AIMC Topic: Multiple Myeloma

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Evaluation of risk factors for thromboembolic events in multiple myeloma patients using multiple machine learning models.

Medicine
Venous thromboembolic events (VTE) is a frequent complication in multiple myeloma (MM) patients, raising mortality. This study aims to use machine learning to identify VTE risk factors in MM, helping to pinpoint high-risk individuals for better clini...

Natural Language Processing Algorithm to Extract Multiple Myeloma Stage From Oncology Notes in the Veterans Affairs Healthcare System.

JCO clinical cancer informatics
PURPOSE: Stage in multiple myeloma (MM) is an essential measure of disease risk, but its measurement in large databases is often lacking. We aimed to develop and validate a natural language processing (NLP) algorithm to extract oncologists' documenta...

Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies.

Cancer research
UNLABELLED: Bone marrow trephine biopsy is crucial for the diagnosis of multiple myeloma. However, the complexity of bone marrow cellular, morphologic, and spatial architecture preserved in trephine samples hinders comprehensive evaluation. To dissec...

Dilemma: Correlation Between Serum Level of Hepcidin and IL-6 in Anemic Myeloma Patients.

Medical archives (Sarajevo, Bosnia and Herzegovina)
INTRODUCTION: Anemia occurs in 60% to 80 % of patients with newly diagnosed myeloma multiplex (MM). The cause of anemia in MM is probably multi factorial and involved among the others hepcidin and some cytokines, especially interleukine-6. Anemia in ...