Learning from incomplete data by combining physiological knowledge and machine learning

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缺失数据的挑战。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。这一问题在卫生或环境记录方面尤为严重，因为这些记录是出了名的不完整和错误。输入机器学习模型的数据中的错误可能会在预测中产生代价高昂甚至致命的错误。鉴于这种知识差距，该项目旨在开发一种方法，最大限度地提高可以从缺失数据的生理数据集中获得的信息量。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。具体而言，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目前仅针对中年男性制定;因此，在目标1中，我们将扩展HumMod以考虑性别和年龄，通过开发中年女性，老年男性和老年女性的模型实例。在目标2中，我们将应用特定性别和年龄的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。预测的准确性将与由相同机器学习模型进行的类似预测进行比较，但应用于由其他数据插补方法清理的数据集。我们希望我们基于生理学的数据插补方法优于其他方法，其中许多方法可能会引入偏倚或对离群值敏感。该项目的影响将大大加强的参与，行业合作伙伴阿斯利康加拿大，谁将提供现金捐款，并通过非营利糖尿病行动加拿大，谁将提供访问一个大型数据库。

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.