Hematology

Investigating feature-engineered predictors for systolic blood pressure changes in an mHealth-based disease management program.

Mobile health (mHealth)-based disease management programs enable continuous monitoring of blood pressure (BP) and related health behaviors. Feature engineering may help to extract informative predictors from longitudinal data, potentially improving BP change prediction. This study aimed to evaluate whether feature-engineered predictors can improve the prediction of systolic BP (SBP) changes using ...

Machine learning-based prediction of significant intraoperative blood loss to guide risk-adapted blood management decisions in spinal surgery.

INTRODUCTION: Significant blood loss (≥ 500 mL) during spinal surgery is common and linked to greater transfusion requirements and worse outcomes. Preoperative tools for risk stratification and individualized blood-management strategies are lacking. We aimed to develop a machine-learning model predicting intraoperative significant blood loss and to assess whether a risk-adapted blood management im...

Prediction model for deep vein thrombosis stability based on multiple machine learning methods.

BackgroundThis study aimed to develop multiple machine learning (ML) models to predict DVT stability based on clinical and computed tomography (CT) te...

Early prediction of transfusion requirements in trauma patients using explainable machine learning.

INTRODUCTION: Hemorrhagic shock is the most common preventable cause of death in trauma patients. Early transfusion significantly improves survivabili...

Use of artificial intelligence in transfusion medicine practice, education and research: A mixed methodology study.

BACKGROUND AND OBJECTIVES: The artificial intelligence (AI) field holds significant promise to revolutionize healthcare, including transfusion medicin...

Using unmanned aerial vehicles (drones) to improve access to blood in low- and middle-income countries: Current challenges and opportunities.

Blood transfusion is life-saving for patients in emergencies, but low- and middle-income countries (LMICs) often face a severe shortage of banked bloo...

Peripheral immune landscape in pancreatic ductal adenocarcinoma reveals expansion of effector states with disease progression.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy often diagnosed late due to the limited sensitivity and specificity of current m...

A Machine Learning-Based Prognostic Model for Sepsis-Associated Liver Injury Using Routine Indicators.

OBJECTIVE: Sepsis-associated liver injury (SALI) occurs in approximately 40% of sepsis cases and is linked to high mortality, a challenge that may ste...

Molecular Stratification of Antiphospholipid Syndrome Through Integrative Analysis of the Whole-Blood RNA Transcriptome.

OBJECTIVE: Antiphospholipid syndrome (APS) is a thromboinflammatory disorder characterized by clinical and mechanistic heterogeneity that complicates ...

Expanding the horizon of oculomics: A narrative review on the emerging roles of the anterior segment and mobile health.

Oculomics uses the eye to gain insights into systemic health. Although initially focused on retinal imaging, oculomics now studies various ocular sign...

Machine learning and AI-Assisted red blood cell morphology analysis for effective thalassemia screening.

Automatic Detection of Subcellular-Scale Metabolic Dynamics via Spontaneous-Stimulated Raman Spatial Colocalization.

Cellular structural heterogeneity and low intrinsic contrast in label-free bright-field imaging hinder accurate localization of subcellular structures...

Decoding lactylation in neuropathic pain: Immune cell infiltration patterns and machine learning-identified candidate biomarkers.

This study aimed to identify lactylation-associated genes linked to immune infiltration and diagnostic potential in neuropathic pain using integrated ...

Identification of a type 1 diabetes-associated T cell receptor repertoire signature from the human peripheral blood.

Type 1 diabetes (T1D) is a T cell-mediated disease with a strong immunogenetic human leukocyte antigen (HLA) dependence. HLA allelic influence on the ...

Research on the Prediction of Coal Workers' Pneumoconiosis Based on Easily Detectable Clinical Data: Machine Learning Model Development and Validation Study.

BACKGROUND: Coal workers' pneumoconiosis (CWP) is the most prevalent occupational disease that causes irreversible lung damage. Early prediction of CW...

Early Risk Factor Prediction in Chronic Kidney Disease Diagnosis Using Feature Selection and Machine Learning Algorithms.

Chronic kidney disease, CKD in short, is a kind of long-term kidney illness in which rapid deterioration of kidney function is observed over a period ...

Comprehensive performance assessment of the BMIA-12 a system for bone marrow cell quantification in normal and hematological malignancy samples.

Research on the Application Value of Blood Plasma Autofluorescence Spectroscopy Model Constructed by Machine Learning Algorithms in Identifying the Types of Post-operative Kidney Transplantation Injury.

BACKGROUND: This study aimed to assess the value of blood plasma autofluorescence spectroscopy in classifying renal injury after renal transplantation...

Identification of key biomarkers for initial corticosteroid resistance in newly diagnosed primary immune thrombocytopenia.

Corticosteroid resistance is a major obstacle to the management of patients with primary immune thrombocytopenia (ITP), as the factors contributing to...

The application of Machine learning in predicting the outcomes of minimally invasive treatments for uterine Fibroids: A systematic review and meta-analysis.

RATIONALE AND OBJECTIVES: Uterine fibroids (UFs) are common benign tumors that impact women's health, particularly through symptoms such as abnormal b...