Learning from incomplete data by combining physiological knowledge and machine learning

结合生理知识和机器学习从不完整数据中学习

• 批准号: 562032-2021
• 负责人: Layton, AnitaAT
• 金额: $ 1.46万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=743653
• 关键词: Learning; incomplete; data; combining; physiological

Data analytics is playing an increasingly critical role in our decision making. However, data-based prediction is often challenged by missing data. Machine learning is well suited for analyzing massive data, but its accuracy can be hampered by the quality of the data. This problem is particularly critical in health or environmental records, which are notoriously incomplete and erroneous. Errors in the data fed to a machine learning model may yield costly or even lethal mistakes in its prediction. Given this knowledge gap, this project seeks to develop a method that maximizes the amount of information that can be gleamed from a physiological dataset with missing data. To accomplish that goal, we will first develop an innovative data imputation method that is based on known physiology. Specifically, the method will be based on HumMod, a state-of-the-art computational model of human physiology. HumMod is presently formulated for a middle-age man only; hence, in Objective 1 we will extend HumMod to take into account sex and age, by developing instantiations of the model for a middle-age woman, an older man, and an older woman. In Objective 2, we will apply the sex- and age-specific HumMod models to clean data provided by our partners, and then apply machine learning analysis to predict individual health status. The accuracy of the prediction will be compared with analogous predictions made by the same machine learning model but applied to datasets cleaned by other data imputation methods. We expect our physiologically-based data imputation method to out-perform alternative methods, many of which can introduce bias or are sensitive to outliers. The impact of this project will be greatly strengthened by the participation of industry partner AstraZeneca Canada, who will provide cash contribution, and by non-profit Diabetes Action Canada, who will provide access to a large database.

数据分析在我们的决策中发挥着越来越重要的作用。然而，基于数据的预测经常受到数据缺失的挑战。机器学习非常适合分析海量数据，但其准确性可能会受到数据质量的影响。这个问题在健康或环境记录中尤其严重，众所周知，这些记录不完整且错误。提供给机器学习模型的数据中的错误可能会在其预测中产生代价高昂甚至致命的错误。鉴于这种知识差距，该项目寻求开发一种方法，最大限度地增加从缺失数据的生理数据集中发现的信息量。为了实现这一目标，我们将首先开发一种基于已知生理学的创新数据插补方法。具体来说，该方法将基于 HumMod，一种最先进的人类生理学计算模型。 HumMod 目前仅针对中年男性配制；因此，在目标 1 中，我们将通过为中年女性、老年男性和老年女性开发模型实例来扩展 HumMod 以考虑性别和年龄。在目标 2 中，我们将应用特定于性别和年龄的 HumMod 模型来清理合作伙伴提供的数据，然后应用机器学习分析来预测个人健康状况。预测的准确性将与相同机器学习模型做出的类似预测进行比较，但应用于通过其他数据插补方法清理的数据集。我们预计基于生理学的数据插补方法将优于替代方法，其中许多方法可能会引入偏差或对异常值敏感。行业合作伙伴阿斯利康加拿大公司（将提供现金捐助）和非营利组织加拿大糖尿病行动组织（将提供大型数据库的访问权限）的参与将大大增强该项目的影响力。