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Development of prediction models for one-year brain tumour survival using machine learning: a comparison of accuracy and interpretability.

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第一作者： Colleen E,Charlton

第一单位： Artificial Intelligence and its Applications Institute, School of Informatics, University of Edinburgh, 10 Crichton Street, Edinburgh EH8 9AB, UK. Electronic address: colleen.charlton@camh.ca.

作者： Colleen E,Charlton ^[1] ; Michael T C,Poon ^[2] ; Paul M,Brennan ^[3] ; Jacques D,Fleuriot ^[4]

作者单位： Artificial Intelligence and its Applications Institute, School of Informatics, University of Edinburgh, 10 Crichton Street, Edinburgh EH8 9AB, UK. Electronic address: colleen.charlton@camh.ca. ^[1] Cancer Research UK Brain Tumour Centre of Excellence, CRUK Edinburgh Centre, University of Edinburgh, Edinburgh, UK; Department of Clinical Neuroscience, Royal Infirmary of Edinburgh, 51 Little France Crescent EH16 4SA, UK.; Translational Neurosurgery, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK. ^[2] Cancer Research UK Brain Tumour Centre of Excellence, CRUK Edinburgh Centre, University of Edinburgh, Edinburgh, UK; Department of Clinical Neuroscience, Royal Infirmary of Edinburgh, 51 Little France Crescent EH16 4SA, UK.; Translational Neurosurgery, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK. ^[3] Artificial Intelligence and its Applications Institute, School of Informatics, University of Edinburgh, 10 Crichton Street, Edinburgh EH8 9AB, UK. ^[4]

关键词 Bayesian rule lists Brain cancer Explainable boosting machine Interpretable models Machine learning Survival

医学主题词人类(Humans);贝叶斯定理(Bayes Theorem);回顾性研究(Retrospective Studies);脑肿瘤(Brain Neoplasms);脑(Brain)

DOI 10.1016/j.cmpb.2023.107482

PMID 36947980

发布时间 2023-04-11

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