Recent breakthroughs in artificial intelligence (AI) and machine learning (ML) have ushered
in a new era of possibilities across various scientific domains. One area where these …

The use of data from randomized clinical trials to justify treatment decisions for real‐world
patients is the current state of the art. It relies on the assumption that average treatment …

Integrating physics models within machine learning models holds considerable promise
toward learning robust models with improved interpretability and abilities to extrapolate. In …

The development of data-informed predictive models for dynamical systems is of
widespread interest in many disciplines. We present a unifying framework for blending …

Mechanistic learning refers to the synergistic combination of mechanistic mathematical
modeling and data-driven machine or deep learning. This emerging field finds increasing …

Background Clinical medicine offers a promising arena for applying Machine Learning (ML)
models. However, despite numerous studies employing ML in medical data analysis, only a …

Pharmacometrics is a multidisciplinary field utilizing mathematical models of physiology,
pharmacology, and disease to describe and quantify the interactions between medication …

Combining domain knowledge (DK) and machine learning is a recent research stream to
overcome multiple issues like limited explainability, lack of data, and insufficient robustness …

Despite increasing interest in using Artificial Intelligence (AI) and Machine Learning (ML)
models for drug development, effectively interpreting their predictions remains a challenge …

Neural ordinary differential equations (neural ODEs) are a popular family of continuous-
depth deep learning models. In this work, we consider a large family of parameterized ODEs …