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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/cdss4
课程评论：没有评论
课程名称：临床决策支持系统 - CDSS 4 课程概述：本课程探讨在临床决策支持系统（CDSS）中使用的机器学习系统，需要进行外部验证、校准分析、偏见和公平性的评估。课程将解释CDSS中采用的机器学习评估的主要概念，并讨论决策曲线分析以及需要可解释的人本CDSS。最后，还将介绍深度学习模型的隐私问题与潜在的对抗性攻击，并提出可解释且保护隐私的新一代CDSS的愿景。 课程大纲： 第一部分：从机器学习模型到临床决策支持系统 描述：在临床决策支持系统中采用机器学习模型需要经过多个步骤，包括外部验证、偏见评估和校准、公平性评估、临床有效性以及可解释性模型和隐私意识型机器学习模型的能力。本模块将讨论这些概念，并提供该领域最前沿研究的多个实例。外部验证和偏见评估已成为临床预测模型的常规要求，但在这些条件下评估和采用深度学习模型仍需进一步工作。 第二部分：机器学习模型中的“公平性” 描述：机器学习在理论上被认为可以不带偏见和社会歧视地做出决策。然而，最近的证据表明，机器学习模型从历史数据中学习偏见，并以类似的方式再现不公平的决策。检测机器学习模型对特定子群体的偏见是一个挑战，因为这些模型本身并未被设计或训练来进行有意的歧视。定义“公平性”指标并研究确保少数群体不受机器学习模型决策不利影响的方案是当前的研究热点。 第三部分：决策曲线分析与人本CDSS 描述：决策曲线分析用于评估预测模型的临床有效性，方法是通过估算净效益即模型的精度和准确性之间的权衡。依据此方法，比较“对所有人进行干预”和“不进行干预”的模型净效益。决策曲线分析是一种以人为本的评估临床有效性的方法，因为它需要专家的意见。伦理人工智能倡议表明，临床决策支持系统中需要人本的方法，以确保问责、安全和监管，同时确保公平性和透明性。 第四部分：CDSS中的隐私问题 描述：深度学习模型具有令人瞩目的记忆能力，即使在不发生过拟合的情况下，模型仍能记住数据。这意味着模型本身可能暴露患者的信息，从而损害隐私，导致推断中的无意数据泄露，并为恶意攻击提供机会。我们将概述常见的隐私攻击及其防御措施，并讨论针对深度学习解释的对抗性攻击。
Part: 1
Title:From machine learning models to clinical decision support systems
Description:Adopting a machine learning model in a Clinical Decision Support System (CDSS) requires several steps that involve external validation, bias assessment and calibration, 'fairness' assessment, clinical usefulness, ability to explain the model's decision and privacy-aware machine learning models. In this module, we are going to discuss these concepts and provide several examples from state-of-the-art research in the area. External validation and bias assessment have become the norm in clinical prediction models. Further work is required to assess and adopt deep learning models under these conditions. On the other hand, research in 'fairness', human-centred CDSS and privacy concerns of machine learning models are areas of active research. The first week is going to cover the ground around the difference between reproducibility and generalisability. Furthermore, calibration assessment in clinical prediction models will be explored while how different deep learning architectures affect calibration will be discussed.
Part: 2
Title:'Fairness' in Machine Learning Models
Description:Naively, machine learning can be thought as a way to come to decisions that are free from prejudice and social biases. However, recent evidence show how machine learning models learn from biases in historic data and reproduce unfair decisions in similar ways. Detecting biases against subgroups in machine learning models is challenging also due to the fact that these models have not been designed or trained to discriminate deliberately. Defining 'fairness' metrics and investigating ways in ensuring that minority groups are not disadvantaged from machine learning models' decisions is an active research area.
Part: 3
Title:Decision Curve Analysis and Human-Centered CDSS
Description:Decision curve analysis is used to assess clinical usefulness of a prediction model by estimating the net benefit with is a trade-off of the precision and accuracy of the model. Based on this approach the strategy of ‘intervention for all’ and ‘intervention for none’ is compared to the model’s net benefit. Decision curve analysis is a human-centred approach of assessing clinical usefulness, since it requires experts’ opinion. Ethical Artificial Intelligence initiative indicate that a human-centred approach in clinical decision support systems is required to enable accountability, safety and oversight while the ensure ‘fairness’ and transparency.
Part: 4
Title:Privacy Concerns in CDSS
Description:Deep learning models have remarkable ability to memorise data even when they do not overfit. In other words, the models themselves can expose information about the patients that compromise their privacy. This can results in unintentional data leakage in inference and also provide opportunities for malicious attacks. We will overview common privacy attacks and defences against them. Finally, we will discuss adversarial attacks against deep learning explanations.
Machine learning systems used in Clinical Decision Support Systems (CDSS) require further external validation, calibration analysis, assessment of bias and fairness. In this course, the main concepts of machine learning evaluation adopted in CDSS will be explained. Furthermore, decision curve analysis along with human-centred CDSS that need to be explainable will be discussed. Finally, privacy concerns of deep learning models and potential adversarial attacks will be presented along with the vision for a new generation of explainable and privacy-preserved CDSS.
