Observational studyJCO clinical cancer informatics2026
Leveraging Digital Technology and Artificial Intelligence to Describe the Real-World Belgian Chronic Lymphocytic Leukemia Patient Population: The BE-CLLEAR Study.
Observational study in JCO clinical cancer informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Real‑World Clinical Characterization of Major Depressive Disorder and Treatment‑Resistant Depression Supported by Natural Language Processing: Multicenter Observational Study From the MOOD Project.Interactive journal of medical research · 2026Article
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Authors and funding
10 authors.
Funding
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Abstract
purposeChronic lymphocytic leukemia (CLL) treatment paradigms have evolved significantly, yet real-world evidence (RWE) on guideline implementation and patient characteristics remains limited. MATERIALS AND
methodsThis multicenter retrospective study leveraged artificial intelligence (AI) to analyze structured and unstructured data from four Belgian hospitals (January 1, 2018-October 31, 2021). Structured data including diagnosis codes, laboratory results, treatment records, and national registries were standardized using the Observational Medical Outcomes Partnership (OMOP) Common Data Model. Unstructured clinical notes and reports were processed using a transformer-based natural language processing (NLP) pipeline. We examined clinical characteristics, diagnostic testing, and treatment patterns among patients with newly diagnosed CLL.
resultsOf 22 variable groups analyzed, 50.0% was derived from structured data only, 36.4% from unstructured data only (NLP-extracted), and 13.6% from mixed sources. Five hundred eighty-six patients with CLL were identified, with a median age of 74 years. One hundred seventy-four patients (29.7%) initiated first-line (1L) treatment, and 41 progressed to second-line treatment. Of 1L treated patients, 68.4% had at least one prespecified comorbidity, including 12.1% with significant cardiovascular disease.
conclusionThis AI-augmented study demonstrates the feasibility and scalability of combining NLP-derived insights with OMOP-standardized structured data to generate reproducible RWE in hematology. Our results highlight an elderly CLL population with significant comorbidities and a shift toward targeted therapies. While treatment patterns aligned with guidelines, data quality depended on source documentation accessibility. Improved integration of molecular testing into electronic health records is essential for enhancing clinical decision making, patient outcomes, and future research.
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