Hematology

Latest AI and machine learning research in hematology for healthcare professionals.

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Prediction of Mutations and Outcome in Gastrointestinal Stromal Tumors with Deep Learning: A Multicenter, Multinational Study

Background: Gastrointestinal stromal tumor (GIST) is the most common gastrointestinal mesenchymal tumor, driven by tyrosine-protein kinase KIT and platelet-derived growth factor receptor A (PDGFRA) mutations. Specific variants, such as KIT exon 11 deletions, carry prognostic and therapeutic implications, whereas wild-type (WT) variants derive limited benefit from tyrosine kinase inhibitors (TKIs)....

Development and internal validation of risk scores to predict survival in the pediatric population following in-hospital cardiac arrest.

Introduction In-hospital cardiac arrest (IHCA) in the pediatric population is associated with poor survival and neurological outcomes. We aimed to develop and internally validate a risk score to predict survival to discharge following pediatric IHCA. Methods We included pediatric IHCA patients in the Get With The Guidelines-Resuscitation(R) registry between 2005 and 2021. We used logistic regressi...

Improving mortality prediction in critically ill cancer patients with a multidimensional machine learning model

Background: Prognostic assessment in critically ill patients with cancer remains challenging, as conventional ICU severity scores often perform subopt...

Physics-Informed Neural Network for Mapping Vascular and Tissue Dynamics Using Laser Speckle Contrast Imaging

Significance: Quantitatively mapping both cerebral blood flow and tissue dynamics from laser speckle contrast imaging (LSCI) is powerful for studying ...

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 growth, cognition, and increased morbidity. Using Wo...

Feb 1 2026 2602.01005v1
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 high prevalence among children under six years of a...

Developing and externally validating machine learning models to forecast short-term risk of ventilator-associated pneumonia

Purpose: Ventilator-associated pneumonia (VAP) remains one of the most serious hospital-acquired infections in the intensive care unit (ICU), with hig...

clinTALL: machine learning-driven multimodal subtypeclassification and treatment outcome prediction in pediatric T-ALL

Background: Childhood T-lineage acute lymphoblastic leukemia (T-ALL) is an aggressive hematologic malignancy with poor prognosis. Differently from B-c...

Multi-omic deep learning identifies exercise-responsive ageing pathways in humans

Genome-wide association studies of physical activity traits have mapped numerous loci, yet the molecular mechanisms through which exercise influences ...

Decoupling Accuracy and Explainability: Machine Learning Strategies for HbA1c Prediction and Biomarker Discovery in Blood FTIR Spectroscopy

Glycated hemoglobin (HbA1c) is a central biomarker for long-term glycemic control and diabetes management, traditionally quantified using laboratory-i...

Predicting Gene Disease Associations in Type 2 Diabetes Using Machine Learning on Single-Cell RNA-Seq Data

Diabetes is a chronic metabolic disorder characterized by elevated blood glucose levels due to impaired insulin production or function. Two main forms...

Nonlinear multi-study factor analysis

High-dimensional data often exhibit variation that can be captured by lower dimensional factors. For high-dimensional data from multiple studies or en...

Jan 26 2026 2601.18128v1
Analyzing Images of Blood Cells with Quantum Machine Learning Methods: Equilibrium Propagation and Variational Quantum Circuits to Detect Acute Myeloid Leukemia

This paper presents a feasibility study demonstrating that quantum machine learning (QML) algorithms achieve competitive performance on real-world med...

Jan 26 2026 2601.18710v1
An AI-enabled tool for quantifying overlapping red blood cell sickling dynamics in microfluidic assays

Understanding sickle cell dynamics requires accurate identification of morphological transitions under diverse biophysical conditions, particularly in...

Jan 25 2026 2601.17703v1
AlphaGenome -enabled analysis of non-coding regulatory variants underlying RHD Expression

Systematic identification of functional non-coding regulatory variants remains a major challenge in human genetics. Conventional approaches such as la...

Machine learning identifies shared blood transcriptional biomarkers and immune correlates across antiphospholipid syndrome and systemic sclerosis

Antiphospholipid syndrome (APS) and systemic sclerosis (SSc) are immune-mediated multisystem autoimmune diseases with distinct clinical phenotypes but...

Peripheral blood profiles reflecting progenitor lineage balance predict treatment response in chronic myeloid leukemia

Early achievement of deep remission improves patients' outcome in chronic myeloid leukemia (CML) treatment, highlighting the need for predictive indic...

ASBA: A-line State Space Model and B-line Attention for Sparse Optical Doppler Tomography Reconstruction

Optical Doppler Tomography (ODT) is an emerging blood flow analysis technique. A 2D ODT image (B-scan) is generated by sequentially acquiring 1D depth...

Jan 20 2026 2601.14165v1
An efficient heuristic for geometric analysis of cell deformations

Sickle cell disease causes erythrocytes to become sickle-shaped, affecting their movement in the bloodstream and reducing oxygen delivery. It has a hi...

Jan 19 2026 2601.12928v1
Enhancing Generalization in Sickle Cell Disease Diagnosis through Ensemble Methods and Feature Importance Analysis

This work presents a novel approach for selecting the optimal ensemble-based classification method and features with a primarly focus on achieving gen...

Jan 19 2026 2601.13021v1
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