Machine Learning Strategies for Augmented Health Informatics

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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.