Hematology

Comparing machine learning models for predicting preoperative DVT incidence in elderly hypertensive patients with hip fractures: a retrospective analysis.

Hip fractures in the elderly present a significant public health challenge globally, especially among patients with hypertension, who are at an increased risk of developing preoperative deep vein thrombosis (DVT). DVT not only heightens surgical risks but also severely impacts the rehabilitation and quality of life of patients. Early risk assessment and management in this population are therefore ...

Diabetes: Non-Invasive Blood Glucose Monitoring Using Federated Learning with Biosensor Signals.

Diabetes is a growing global health concern, affecting millions and leading to severe complications if not properly managed. The primary challenge in diabetes management is maintaining blood glucose levels (BGLs) within a safe range to prevent complications such as renal failure, cardiovascular disease, and neuropathy. Traditional methods, such as finger-prick testing, often result in low patient ...

Risk Factors and Outcomes of Hemorrhagic Transformation in Acute Ischemic Stroke Following Thrombolysis: Analysis of a Single-Center Experience and Review of the Literature.

: This is a retrospective study conducted at the Clinical County Hospital of Craiova, Romania, providing valuable insights into hemorrhagic transforma...

c-Triadem: A constrained, explainable deep learning model to identify novel biomarkers in Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disorder that requires early diagnosis for effective management. However, issues with currently availa...

Development of a machine learning-based diagnostic model using hematological parameters to differentiate periductal mastitis from granulomatous lobular mastitis.

ObjectiveNonpuerperal mastitis (NPM) is an inflammatory condition, including periductal mastitis (PDM) and granulomatous lobular mastitis (GLM). The c...

Analysis of the Relationship Between and Cytokine Gene Expression in Hematological Malignancy: Leveraging Explained Artificial Intelligence and Machine Learning for Small Dataset Insights.

This study measures expression of () and related cytokine genes in bone marrow mononuclear cells in patients with hematological malignancies, analyzi...

Assessing the Usability of ChatGPT Responses Compared to Other Online Information in Hand Surgery.

BACKGROUND: ChatGPT is a natural language processing tool with potential to increase accessibility of health information. This study aimed to: (1) ass...

Tumor-educated platelets in lung cancer.

Non-invasive diagnostic monitoring techniques have become essential for treating lung cancer (LC), which continues to be the primary cause of cancer-r...

Deep learning-based identification of patients at increased risk of cancer using routine laboratory markers.

Early screening for cancer has proven to improve the survival rate and spare patients from intensive and costly treatments due to late diagnosis. Canc...

CASCADE-FSL: Few-shot learning for collateral evaluation in ischemic stroke.

Assessing collateral circulation is essential in determining the best treatment for ischemic stroke patients as good collaterals lead to different tre...

Engineering TCR-controlled fuzzy logic into CAR T cells enhances therapeutic specificity.

Chimeric antigen receptor (CAR) T cell immunotherapy represents a breakthrough in the treatment of hematological malignancies, but poor specificity ha...

Deep learning-based classification of lymphedema and other lower limb edema diseases using clinical images.

Lymphedema is a chronic condition characterized by lymphatic fluid accumulation, primarily affecting the limbs. Its diagnosis is challenging due to sy...

Analyzing the impact of COVID-19 on seasonal infectious disease outbreak detection using hybrid SARIMAX-LSTM model.

BACKGROUND: This study estimates the incidence of seasonal infectious diseases, including influenza, norovirus, severe fever with thrombocytopenia syn...

A morphology and secretome map of pyroptosis.

Pyroptosis represents one type of programmed cell death. It is a form of inflammatory cell death that is canonically defined by caspase-1 cleavage and...

Machine learning approach for the prediction of 30-day mortality in patients with sepsis-associated delirium.

This study aimed to develop models for predicting the 30-day mortality of sepsis-associated delirium (SAD) by multiple machine learning (ML) algorithm...

Machine learning integration of multimodal data identifies key features of circulating NT-proBNP in people without cardiovascular diseases.

N-Terminal Pro-Brain Natriuretic Peptide (NT-proBNP) is important for diagnosing and predicting heart failure or many other diseases. However, few stu...

Prediction of moderate to severe bleeding risk in pediatric immune thrombocytopenia using machine learning.

UNLABELLED: This study aimed to develop and validate a risk prediction model for moderate to severe bleeding in children with immune thrombocytopenia ...

Utilisation of Deep Neural Networks for Estimation of Cajal Cells in the Anal Canal Wall of Patients with Advanced Haemorrhoidal Disease Treated by LigaSure Surgery.

Interstitial cells of Cajal (ICCs) play a key role in gastrointestinal smooth muscle contractions, but their relationship with anal canal function in ...

Febrile neutropenia management in high-risk neutropenic patients: a narrative review on antibiotic prophylaxis and empirical treatment.

INTRODUCTION: Although febrile neutropenia (FN) remains a major cause of morbidity and mortality in patients with hematologic malignancies and hematop...

Integrative Multi-Omics and Routine Blood Analysis Using Deep Learning: Cost-Effective Early Prediction of Chronic Disease Risks.

Chronic noncommunicable diseases (NCDS) are often characterized by gradual onset and slow progression, but the difficulty in early prediction remains ...