Analysis of epidemiological association patterns of serum thyrotropin by combining random forests and Bayesian networks - Becker, Ann-Kristin; Ittermann, Till; Dörr, Markus; Felix, Stephan B.; Nauck, Matthias; Teumer, Alexander; Völker, Uwe; Völzke, Henry; Kaderali, Lars; Nath, Neetika (PloS one, 2022)
Approaching epidemiological data with flexible machine learning algorithms is of great value for understanding disease-specific association patterns. However, it can be difficult to correctly extract and understand those patterns due to the lack of model interpretability. We here propose a machine learning workflow that combines random forests with Bayesian network surrogate models to allow for a deeper level of interpretation of complex association patterns. We first evaluate the proposed workflow on synthetic data. We then apply it to data from the large population-based Study of Health in Pomerania (SHIP). Based on this combination, we discover and interpret broad patterns of individual serum TSH concentrations, an important marker of thyroid functionality. We demonstrate that the combination of random forest and Bayesian network analysis is helpful to reveal and interpret broad association patterns of individual TSH concentrations. The discovered patterns are in line with state-of-the-art literature. They may be useful for future thyroid research and improved dosing of therapeutics.