Hematology

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

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A Multianalyte Machine Learning Model to Detect Wrong Blood in Complete Blood Count Tube Errors in a Pediatric Setting.

BACKGROUND: Multianalyte machine learning (ML) models can potentially identify previously undetectab...

Machine Learning in Drug Development for Neurological Diseases: A Review of Blood Brain Barrier Permeability Prediction Models.

The blood brain barrier (BBB) is an endothelial-derived structure which restricts the movement of ce...

A Machine Learning Model for Diagnosing Opportunistic Infections in HIV Patients: Broad Applicability Across Infection Types.

Opportunistic infections (OIs) are the leading cause of hospitalisation and mortality among Human Im...

Towards Diagnostic Intelligent Systems in Leukemia Detection and Classification: A Systematic Review and Meta-analysis.

OBJECTIVE: Leukemia is a type of blood cancer that begins in the bone marrow and results in high num...

Predicting Sprint Potential: A Machine Learning Model Based on Blood Metabolite Profiles in Young Male Athletes.

This study aims to utilize male blood metabolite signatures for (i) distinguishing between healthy i...

An Attention-Based Deep Neural Network Model to Detect Cis-Regulatory Elements at the Single-Cell Level From Multi-Omics Data.

Cis-regulatory elements (cREs) play a crucial role in regulating gene expression and determining cel...

[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...

[Advancements in artificial intelligence for the precise diagnosis and treatment of hematological malignancies].

Hematological malignancy is a highly heterogeneous disease with complex biological characteristics a...

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...

Machine learning algorithm approach to complete blood count can be used as early predictor of COVID-19 outcome.

Although the SARS-CoV-2 infection has established risk groups, identifying biomarkers for disease ou...

Measuring the operational performance of an artificial intelligence-based blood tube-labeling robot, NESLI.

OBJECTIVES: Laboratory testing, crucial for medical diagnosis, has 3 phases: preanalytical, analytic...

Machine learning-based blood pressure estimation using impedance cardiography data.

OBJECTIVE: Accurate blood pressure (BP) measurement is crucial for the diagnosis, risk assessment, t...

Noninvasive Anemia Detection and Hemoglobin Estimation from Retinal Images Using Deep Learning: A Scalable Solution for Resource-Limited Settings.

PURPOSE: The purpose of this study was to develop and validate a deep-learning model for noninvasive...

Bioinformatics meets machine learning: identifying circulating biomarkers for vitiligo across blood and tissues.

BACKGROUND: Vitiligo is a skin disorder characterized by the progressive loss of pigmentation in the...

Construction of a predictive model for rebleeding risk in upper gastrointestinal bleeding patients based on clinical indicators such as infection.

BACKGROUND: The annual incidence of upper gastrointestinal hemorrhage (UGIB) is about 60 cases/100,0...

Significance and mechanisms of perineural invasion in malignant tumors.

Cancer remains the second leading cause of death worldwide. Tumor invasion and metastasis pose signi...

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