Hematology

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

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WISP2/CCN5 revealed as a potential diagnostic biomarker for endometriosis based on machine learning and single-cell transcriptomic analysis.

OBJECTIVE: Endometriosis is a prevalent gynecological disease characterized by the ectopic growth of...

Prediction of Sepsis after Endourologic Kidney Stone Surgery: A Machine Learning Approach.

Sepsis secondary to urinary tract infection after kidney stone surgery is associated with considera...

Analysis of maternal fetal outcomes and complete blood count parameters according to the stages of placental abruption: a retrospective study.

BACKGROUND: To compare the demographic characteristics, maternal and perinatal outcomes and hemoglob...

Machine learning-based model for acute asthma exacerbation detection using routine blood parameters.

BACKGROUND: Acute asthma exacerbations (AAEs) are a leading cause of asthma-related morbidity and mo...

Advanced molecular approaches to thalassemia disorder and the selection of molecular-level diagnostic testing in resource-limited settings.

Beta-thalassemia is a genetic disorder that significantly burdens healthcare systems globally. This ...

A tumor microenvironment model for glioma diagnosis and therapeutic evaluation based on the analysis of tissues and biological fluids.

Traditional glioma diagnostic methods have limitations, while liquid biopsy is a promising non-invas...

Machine learning application for bleeding risk prediction in patients with atrial fibrillation treated with oral anticoagulation.

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with a significantly increased...

The 2024 ESC guidelines on atrial fibrillation: essential updates for everyday clinical practice.

Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia, and it is associated with substan...

DeepHeme, a high-performance, generalizable deep ensemble for bone marrow morphometry and hematologic diagnosis.

Cytomorphological analysis of the bone marrow aspirate (BMA) is pivotal for the diagnostic workup of...

28-day all-cause mortality in patients with alcoholic cirrhosis: a machine learning prediction model based on the MIMIC-IV.

To develop and validate a machine learning prediction model for 28-day all-cause mortality in patien...

Development and validation of an interpretable risk prediction model for the early classification of thalassemia.

Thalassemia is an inherited blood disorder. Current diagnostic methods mainly rely on sophisticated ...

Exploring the performance of an artificial intelligence- and morphology-driven workflow integrating 4 platelet enumeration technologies.

OBJECTIVE: Platelet count, one of the main parameters of the complete blood count, requires accurate...

Assessing serum thrombopoietin for enhanced diagnosis of ITP, AA, and MDS using machine learning: A retrospective cohort study.

Differentiating between immune thrombocytopenia (ITP), aplastic anemia (AA), and myelodysplastic syn...

AI-Driven Blood Loss Prediction in Large-Volume Liposuction: Enhancing Precision and Patient Safety.

BACKGROUND: Over 2.3 million liposuctions are performed annually with a complication rate of about 5...

Direct-to-Consumer Testing: Relevance for Diabetes Therapy.

The performance of laboratory measurements by the people with diabetes (PwD) themselves ("direct-to-...

Role of tight junctions in three-dimensional mechanical model of blood-brain barrier.

The increasing prevalence of central nervous system (CNS) disorders has imposed a significant social...

Predicting rapid kidney function decline in middle-aged and elderly Chinese adults using machine learning techniques.

The rapid decline of kidney function in middle-aged and elderly people has become an increasingly se...

Optimization of hemocompatibility metrics in ventricular assist device design using machine learning and CFD-based response surface analysis.

Ventricular assist devices (VADs) are essential for end-stage heart failure patients, but their desi...

Machine learning and multi-omics analysis reveal key regulators of proneural-mesenchymal transition in glioblastoma.

Glioblastoma (GBM) is classified into subtypes according to the molecular expression profile; the pr...

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