Robust machine learning for healthcare

用于医疗保健的强大机器学习

• 批准号: RGPIN-2020-05777
• 负责人: Goldenberg, Anna
• 金额: $ 2.99万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=740819
• 关键词: Robust; machine; learning; healthcare

The great promise of AI in healthcare is taking time to materialize. Besides difficulties with access to the data and unrealistic expectations of the AI due to the hype fueled by the media, there are many fundamental machine learning advances that need to be made to achieve the widespread use of AI in healthcare. Some of the current shortcomings include but are not limited to 1) high error rates in predicting rare critical events (such as cardiac arrest); ii) lack of model robustness to the underlying changes in the data arising due to changes in the policy and practice; iii) lack of the model explainability that makes it hard for the users, e.g. clinicians, to act upon predictions that diverge from their intuition. In my lab, we have started addressing some of these fundamental issues. In this grant I am proposing a research program to make substantial improvements in addressing these questions over the next 5 years. Classification vs outlier detection The frequency of many critical events, such as cardiac arrest, is usually below 5%. These problems are often incorrectly treated as classification. One of the goals of our work will be to expand the class of generative models, creating models fit for the purpose of outlier detection in healthcare. Dealing with shifts in the data. Patient populations, care practices, and database systems evolve over time, and yet few works to date have explicitly addressed data shift and evolution as part of the medical record modeling. We showed that there is a simple yet effective mitigation strategy: aggregation of raw features into expert defined clinical concepts. We will develop new robust feature representation techniques and adaptive learning over time to make increase readiness of machine learning (ML) for healthcare deployment in the future. Explainability. Once developed, translating ML models effectively to practice requires establishing users' trust. We surveyed clinicians to understand their perception of interpretable models. The next step is to integrate these perceptions of explainability into the ML model development. In the program proposed in this grant I will explore counterfactual models to achieve this goal. Training. My work draws on and affects many disciplines, from computer science to medicine to ethics. Over the last eight years, I have been training HQP coming from a variety of backgrounds, from computer science to biology, from engineering to economics as well as medical professionals in using and developing new AI models for healthcare. It is my goal to supervise and help grow this new and burgeoning field to result in a very highly skilled workforce. Impact. The amount of available health data has made it clear that machine learning has a great role to play in the future of medicine. The work proposed in this grant will help to build the foundation for responsible AI+healthcare to ensure the realization of the AI potential in this important field.

人工智能在医疗保健领域的巨大前景需要时间才能实现。除了由于媒体炒作而导致数据获取困难和对人工智能不切实际的期望之外，还需要在机器学习方面取得许多基础性进展，以实现人工智能在医疗保健领域的广泛使用。目前的一些缺点包括但不限于：1）预测罕见危急事件（例如心脏骤停）的错误率较高； ii) 模型对因政策和实践变化而引起的数据潜在变化缺乏稳健性； iii）模型缺乏可解释性，这给用户带来了困难，例如临床医生根据偏离直觉的预测采取行动。在我的实验室中，我们已经开始解决其中一些基本问题。在这笔赠款中，我提议开展一项研究计划，以在未来 5 年内对解决这些问题做出重大改进。分类与异常值检测 许多关键事件（例如心脏骤停）的频率通常低于 5%。这些问题常常被错误地视为分类。我们工作的目标之一是扩展生成模型的类别，创建适合医疗保健异常值检测目的的模型。 处理数据的变化。患者群体、护理实践和数据库系统随着时间的推移而发展，但迄今为止，很少有工作明确地将数据转移和演变作为医疗记录建模的一部分。我们证明了有一种简单而有效的缓解策略：将原始特征聚合为专家定义的临床概念。随着时间的推移，我们将开发新的强大的特征表示技术和自适应学习，以提高机器学习 (ML) 为未来医疗保健部署做好的准备。可解释性。一旦开发完成，将机器学习模型有效地转化为实践需要建立用户的信任。我们调查了临床医生，以了解他们对可解释模型的看法。下一步是将这些可解释性的看法整合到机器学习模型开发中。在这笔赠款提出的计划中，我将探索反事实模型来实现这一目标。训练。我的工作借鉴并影响了许多学科，从计算机科学到医学再到伦理学。在过去的八年里，我一直在培训来自不同背景的总部人员，从计算机科学到生物学，从工程学到经济学，以及医疗专业人员使用和开发新的医疗保健人工智能模型。我的目标是监督和帮助发展这个新兴的新兴领域，以培养出一支高技能的劳动力队伍。影响。大量可用的健康数据清楚地表明，机器学习在未来的医学中可以发挥巨大的作用。这笔赠款中提出的工作将有助于为负责任的人工智能+医疗保健奠定基础，以确保实现这一重要领域的人工智能潜力。