Explanation and reliability of prediction models: the case of breast cancer recurrence

Erik Štrumbelj; Zoran Bosnić; Igor Kononenko; Branko Zakotnik; Cvetka Grašič Kuhar

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Explanation and reliability of prediction models: the case of breast cancer recurrence

机译：预测模型的解释和可靠性：乳腺癌复发病例

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In this paper, we describe the first practical application of two methods, which bridge the gap between the non-expert user and machine learning models. The first is a method for explaining classifiers’ predictions, which provides the user with additional information about the decision-making process of a classifier. The second is a reliability estimation methodology for regression predictions, which helps the users to decide to what extent to trust a particular prediction. Both methods are successfully applied to a novel breast cancer recurrence prediction data set and the results are evaluated by expert oncologists.

机译：在本文中，我们描述了两种方法的首次实际应用，它们在非专家用户模型和机器学习模型之间架起了桥梁。第一种是解释分类器预测的方法，它为用户提供有关分类器决策过程的其他信息。第二种是用于回归预测的可靠性估计方法，它可以帮助用户决定在多大程度上信任特定的预测。两种方法均成功地应用于新型乳腺癌复发预测数据集，并由专业的肿瘤学家对结果进行评估。

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来源
《Knowledge and Information Systems》 |2010年第2期|p.305-324|共20页
作者
Erik Štrumbelj; Zoran Bosnić; Igor Kononenko; Branko Zakotnik; Cvetka Grašič Kuhar;
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正文语种 eng
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关键词
Data mining; Machine learning; Breast cancer; Classification explanation; Prediction reliability;

机译：数据挖掘;机器学习;乳腺癌;分类说明;预测可靠性;

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专利

1. Explanation and reliability of prediction models: the case of breast cancer recurrence [J] . Erik Strumbelj, Zoran Bosnic, Igor Kononenko, Knowledge and information systems . 2010,第2期

机译：预测模型的解释和可靠性：乳腺癌复发病例
2. Factors associated with breast cancer recurrences or mortality and dynamic prediction of death using history of cancer recurrences: the French E3N cohort [J] . Alexandre Lafourcade, Mathilde His, Laura Baglietto, BMC Cancer . 2018,第1期

机译：与乳腺癌复发或死亡相关的因素以及使用癌症复发史动态预测死亡：法国E3N队列
3. Prediction of late distant recurrence in patients with oestrogen-receptor-positive breast cancer: a prospective comparison of the breast-cancer index (BCI) assay, 21-gene recurrence score, and IHC4 in the TransATAC study population (vol 14, pg 1067, 2013) [J] . Sgroi D. C., Sestak I, Cuzick J. The lancet oncology . 2018,第4期

机译：预测雌激素受体阳性乳腺癌患者的晚期复发：乳腺癌指数（BCI）测定，21-基因复发评分和IHC4在Transatac研究人群中的前瞻性比较（Vol 14，PG 1067， 2013）
4. Prediction of Recurrence and Non-recurrence Events of Breast Cancer using Bagging Algorithm [C] . Sajib Kabiraj, Laboni Akter, M. Raihan, International Conference on Computing, Communication and Networking Technologies . 2020

机译：使用装袋算法预测乳腺癌的复发和非复发事件
5. Fear of breast cancer recurrence, resilience, and age as predictors of posttraumatic growth in breast cancer survivors [D] . Teixeira da Silva, Maria A. 2015

机译：担心乳腺癌复发，适应力和年龄是乳腺癌幸存者创伤后生长的预测指标
6. Deep Learning-Based Prediction Model for Breast Cancer Recurrence Using Adjuvant Breast Cancer Cohort in Tertiary Cancer Center Registry [O] . Ji-Yeon Kim, Yong Seok Lee, Jonghan Yu, 2021

机译：基于深度学习的乳腺癌复发预测模型在第三族癌症中心登记处使用佐剂乳腺癌群体
7. Explanation and Reliability of Prediction Models: The Case of Breast Cancer Recurrence [O] . Erik ˇ Strumbelj, Zoran Bosnić, Branko Zakotnik, 2009

机译：预测模型的解释与可靠性：乳腺癌复发的案例

Explanation and reliability of prediction models: the case of breast cancer recurrence

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