ArticleNature communications2020
Machine learning can identify newly diagnosed patients with CLL at high risk of infection.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
52 citing papers in PubMed.
- Machine Learning for Prediction of High-Risk Infections in Patients With Cancer.Cancer medicine · 2026Article
- Machine learning enhances risk stratification and treatment failure prediction in diffuse large B-cell lymphoma.HemaSphere · 2026Article
- Review
- Post-treatment infection prediction in CLL using domain adaptation of lymphoma electronic health records.Acta oncologica (Stockholm, Sweden) · 2026Article
- The Danish Lymphoid Cancer Research (DALY-CARE): Genetic Cohort Profile.Clinical epidemiology · 2026Article
- Artificial intelligence for risk assessment and outcome prediction in malignant haematology.British journal of haematology · 2026Review
- Regional Electronic Health Records in Denmark: Research Potential for Clinical Epidemiology.Clinical epidemiology · 2026Review
- Chronic lymphocytic leukemia and associated chronic lung diseases.Haematologica · 2025Article
- Machine Learning for Prediction of High-Risk Hospitalizations in Lymphoma Patients: A Danish Population-Based Study.European journal of haematology · 2025Article
- State of the art biology, progression, and clinical management of monoclonal B-cell lymphocytosis (MBL): consensus report from the Intercepting Blood Cancers Workshop Committee.Blood cancer journal · 2025Article
- Predicting p53 Status in IDH-Mutant Gliomas Using MRI-Based Radiomic Model.Cancer medicine · 2025Article
- Refining Criteria for Choosing the First-Line Treatment for Real-World Patients with AdvancedInternational journal of molecular sciences · 2025Review
- Machine Learning Model Predicts Abnormal Lymphocytosis Associated With Chronic Lymphocytic Leukemia.JCO clinical cancer informatics · 2025Observational
- Machine learning-based prediction of postoperative mortality risk after abdominal surgery.World journal of gastrointestinal surgery · 2025Article
- Elucidating novel immune profiles for predicting infection in high-risk cohorts: a pilot study in patients with relapsed and refractory chronic lymphocytic leukaemia.Clinical & translational immunology · 2025Article
- Recent advances in and applications of ex vivo drug sensitivity analysis for blood cancers.Blood research · 2024Review
- Explainable Machine Learning Models for Brain Diseases: Insights from a Systematic Review.Neurology international · 2024Review
- Exploring the prognostic value of T follicular helper cell levels in chronic lymphocytic leukemia.Scientific reports · 2024Article
- Article
- It is feasible and safe to stop specialized follow-up of asymptomatic lower-risk chronic lymphocytic leukemia.Blood advances · 2024Article
Corrections and comments
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Authors and funding
20 authors.
Funding
Abstract
Infections have become the major cause of morbidity and mortality among patients with chronic lymphocytic leukemia (CLL) due to immune dysfunction and cytotoxic CLL treatment. Yet, predictive models for infection are missing. In this work, we develop the CLL Treatment-Infection Model (CLL-TIM) that identifies patients at risk of infection or CLL treatment within 2 years of diagnosis as validated on both internal and external cohorts. CLL-TIM is an ensemble algorithm composed of 28 machine learning algorithms based on data from 4,149 patients with CLL. The model is capable of dealing with heterogeneous data, including the high rates of missing data to be expected in the real-world setting, with a precision of 72% and a recall of 75%. To address concerns regarding the use of complex machine learning algorithms in the clinic, for each patient with CLL, CLL-TIM provides explainable predictions through uncertainty estimates and personalized risk factors.
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Registered trials
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