Machine Learning Strategies for Augmented Health Informatics

增强健康信息学的机器学习策略

• 批准号: RGPIN-2020-06841
• 负责人: Bui, Francis
• 金额: $ 2.04万
• 依托单位: University of Saskatchewan
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=718207
• 关键词: Machine; Learning; Strategies; Augmented; Health

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.

本研究计划重点关注机器学习（ML）中与健康信息学应用密切相关的三个突出挑战：(1)机器学习有效运行的假设与现实应用中实际遇到的情况之间的不匹配；(2)缺乏针对特定情况选择合适ML架构的系统指南；(3)认为机器学习是一种黑箱方法，不利于人类理解的根深蒂固的看法。为了应对这些挑战，提出了三种相应的工程策略。