Preliminary analysis of explainable machine learning methods for multiple myeloma chemotherapy treatment recognition
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Cognitive Neuroscience,Computer Vision and Pattern Recognition,Mathematics (miscellaneous)
Link
https://link.springer.com/content/pdf/10.1007/s12065-023-00833-3.pdf
Reference57 articles.
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2. Breiman L (2001) Random forests. Mach Learn 45:5–32
3. Burkart N, Huber MF (2021) A survey on the explainability of supervised machine learning. J Artif Int Res 70:245–317. https://doi.org/10.1613/jair.1.12228
4. de Campos Aranovich T, Matulionyte R (2022) Ensuring ai explainability in healthcare: problems and possible policy solutions. Inform Commun Technol Law. https://doi.org/10.1080/13600834.2022.2146395
5. Cavo M, Tacchetti P, Patriarca F et al (2010) Bortezomib with thalidomide plus dexamethasone compared with thalidomide plus dexamethasone as induction therapy before, and consolidation therapy after, double autologous stem-cell transplantation in newly diagnosed multiple myeloma: a randomised phase 3 study. The Lancet 376(9758):2075–2085. https://doi.org/10.1016/S0140-6736(10)61424-9
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