Combining Data-driven and Knowledge-guided Methods to Induce Interpretable Physiological Models

Abstract

In this paper, we review the paradigm of inductive process modeling and examine its application to human physiology. This framework represents models as a set of interacting processes, each with associated differential or algebraic equations that express causal relations among variables. Simulating such a quantitative process model produces trajectories for variables over time that one can compare to observations. Background knowledge about candidate processes enables search through the space of model structures and their associated parameters, and thus identify quantitative models that explain time-series data. We present an initial process model for aspects of human physiology, consider its uses for health monitoring, and discuss the induction of such models. In closing, we consider related efforts on physiological modeling and our plans for collecting data to evaluate our framework in this domain.