常规特征与不可知特征之间关系的统计探索
Statistical Exploration of Relationships Between Routine and Agnostic Features Towards Interpretable Risk Characterization
摘要：
如在高通量系统的其他应用领域中典型的，放射学面临着解释日益复杂的预测模型（诸如从放射组学分析导出的预测模型）的挑战。解释可以由来自机器学习模型的学习输出来指导，然而，其可以随着每种技术而变化很大。无论这种产出模式是什么，它都会提出一些基本问题。我们如何解释临床实施的预后模型？我们如何识别放射组学特征集内的潜在信息结构，以创建临床可解释的模型？我们如何重组或利用特征之间的潜在关系来提高可解释性？一些统计技术进行了探讨，以评估（可能是非线性的）放射学特征之间的关系，从不同的角度。
英文摘要：
As is typical in other fields of application of high throughput systems, radiology is faced with the challenge of interpreting increasingly sophisticated predictive models such as those derived from radiomics analyses. Interpretation may be guided by the learning output from machine learning models, which may however vary greatly with each technique. Whatever this output model, it will raise some essential questions. How do we interpret the prognostic model for clinical implementation? How can we identify potential information structures within sets of radiomic features, in order to create clinically interpretable models? And how can we recombine or exploit potential relationships between features towards improved interpretability? A number of statistical techniques are explored to assess (possibly nonlinear) relationships between radiological features from different angles.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
