Myeloma

OpenScientist: evaluating an open agentic AI co-scientist to accelerate biomedical discovery

Background: Advances in medicine depend on analyzing large and complex data sources, but discovery i...

Heterogenous treatment effects of blood transfusion in hospitalized patients with congestive heart failure

Background: Anemia is nearly ubiquitous in hospitalized patients with congestive heart failure (CHF)...

Multi-Model Clinical Validation of an AI-Powered Biomarker Analysis Framework: A Cross-Vendor Benchmark on 4,018 NHANES Patients

Background: Large language models (LLMs) show promise for clinical decision support, yet most valida...

A custom phenotypic profile for Fanconi anemia: Addressing gaps in existing disease annotations

Fanconi anemia (FA) is a rare genetic disorder of impaired DNA repair characterized by progressive b...

Predicting Anemia Among Under-Five Children in Nepal Using Machine Learning and Deep Learning

Childhood anemia remains a major public health challenge in Nepal and is associated with impaired gr...

Machine Learning-Based Non-Invasive Diagnosis of Anemia in Children Using Palm Image Analysis

Abstract: Anemia, particularly iron-deficiency anemia, is a critical global health concern, with a h...

Reprogramming BCMA-Targeted CAR-T Cells through γ-Secretase Modulation Blocks Antigen Shedding and Extends CAR-T Longevity

B-cell maturation antigen (BCMA) shedding by {gamma}-secretase generates soluble BCMA (sBCMA) , whic...

A negative combined effect of exposure to maternal Mn-Cu-Rb-Fe metal mixtures on gestational anemia, and the mediating role of creatinine in the Guangxi Birth Cohort Study (GBCS): Twelve machine learning algorithms.

The link between individual metals and gestational anemia has been established, but the impact of me...

Correction to: Artificial intelligence-based, volumetric assessment of the bone marrow metabolic activity in [F]FDG PET/CT predicts survival in multiple myeloma.

Automated vertebrae identification and segmentation with structural uncertainty analysis in longitudinal CT scans of patients with multiple myeloma.

OBJECTIVES: Optimize deep learning-based vertebrae segmentation in longitudinal CT scans of multiple...

Associations of dietary patterns with serum 25(OH) vitamin D and serum anemia related biomarkers among expectant mothers: A machine learning based approach.

BACKGROUND: Machine learning algorithms (MLA) gained prominence in nutritional epidemiology for anal...

Characterization Of Diseases In Temporal Comorbidity Networks

Comorbidity networks, which capture disease-disease co-occurrence usually based on electronic heal...

Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence

Risk stratification is a key tool in clinical decision-making, yet current approaches often fail t...

Leveraging artificial intelligence chatbots for anemia prevention: A comparative study of ChatGPT-3.5, copilot, and Gemini outputs against Google Search results.

AIM: This study evaluated the understandability, actionability, and readability of text on anemia ge...

Development of Interactive Nomograms for Predicting Short-Term Survival in ICU Patients with Aplastic Anemia

Aplastic anemia is a rare, life-threatening hematologic disorder characterized by pancytopenia and...

Machine learning approach for differentiating iron deficiency anemia and thalassemia using random forest and gradient boosting algorithms.

Formulas based on red blood cell indices have been used to differentiate between iron deficiency ane...

Targeting high-risk multiple myeloma genotypes with optimized anti-CD70 CAR-T cells.

Despite the success of BCMA-targeting CAR-Ts in multiple myeloma, patients with high-risk cytogeneti...

A weakly-supervised deep learning model for fast localisation and delineation of the skeleton, internal organs, and spinal canal on Whole-Body Diffusion-Weighted MRI (WB-DWI)

Background: Apparent Diffusion Coefficient (ADC) values and Total Diffusion Volume (TDV) from Whol...

Evaluating Skellytour for Automated Skeleton Segmentation from Whole-Body CT Images.

Purpose To construct and evaluate the performance of a machine learning model for bone segmentation ...

[Study on lightweight plasma recognition algorithm based on depth image perception].

In the clinical stage, suspected hemolytic plasma may cause hemolysis illness, manifesting as sympto...

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

Venous thromboembolic events (VTE) is a frequent complication in multiple myeloma (MM) patients, rai...