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

Caitlin E. Coombes, Zachary B. Abrams, Suli Li, Lynne V. Abruzzo, Kevin R. Coombes

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

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 survival analysis using a Cox proportional hazard model, log-rank test, and Kaplan-Meier curves. Results: In both experiments, survival analysis revealed a statistically significant association between clusters and survival outcomes (A: overall survival, P =. 0164; B: time from diagnosis to treatment, P =. 0039). Multidimensional scaling separated clusters along a gradient mirroring the order of overall survival. Longer survival was associated with mutated immunoglobulin heavy-chain variable region gene (IGHV) status, absent Zap 70 expression, female sex, and younger age. Conclusions: This approach to mixed-type data handling and selection of distance metric captured well-understood, binary, prognostic markers in chronic lymphocytic leukemia (sex, IGHV mutation status, ZAP70 expression status) with high fidelity.

Original languageEnglish (US)
Pages (from-to)1019-1027
Number of pages9
JournalJournal of the American Medical Informatics Association
Volume27
Issue number7
DOIs
StatePublished - Jul 1 2020
Externally publishedYes

Keywords

  • chronic lymphocytic leukemia
  • clinical informatics, mixed-type data
  • clustering
  • unsupervised machine learning

ASJC Scopus subject areas

  • Health Informatics

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