Phenotype clustering in health care: A narrative review for clinicians

Abstract

Human pathophysiology is occasionally too complex for unaided hypothetical-deductive reasoning and the isolated application of additive or linear statistical methods. Clustering algorithms use input data patterns and distributions to form groups of similar patients or diseases that share distinct properties. Although clinicians frequently perform tasks that may be enhanced by clustering, few receive formal training and clinician-centered literature in clustering is sparse. To add value to clinical care and research, optimal clustering practices require a thorough understanding of how to process and optimize data, select features, weigh strengths and weaknesses of different clustering methods, select the optimal clustering method, and apply clustering methods to solve problems. These concepts and our suggestions for implementing them are described in this narrative review of published literature. All clustering methods share the weakness of finding potential clusters even when natural clusters do not exist, underscoring the importance of applying data-driven techniques as well as clinical and statistical expertise to clustering analyses. When applied properly, patient and disease phenotype clustering can reveal obscured associations that can help clinicians understand disease pathophysiology, predict treatment response, and identify patients for clinical trial enrollment.

Keywords: artificial intelligence; cluster; endotype; endotyping; machine learning.

Figure 1

Phenotype clustering in health care applies clustering algorithms to clinical data, biomarkers, or genomic data to form unique groupings that can elucidate pathophysiology, predict treatment response, or augment clinical trial enrollment.

Figure 2

Similarity of elements in clustering algorithms is inversely proportional to distance. This is often derived by applying the Pythagorean theorem to calculate Euclidean distance. We illustrate this approach in two-dimensional space, though similar calculations apply for data points of arbitrary dimensionality.