Mechanistic Machine Learning

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PROJECT SUMMARY / ABSTRACT The goal of this project is to combine empirical data with mechanistic physiologic knowledge to produce personalized, quantitative predictions that can lead to improved treatments. In normal practice, physicians reason by analogy from generic physiologic principles, but the technology exists to exploit even imperfect physiologic models make treatment personalized and quantitatively grounded in physiology, and to improve learning from empirical data. We will apply data assimilation (DA), mechanistic mathematical modeling, machine learning, and control theory, which have revolutionized space travel, weather forecasting, transportation and flight, and manufacturing. Data assimilation and control theory have seen very limited use in medicine, usually applied in data-rich circumstances like continuous glucose monitoring or packemakers. Our previous work demonstrated use of data assimilation with glucose-insulin models to predict glucose in the outpatient type 2 diabetes setting. We will extend data assimilation and control theory using, for example, a constrained ensemble Kalman filter and an offline Markov Chain Monte Carlo algorithm, to better handle sparse, short training sets on rapidly changing patients, and we will apply it in the setting of glucose management in the intensive care unit (ICU). Moreover, we will develop DA for phenotyping applications by exploiting the parameter estimation capabilities of DA. Data assimilation can be used to estimate measureable and unmeasureable physiologic states and parameters, and we will use these estimates to create higher definition phenotypes. While we are focusing on glucose management in the ICU, we will develop methods that are likely to generalize, beginning the effort to develop DA in the context of healthcare more broadly. The work we propose is a necessary step toward being able to use mechanism-driven DA to test, validate and optimize personalized short-term treatment strategies, long-term health forecasts, and mechanistic physiologic understanding. We will carry out the following aims: AIM 1—forecast—extend the DA methodology to allow forecasting, personalization, model evaluation, and model selection in the ICU context, relating treatment input to physiologic outcome; AIM 2—phenotype—extend the DA framework to state and parameter estimation to allow for mechanism-based phenotyping, careful uncertainty quantification, and inference of difficult or impossible-to-measure physiology; AIM 3—control—extend the DA to include a controller that begins with desired clinical outcomes, e.g., glucose range, and estimates the inputs, e.g., insulin or nutrition, required to achieve the outcomes.