Hematology

Prediction model of gastrointestinal tumor malignancy based on coagulation indicators such as TEG and neural networks.

OBJECTIVES: Accurate determination of gastrointestinal tumor malignancy is a crucial focus of clinical research. Constructing coagulation index models using big data is feasible to achieve this goal. This study builds various prediction models through machine learning methods based on the different coagulation statuses under varying malignancy levels of gastrointestinal tumors. The aim is to use c...

AI-Derived Blood Biomarkers for Ovarian Cancer Diagnosis: Systematic Review and Meta-Analysis.

BACKGROUND: Emerging evidence underscores the potential application of artificial intelligence (AI) in discovering noninvasive blood biomarkers. However, the diagnostic value of AI-derived blood biomarkers for ovarian cancer (OC) remains inconsistent.

Development and validation of a machine learning model to predict hemostatic intervention in patients with acute upper gastrointestinal bleeding.

BACKGROUND: Acute upper gastrointestinal bleeding (UGIB) is common in clinical practice and has a wide range of severity. Along with medical therapy, ...

Machine Learning-Based VO Estimation Using a Wearable Multiwavelength Photoplethysmography Device.

The rate of oxygen consumption, which is measured as the volume of oxygen consumed per mass per minute (VO) mL/kg/min, is a critical metric for evalua...

Development of an artificial intelligence-enhanced warfarin interaction checker platform.

Warfarin is a common anticoagulant drug for thrombo-prophylaxis in stroke and venous thromboembolism, which has many advantages but also some disadvan...

Deciphering the molecular fingerprint of haemoglobin in lung cancer: A new strategy for early diagnosis using two-trace two-dimensional correlation near infrared spectroscopy (2T2D-NIRS) and machine learning techniques.

Lung cancer remains one of the deadliest malignancies worldwide, highlighting the need for highly sensitive and minimally invasive early diagnostic me...

Explainable SHAP-XGBoost models for pressure injuries among patients requiring with mechanical ventilation in intensive care unit.

pressure injuries are significant concern for ICU patients on mechanical ventilation. Early prediction is crucial for enhancing patient outcomes and r...

Anticoagulation colloidal microrobots based on heparin-mimicking polymers.

Coagulation within blood vessels is a major cause of cardiovascular disease and global mortality, highlighting the urgent need for effective anticoagu...

Precision Risk Stratification in Atrial Fibrillation: Evaluating Machine Learning Models for Bleeding Prediction and Clinical Integration.

State-of-the-art for automated machine learning predicts outcomes in poor-grade aneurysmal subarachnoid hemorrhage using routinely measured laboratory & radiological parameters: coagulation parameters and liver function as key prognosticators.

The objective of this study was to develop and evaluate automated machine learning (aML) models for predicting short-term (1-month) and medium-term (3...

Applying machine learning algorithms to explore the impact of combined noise and dust on hearing loss in occupationally exposed populations.

This study aimed to explore the combined impacts of occupational noise and dust on hearing and extra-auditory functions and identify associated risk f...

Presenting a prediction model for HELLP syndrome through data mining.

BACKGROUND: The HELLP syndrome represents three complications: hemolysis, elevated liver enzymes, and low platelet count. Since the causes and pathoge...

AI Enhanced explainable early prediction of blood culture positivity in neutropenic patients using clinical and hematologic parameters.

Leukemia patients who receive chemotherapy experience a decline in neutrophils and an increased risk of infections. Neutropenic sepsis is a life-threa...

Risk prediction of hyperuricemia based on particle swarm fusion machine learning solely dependent on routine blood tests.

Hyperuricemia has seen a continuous increase in incidence and a trend towards younger patients in recent years, posing a serious threat to human healt...

AI-driven innovations in smart multifunctional nanocarriers for drug and gene delivery: A mini-review.

The convergence of artificial intelligence (AI) and nanomedicine has revolutionized the design of smart multifunctional nanocarriers (SMNs) for drug a...

Utilization of Machine Learning in the Prediction, Diagnosis, Prognosis, and Management of Chronic Myeloid Leukemia.

Chronic myeloid leukemia is a clonal hematologic disease characterized by the presence of the Philadelphia chromosome and the BCR::ABL1 fusion protein...

Enhancing cardiovascular risk prediction: the role of wall viscoelasticity in machine learning models.

This study aims to evaluate the significance of wall viscoelasticity in enhancing cardiovascular disease (CVD) risk prediction. We collected data on t...

Nano biosensor unlocks tumor derived immune signals for the early detection of ovarian cancer.

Ovarian cancer is a critical health issue for women nowadays. Its impact is significant because of its high mortality rate (324,603 worldwide), late-s...

Intelligent risk stratification of hypertension based on ambulatory blood pressure monitoring and machine learning algorithms.

. Risk stratification of hypertension plays a crucial role in the treatment decisions and medication guidance during clinical practices. Although frui...

Rapid Blood Clot Removal via Remote Delamination and Magnetization of Clot Debris.

Micro/nano-scale robotic devices are emerging as a cutting-edge approach for precision intravascular therapies, offering the potential for highly targ...