Journal
- Artificial Organs
- Published
- 2025-09-20
- DOI
- https://doi.org/10.1111/aor.70008
- Citations
- 2
- Primary Topic
- Dialysis and Renal Disease Management
- Type
- article
- Field-Weighted Citation Impact
- 2.86