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Artificial intelligence (AI) in breast cancer care - Leveraging multidisciplinary skills to improve care.

作者信息

Cardoso Maria Joao, Houssami Nehmat, Pozzi Giuseppe, Séroussi Brigitte

机构信息

Breast Unit, Champalimaud Clinical Center, Champalimaud Foundation, Lisbon, Portugal; Faculdade de Medicina da Universidade Nova de Lisboa, Nova Medical School, Lisbon, Portugal.

Sydney School of Public Health, Faculty of Medicine and Health, Fisher Road, The University of Sydney, New South Wales, Australia.

出版信息

Breast. 2021 Apr;56:110-113. doi: 10.1016/j.breast.2020.11.012. Epub 2020 Dec 9.

DOI:10.1016/j.breast.2020.11.012
PMID:33308879
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7982546/
Abstract
摘要

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Autoencoded DNA methylation data to predict breast cancer recurrence: Machine learning models and gene-weight significance.基于自动编码的 DNA 甲基化数据预测乳腺癌复发：机器学习模型和基因权重意义。
Artif Intell Med. 2020 Nov;110:101976. doi: 10.1016/j.artmed.2020.101976. Epub 2020 Oct 22.
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Domain expertise-agnostic feature selection for the analysis of breast cancer data.乳腺癌数据分析的领域专业知识无关特征选择。
Artif Intell Med. 2020 Aug;108:101928. doi: 10.1016/j.artmed.2020.101928. Epub 2020 Jul 16.
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Implementation of an ontological reasoning to support the guideline-based management of primary breast cancer patients in the DESIREE project.在DESIREE项目中实施本体推理以支持原发性乳腺癌患者的基于指南的管理。
Artif Intell Med. 2020 Aug;108:101922. doi: 10.1016/j.artmed.2020.101922. Epub 2020 Jul 2.
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A case-based ensemble learning system for explainable breast cancer recurrence prediction.一种用于可解释性乳腺癌复发预测的基于案例的集成学习系统。
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