Learning from incomplete data by combining physiological knowledge and machine learning

数据分析在我们的决策中扮演着越来越重要的角色。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大量数据，但其准确性可能会受到数据质量的影响。这一问题在卫生或环境记录中尤为严重，这些记录是出了名的不完整和错误。提供给机器学习模型的数据中的错误可能会在预测中导致代价高昂甚至致命的错误。鉴于这一知识差距，该项目寻求开发一种方法，最大限度地从缺失数据的生理数据集中获取信息量。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输入方法。具体来说，该方法将基于HumMod，这是一种最先进的人体生理学计算模型。HumMod目前只适用于中年男性；因此，在目标1中，我们将通过开发中年女性、老年男性和老年女性的模型实例，扩展HumMod以考虑性别和年龄。在Objective 2中，我们将应用基于性别和年龄的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体的健康状况。预测的准确性将与同一机器学习模型所做的类似预测进行比较，但将其应用于通过其他数据输入方法清洗的数据集。我们希望我们基于生理的数据输入方法优于其他方法，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（AstraZeneca Canada）将提供现金捐助，非营利性的加拿大糖尿病行动组织（Diabetes Action Canada）将提供访问大型数据库的途径，这将大大加强该项目的影响。

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.