Hematology

Ensemble machine learning for predicting breast cancer recurrence and mortality using clinical and hemogram data.

Accurate prediction of breast cancer recurrence remains difficult because prognosis varies significantly across molecular subtypes. This underscores the need for scalable, affordable, and interpretable prognostic tools. We developed machine learning models integrating hematological indices with clinicopathologic data to predict 2- and 10-year recurrence or all-case death. We retrospectively analyz...

Integrating epigenetic and metabolic indicators for non-invasive endometrial cancer triage: a machine learning approach.

OBJECTIVE: Abnormal uterine bleeding (AUB) is a primary symptom indicative of endometrial cancer (EC), yet its diagnosis still primarily relies on invasive biopsy. This study aimed to develop and validate a non-invasive, interpretable machine learning (ML) model integrating local epigenetic (exfoliated cell CDO1 methylation) and systemic metabolic indicators for EC triage. METHODS: CDO1 was identi...

Integrated machine learning framework identifies EPSTI1 as a key diagnostic biomarker for Sjögren's disease: multi-cohort transcriptomic validation and single-cell characterization.

OBJECTIVE: This study aimed to develop a robust transcriptomic diagnostic signature for Sjögren's disease (SjD; formerly Sjögren's syndrome) and eluci...

A machine learning-driven multitask framework for antibiotic resistance prediction in Escherichia coli, Klebsiella pneumoniae, and Enterobacter cloacae bloodstream infections among hematological malignancy patients.

BACKGROUND: Inappropriate empirical antimicrobial therapy (IEAT) significantly increases mortality in patients with resistant Gram-negative bacteremia...

Evaluation of Large Language Models in the Clinical Management of Patients With Upper Gastrointestinal Bleeding: Insights From Real-World Patient Data.

OBJECTIVE: To evaluate large language models (LLMs) for pre-endoscopy (PE) risk stratification and prediction of endoscopic findings in upper gastroin...

Study on the construction and verification of intraoperative pressure injury risk prediction model for children undergoing cardiac surgery.

BACKGROUND: This study applied nomogram to develop an intraoperative acquired pressure injury (IAPI) risk prediction model for pediatric cardiac surge...

Prediction of spinal cord injury after thoracoabdominal aortic repair by machine learning method.

BACKGROUND: Spinal cord injury (SCI) is a severe complication after thoracoabdominal aortic repair (TAAR), substantially increasing postoperative mort...

LWF-YOLO: a lightweight framework based YOLO for blood cell detection.

Accurate detection of blood cells-red blood cells, white blood cells, and platelets-is essential for diagnosing hematological disorders such as anemia...

Genomic profiling as an option for ovarian cancer diagnostics.

INTRODUCTION: Ovarian cancer (OC) is a highly heterogeneous and lethal gynecological malignancy. Precision oncology has shifted the management paradig...

AI-driven diagnostic algorithm enhances early detection of paroxysmal nocturnal hemoglobinuria in real-world settings.

Paroxysmal nocturnal haemoglobinuria (PNH) is a rare, life-threatening hematologic disease with diagnostic delays exceeding 5 years in 24% of cases. W...

Personalized antiplatelet therapy: are we ready for a precision medicine approach.

Dual antiplatelet therapy (DAPT), combining aspirin with a P2Y12 inhibitor, remains central to secondary prevention after acute coronary syndrome and ...

Using artificial intelligence to identify characteristics associated with clinical and economic outcomes in MASH (FOCUS-MASH).

BACKGROUND: Heterogeneity in the disease characteristics of metabolic dysfunction-associated steatohepatitis (MASH) complicates efforts to identify in...

Dysregulated glucocorticoid-responsive immune genes in peripheral blood mononuclear cells as a shared molecular signature of autism spectrum disorder and irritable bowel syndrome.

BACKGROUND: Autism spectrum disorder (ASD) is frequently accompanied by gastrointestinal (GI) disturbances resembling irritable bowel syndrome (IBS). ...

A blood gas parameter-based assessment model for predicting poor prognosis in sepsis: A retrospective analysis of the MIMIC-IV and eICU-CRD.

BACKGROUND: Blood gas parameters are associated with sepsis prognosis. This study aimed to develop an assessment model based on blood gas parameters f...

A machine-learning-derived online prediction model for risk during the activity period in SLE patients: a retrospective historical cross-sectional study.

OBJECTIVE: Given the challenges in predicting systemic lupus erythematosus (SLE) disease activity and the limitations of existing assessment tools, th...

Systematic review of risk prediction models for autogenous arteriovenous fistula dysfunction in maintenance hemodialysis patients.

OBJECTIVE: To systematically evaluate existing risk prediction models for autogenous arteriovenous fistula (AVF) dysfunction in maintenance hemodialys...

Transformer-Based Deep Learning Model for Predicting Hemoglobin Response to Mircera® in Hemodialysis Patients.

BACKGROUND: Anemia management in hemodialysis (HD) depends on individualized erythropoiesis-stimulating agent (ESA) dosing to achieve and maintain tar...

Plasticizer-related toxicogenomic signatures in gestational diabetes: Mendelian randomization, placental single-cell transcriptomics and trophoblast validation.

Plasticizer exposure has been associated with gestational diabetes mellitus (GDM), but the placental toxicogenomic mechanisms that may connect environ...

Validation of a pan-ELastography Machine-learning (ELM) score to predict clinically significant portal hypertension in compensated advanced chronic liver disease.

BACKGROUND: Clinically significant portal hypertension (CSPH) drives decompensation and mortality in advanced chronic liver disease (ACLD). Although n...

Predicting COVID-19 severity using machine learning: the role of clinical parameters, blood groups, and vaccination status across age groups.

OBJECTIVES: COVID-19 has had strong impacts on both global health systems and economies. Therefore, understanding disease severity and progression is ...