Table 1.

Summary of machine learning approaches that are promising in addressing issues imposed by trait heterogeneity. The Approaches column lists examples of algorithms in the respective method category. Strengths and Limitations are described in terms of EHR-derived data applications. The Biomedical Applications column lists traits for which subgroups have been identified using the respective method.

Method Category	Approaches	Strengths	Limitations	Biomedical Applications
Cluster analysis	Hierarchical, k-means	Wide range of applications; easy interpretation	Not robust to highly dimensional data or large datasets; most approaches restricted to one data type; some approaches require number of clusters	COPD[11,37], Fibromyalgia[39], Tinnitus[40], Diabetes[41], Obesity[42,43]
Topological approaches	TDA, manifold learning algorithms	Able to handle highly dimensional and noisy data; does not require knowledge of number of clusters; sensitive to global and local structure	Optimization of free parameters; computational cost; deep knowledge of topological methods for correct application	T2D[9], Breast cancer [53], Attention deficit [52]
Dimensionality Reduction	Linear (PCA), Non-linear (MDS, t-SNE, Isomap, LLE)	Able to handle highly dimensional, noisy data; does not require knowledge of number of clusters	Optimization of free parameters; Many methods are non-parametric and do not provide information on how dimensionality was reduced; projection loss; result inconsistency; computational cost	COPD[37,46], Changes during anesthesia[54], temporal lobe epilepsy[55]