Unsupervised machine learning and prognostic factors of survival in chronic lymphocytic leukemia. (1st June 2020)

• Record Type: Journal Article
• Authors: Coombes, Caitlin E; Abrams, Zachary B; Li, Suli; Abruzzo, Lynne V; Coombes, Kevin R
• Is Part Of: Journal of the American Medical Informatics Association. Volume 27:Number 7(2020)
• Page Start: 1019
• Page End: 1027
• Publication Date: 2020-06-01
• Subjects: unsupervised machine learning -- clustering -- chronic lymphocytic leukemia -- clinical informatics, mixed-type data; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocaa060
• Languages: English
• ISSNs: 1067-5027

Abstract

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.