Hematology

Development of a new disseminated intravascular coagulation score tailored for patients with chronic liver disease: a prospective observational study.

BACKGROUND: Diagnosing disseminated intravascular coagulation (DIC) in patients with chronic liver disease is challenging because cirrhosis-related hemostatic abnormalities mimic DIC laboratory features, leading to frequent overdiagnosis using conventional DIC scoring systems. OBJECTIVES: This study aimed to evaluate the performance of existing DIC scores in cirrhotic patients and develop a tailor...

Diagnosis and classification of thalassemia disease using machine learning: Comparative analysis of traditional models and a novel hybrid approach.

Thalassemia is a hereditary blood disorder characterized by abnormal hemoglobin production. Common diagnostic methods include complete blood count, high-performance liquid chromatography, and hemoglobin electrophoresis. While physicians make the final diagnosis, advancements in artificial intelligence, specifically machine learning (ML) and deep learning, offer significant potential as auxiliary t...

Population-Level Raman Biochemical Staging of Malaria in Human Red Blood Cells Using Interpretable Machine Learning.

Accurate malaria staging is vital for treatment decisions and monitoring of transmission. Because mature Plasmodium falciparum parasites sequester in ...

Designing the Future of Hemostasis.

Bleeding disorders arising from dysfunctional platelet-protein interactions pose a significant clinical challenge due to their heterogeneity and compl...

An artificial intelligence model for prediction of hepatocellular carcinoma risk in patients with chronic hepatitis C.

Hepatocellular carcinoma (HCC) still occurs in patients with hepatitis C who achieved sustained virologic response (SVR) after direct-acting antiviral...

Prediction of syncope recurrence after percutaneous closure of patent foramen ovale based on machine learning.

Patent foramen ovale (PFO) has been increasingly associated with otherwise unexplained syncope, but predictors of syncope recurrence after percutaneou...

Integration of Transcriptomics With Interpretable Artificial Intelligence for Identifying Molecular Signatures of Physiological Stress in Sleep Deprivation.

Sleep deprivation induces systemic physiological stress accompanied by transcriptomic remodelling and immune dysregulation, yet objective molecular in...

Artificial Intelligence Among U.S. Hematology Oncology Fellows: A Multicenter Survey of Education, Attitudes, and Clinical Use.

PURPOSE: A prior national survey of U.S. hematology/oncology (H/O) fellowship curricula demonstrated substantial heterogeneity and limited protected d...

Clinical subtypes and prognosis of new-onset atrial fibrillation in critically ill patients.

BACKGROUND: New-onset atrial fibrillation (NOAF) is a common cardiovascular complication in critically ill patients and is consistently associated wit...

Unified comparison of machine learning paradigms for blood transfusion prediction in pediatric congenital heart surgery.

Blood transfusion prediction studies in pediatric cardiac surgery have employed direct regression, two-stage, and multi-class classification paradigms...

Quality of AI-generated post-operative patient education after minimally invasive gynecologic surgery: A comparative analysis.

STUDY OBJECTIVE: To compare the quality of AI-generated responses to gynecologic post-operative questions with educational materials published by prof...

Techniques and outcomes review of the endoscopic management of gastrointestinal bleeding.

INTRODUCTION: Gastrointestinal (GI) bleeding is a frequent and potentially life-threatening emergency that imposes a substantial healthcare burden wor...

Automated leukemia detection from microscopic images using deep transfer learning with explainable AI-based analysis.

Leukemia, a life-threatening hematological malignancy, necessitates early and accurate diagnosis for effective treatment. Traditional microscopic exam...

Advancing child health: forecasting the next great research achievements.

Advances in pediatric research have improved the lives of infants, children, adolescents, and society at large. A decade ago the American Academy of P...

Electronic health record-derived machine learning model for hypoglycemia risk prediction in type 2 diabetes mellitus patients: development and validation.

BACKGROUND: Hypoglycemia is a serious complication of diabetes. Early recognition of hypoglycemia can improve clinical prognosis, however, traditional...

Machine learning prediction of long-term postoperative pneumonia risk: a retrospective cohort study.

BACKGROUND: Postoperative pneumonia is a significant complication, highlighting a patient's ongoing vulnerability. While traditional tools focus on sh...

Machine learning prediction of PICC-associated thrombotic complications in critically ill patients.

BACKGROUND: Peripherally inserted central catheters (PICCs) are widely used vascular access devices in intensive care, yet thrombotic complications re...

Machine learning identifies traffic-related pollutant mixtures as potential factors suggestive of association with osteoporosis in a cross-sectional analysis of NHANES.

The combined effect of traffic-related pollutant mixtures on osteoporosis (OP) remains unclear. This study aimed to evaluate such associations using m...

Integration of network pharmacology, deep learning, and molecular biology reveals the efficacy of Citrus aurantium L. var. amara Engl. blossom extract in ameliorating diabetic osteoporosis.

ETHNOPHARMACOLOGICAL RELEVANCE: Diabetic osteoporosis (DOP) is a type of metabolic bone disease. Blossom of Citrus aurantium L. var. amara Engl. (CAVA...

A Multidimensional Integrated Strategy for Identifying Anti-thrombotic Quality Markers(Q-Markers) in Taohong Siwu Decoction through Combined Analysis of Chemical Profiling, Bioactivity, and Pharmacodynamic Efficacy.

ETHNOPHARMACOLOGICAL RELEVANCE: Taohong Siwu Decoction (TSD), originating from the Qing Dynasty medical text Gynecology Ice Mirror, is a representativ...