Machine Learning Strategies for Augmented Health Informatics

该研究计划侧重于机器学习（ML）中的三个突出挑战，这些挑战与健康信息学应用密切相关：（1）ML有效运行的假设与现实世界应用中实际遇到的情况之间的不匹配;（2）缺乏为特定情况选择适当ML架构的系统指南;（3）缺乏对机器学习的系统指南。（3）一种根深蒂固的看法，即ML是一种黑箱方法，不利于人类理解。为了应对这些挑战，提出了三个相应的工程策略。 对于第一个挑战，将研究数据平衡和生成建模策略。特别是，ML方法通常取决于成功部署的可用训练数据的数量和质量。这些假设在实际的健康应用中可能无法满足，从而严重影响了ML的适用性。然而，通过数据增强和生成对抗网络等技术，开发ML初始化和训练解决方案以适应实际应用中的有限数据集是可行的。 对于第二个挑战，将研究基于资源可用性和遇到的环境进行系统ML架构选择和适应的策略。响应于操作条件的变化，自适应ML策略应该允许系统参数的有效调整和优化。因此，条件监控和反馈将用于机会主义地调整ML系统，以在各种条件下实现稳健的性能。 对于第三个挑战，将探索统称为可解释人工智能（XAI）的策略，以将人类设计因素纳入ML系统。这些策略旨在开发ML方法，不仅提供最终的输出决策，还提供相关的解释，以便人类能够理解和解释结果。在健康信息学应用中，机器学习系统通常必须与人类进行交互，以使选定的治疗方案合理化。为此，XAI代表了促进ML系统与人类用户之间成功交互的一个有希望的方向。 总之，这些新策略应该允许人类有效地利用ML进行决策，同时保持认知和控制所做的决策，这些特征与新兴的增强智能（AmI）范式一致。特别是在健康信息学，一个高度影响加拿大生活质量的应用领域，可以预期医疗质量的进步：基于ML的疾病诊断和治疗不仅可以及时准确，而且与人类相关并可解释。因此，这些研究策略应该在提升加拿大作为健康和ML技术领导者的声誉方面发挥重要作用。

This research program focuses on three outstanding challenges in machine learning (ML), which are notably germane to health informatics applications: (1) a mismatch between assumptions for ML to operate effectively, and situations actually encountered in real-world applications; (2) a lack of systematic guidelines for selecting the appropriate ML architecture for a particular situation; (3) an entrenched perception that ML is a black-box approach, not conducive to human understanding. In order to tackle these challenges, three corresponding engineering strategies are proposed. For the first challenge, data balancing and generative modelling strategies will be investigated. In particular, ML methods are typically contingent on both the quantity and quality of available training data for successful deployment. These assumptions may not be met in practical health applications, thus severely compromising the applicability of ML. However, with techniques such as data augmentation and generative adversarial networks, it is feasible to develop ML initialization and training solutions for accommodating limited datasets in practical applications. For the second challenge, strategies allowing for systematic ML architecture selection and adaptation, based on resource availability and encountered environment, will be studied. In response to changes in operating conditions, the adaptive ML strategies should allow for effective tuning and optimization of system parameters. Accordingly, condition monitoring and feedback will be used to opportunistically adapt the ML systems for robust performance in various conditions. For the third challenge, strategies known collectively as explainable artificial intelligence (XAI) will be explored to incorporate human design factors into ML systems. These strategies seek to develop ML methods that provide not only the final output decisions but also the associated explanations, so that humans can understand and interpret the results. In health informatics applications, ML systems typically have to interact with humans in their collaborative efforts to rationalize a selected therapeutic solution. To this end, XAI represents a promising direction to facilitate the successful interaction between ML systems and human users. Together, these novel strategies should allow humans to efficiently utilize ML for decision making, while remaining cognizant and in control of the decisions made, characteristics which are consistent with the emerging paradigm of augmented intelligence (AmI). Specifically within health informatics, an application domain that highly impacts Canadian quality of life, advances in the quality of care can be expected: diagnosis and treatment of diseases based on ML can be not only timely and accurate, but also relevant and explainable to humans. Therefore, these research strategies should play a significant role in advancing Canada's reputation as a leader in both health and ML technologies.