ArticleJournal of the American Medical Informatics Association : JAMIA2020
Unsupervised machine learning and prognostic factors of survival in chronic lymphocytic leukemia.
Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 32 citations in OpenAlex.
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- Prognostic determinants in cancer survival: a multidimensional evaluation of clinical and genetic factors across 10 cancer types in the participants of Genomics England's 100,000 Genomes Project.Discover oncology · 2024Article
- Topological Structures in the Space of Treatment-Naïve Patients with Chronic Lymphocytic Leukemia.Cancers · 2024Article
- Prediction of leukemia peptides using convolutional neural network and protein compositions.BMC cancer · 2024Article
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- SillyPutty: Improved clustering by optimizing the silhouette width.bioRxiv : the preprint server for biology · 2023Article
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- Comparing machine learning algorithms to predict 5-year survival in patients with chronic myeloid leukemia.BMC medical informatics and decision making · 2022Article
- Clinical Informatics Approaches to Understand and Address Cancer Disparities.Yearbook of medical informatics · 2022Review
- A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects.Journal of medical Internet research · 2022Review
- A deep learning method and device for bone marrow imaging cell detection.Annals of translational medicine · 2022Article
- The importance of genomic predictors for clinical outcome of hematological malignancies.Blood science (Baltimore, Md.) · 2021Article
- Simulation-derived best practices for clustering clinical data.Journal of biomedical informatics · 2021Article
- A novel model to label delirium in an intensive care unit from clinician actions.BMC medical informatics and decision making · 2021Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveUnsupervised 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.
methodsTo 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.
resultsIn 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.
conclusionsThis 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.