Machine learning for health modeling and simulations

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The overarching goal of my research program is to improve and automatize the methodology of mathematical modeling in medicine and public health using artificial intelligence. I will elaborate new methods, software tools and algorithms to automatize the generation and calibration of interpretable mathematical models for decision support systems in respiratory health. The scaling of the capacity of these models, and their simulations, will enable the assimilation of data streams in real time to support stakeholders in the management of respiratory diseases and the prevention of epidemics. The three themes of my program will focus on the different aspects of automatic model generation and calibration: computer-aided composition of interpretable models (Theme 1), machine learning of model structure (Theme 2), and real-time update of simulations based on data streams (Theme 3). In Theme 1, I will use methods stemming from mathematical modeling and computer science to create flexible model templates and design a software environment to create, edit and manipulate models represented in these templates. In Theme 2, I will design algorithms to train these models to solve prediction problems represented by examples of time-dependent input-output data, and I will compare their performance to that of state-of-the-art machine learning and deep learning approaches. In Theme 3, I will design software tools for the validation and cleaning of data streams, and I will adapt active learning approaches to the process of updating simulations in real time concurrently to the data assimilation process of the underlying model. The methodological innovations of my emerging research program will result in software tools and algorithms that are urgently needed for the analysis of big health data with models that are reliable and interpretable for knowledge users as well as easy to maintain and reuse for modelers. The combination of mathematical models and machine learning in a data-driven modeling approach is a promising avenue for health research (in healthcare and public health) that has broad applicability. I have the perfect skillset to carry forward this program that draws upon the fields of mathematics, computer science and engineering, and health sciences. My research program will be supported by data obtained in interdisciplinary collaborative projects where I lead machine learning components. I will only use data that will be certified from ethics review boards, or that will be publicly available. I am already using data from the research data warehouse at Sainte-Justine's hospital pediatric intensive care unit, which has ethics certification for the development of cardiorespiratory models. I am also developing models of COVID-19 vaccination campaigns based on public health data in Quebec.