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Using Machine Learning on Imbalanced Guideline Compliance Data to Optimize Multidisciplinary Tumour Board Decision
My-Anh Le Thien1, Akram Redjdal1, Jacques Bouaud2,1
1Sorbonne Université, Université Sorbonne Paris Nord, Inserm, UMR S_1142, LIMICS, Paris, France.
Abstract:
Complex breast cancer cases that need further multidisciplinary tumor board (MTB) discussions should have priority in the organization of MTBs. In order to optimize MTB workflow, we attempted to predict complex cases defined as non-compliant cases despite the use of the decision support system OncoDoc, through the implementation of machine learning procedures and algorithms (Decision Trees, Random Forests, and XGBoost). F1-score after cross-validation, sampling implementation, with or without feature selection, did not exceed 40%.
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