Unsupervised machine learning and prognostic factors of survival in chronic lymphocytic leukemia.

ABSTRACT:

Objective

Unsupervised machine learning approaches hold promise for large-scale clinical data. However, the heterogeneity of clinical data raises new methodological challenges in feature selection, choosing a distance metric that captures biological meaning, and visualization. We hypothesized that clustering could discover prognostic groups from patients with chronic lymphocytic leukemia, a disease that provides biological validation through well-understood outcomes.

Methods

To address this challenge, we applied k-medoids clustering with 10 distance metrics to 2 experiments ("A" and "B") with mixed clinical features collapsed to binary vectors and visualized with both multidimensional scaling and t-stochastic neighbor embedding. To assess prognostic utility, we performed sur

SUBMITTER: Coombes CE 

PROVIDER: S-EPMC7647286 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature