Investigating AI Approaches for Survival Prediction in Chronic Lymphocytic Leukemia...International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH) (Virtual), December 13-15, 2024.
Chronic lymphocytic leukemia (CLL) exhibits a heterogeneous clinical course. Prognostic markers that impact patient outcomes have been identified, including MYC gene abnormalities. This study investigates machine learning (ML) models for predicting survival in CLL, comparing the performance of Rando...
| Publicado en: | Studies in Health Technology & Informatics Vol. 323; pp. 96 - 101 |
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| Autores principales: | , , , |
| Formato: | proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184463104&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184463104 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2025 vid: 323 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 184463104 184463104 184463104 10.3233/SHTI250056 184463104 ppf: 96 ppct: 5 formats: tig: atl: Investigating AI Approaches for Survival Prediction in Chronic Lymphocytic Leukemia...International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH) (Virtual), December 13-15, 2024. aug: au: Mouazer, Abdelmalek Degroodt, Edgar Nguyen-Khac, Florence Chapiro, Elise affil: Sorbonne Université, Université Sorbonne Paris Nord, INSERM, Limics, Paris, France sug: subj: Leukemia, Lymphocytic, Chronic Prognosis Prediction Models Evaluation Artificial Intelligence Evaluation Machine Learning Evaluation Diagnosis, Computer Assisted Methods Congresses and Conferences Human Male Female Survival Analysis Leukemia, Lymphocytic, Chronic Familial and Genetic Genes Random Forest Decision Trees Cox Proportional Hazards Model External Validity Algorithms Male Female ab: Chronic lymphocytic leukemia (CLL) exhibits a heterogeneous clinical course. Prognostic markers that impact patient outcomes have been identified, including MYC gene abnormalities. This study investigates machine learning (ML) models for predicting survival in CLL, comparing the performance of Random Survival Forest (RSF), Decision Tree (DT), and Cox proportional hazards models across two cohorts: MYC-positive patients and a general CLL population. Three time-to-event outcomes were assessed: 10-year from diagnosis, 10-year from cytogenetic assessment, and time to first treatment. Model performance was evaluated using the C-index and AUC, revealing that RSF and DT models outperformed Cox models in predictive accuracy. Permutation importance highlighted key predictive variables; however, RSF and DT models pose interpretability challenges compared to Cox models, which offer clear hazard ratios. Additionally, an interactive application is available via Streamlit, and the source code is open access on GitHub. Despite limitations in dataset size and external validity, ML models show promise for personalized survival predictions in CLL, especially for MYC-positive cases, underscoring the potential for further model refinement to enhance clinical usability. pubtype: Academic Journal doctype: proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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