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Article

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

Journal of the American Medical Informatics Association : JAMIA - 1 Jul 2020

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

Abstract excerpt

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...

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Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Kaplan-Meier Estimate

Share to Leukemia, Lymphocytic, Chronic, B-Cell

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Source

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Identifiers

PMID: 32483590
DOI: 10.1093/jamia/ocaa060
PMCID: PMC7647286

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026. Manifest publication-graph-cohort-2026-09-06.1.

Source receipt: pubmed, retrieved 6 Sept 2026.

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