Using Machine Learning on Imbalanced Guideline Compliance Data to Optimize Multidisciplinary Tumour Board Decision Making for the Management of Breast Cancer Patients - Archive ouverte HAL Accéder directement au contenu
Chapitre D'ouvrage Année : 2022

Using Machine Learning on Imbalanced Guideline Compliance Data to Optimize Multidisciplinary Tumour Board Decision Making for the Management of Breast Cancer Patients

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Résumé

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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Dates et versions

hal-03767792 , version 1 (24-11-2022)

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Citer

My-Anh Le Thien, Akram Redjdal, Jacques Bouaud, Brigitte Séroussi. Using Machine Learning on Imbalanced Guideline Compliance Data to Optimize Multidisciplinary Tumour Board Decision Making for the Management of Breast Cancer Patients. Studies in Health Technology and Informatics, 2022, ⟨10.3233/shti220186⟩. ⟨hal-03767792⟩
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