Why implementing machine learning algorithms in the clinic is not a plug-and-play solution: a simulation study of a machine learning algorithm for acute leukaemia subtype diagnosis
This study was supported by the DFG-cofounded UMEA Clinician Scientist Program and the Ministry of Culture and Science of the State of North Rhine-Westphalia.
Authors
- Jens Kleesiek (ORCID: https://orcid.org/0000-0001-8686-0682)
- Hans Christian Reinhardt (ORCID: https://orcid.org/0000-0001-5706-9349)
- Gernot Pucher (ORCID: https://orcid.org/0000-0002-3519-0217)
- Christopher Martin Sauer (ORCID: https://orcid.org/0000-0002-2388-5919)
- Till Rostalski (ORCID: https://orcid.org/0000-0003-0064-225X)
- Felix Nensa (ORCID: https://orcid.org/0000-0002-5811-7100)
Institutions
- Essen University Hospital (DE)
- West German Heart and Vascular Center Essen (DE)
Publication Details
- Journal
- EBioMedicine
- Published
- 2024-12-24
- DOI
- https://doi.org/10.1016/j.ebiom.2024.105526
- Citations
- 2
- Primary Topic
- Digital Imaging for Blood Diseases
- Type
- article
- Field-Weighted Citation Impact
- 0.50
Funders
- Deutsche Forschungsgemeinschaft
- Bundesministerium für Bildung und Forschung
- Else Kröner-Fresenius-Stiftung
- Deutsche Krebshilfe
- Institut National Du Cancer
- Universität Duisburg-Essen
- Siemens Healthineers