Artificial intelligence for risk assessment and outcome prediction in malignant haematology
Machine learning models allow for dynamic and scalable risk stratification and outcome prediction. Different modalities of data such as electronic health records, patient genetics or laboratory results can be used as input. ML models autonomously select features weighing their prognostic value. Methods of model explainability in feature selection allow
Jan‐Niklas Eckardt +3 more
wiley +1 more source
MLR Corresponds to the Functional Status of Monocytes in Chronic Lymphocytic Leukemia. [PDF]
Grzegorzewska W +7 more
europepmc +1 more source
Chronic Lymphocytic Leukemia (CLL): First-Line Treatment [PDF]
Michael Hallek
openalex +1 more source
The Role of Tumor Microenvironment and Targeted Therapy in Chronic Lymphocytic Leukemia. [PDF]
Saleh K +13 more
europepmc +1 more source
Sustained efficacy and detailed clinical follow-up of first-line ibrutinib treatment in older patients with chronic lymphocytic leukemia: Extended phase 3 results from RESONATE-2 [PDF]
Bartlett, Nancy L, et al.,
core +1 more source
Correction for: Accelerated chronic lymphocytic leukemia - characteristics and retrospective analysis of the Polish Adult Leukemia Study Group. [PDF]
Sośnia O +22 more
europepmc +1 more source
Prognostic significance of in patients with chronic lymphocytic leukemia: A meta-analysis. [PDF]
Huang L +5 more
europepmc +1 more source
IGHV1 usage is associated with lymphadenopathy and aggressive disease in the TCL1 mouse model for chronic lymphocytic leukemia. [PDF]
Drothler S +9 more
europepmc +1 more source
Editorial: Reviews in hematologic malignancies: 2023
Billy Michael Chelliah Jebaraj +2 more
doaj +1 more source
V-H gene usage differs in germline and mutated B-cell chronic lymphocytic leukemia [PDF]
Amlot, P +8 more
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