Hematology

Augmented intelligence to predict 30-day mortality in patients with cancer.

An augmented intelligence tool to predict short-term mortality risk among patients with cancer could help identify those in need of actionable interventions or palliative care services. An algorithm to predict 30-day mortality risk was developed using socioeconomic and clinical data from patients in a large community hematology/oncology practice. Patients were scored weekly; algorithm performanc...

Combining microfluidics with machine learning algorithms for RBC classification in rare hereditary hemolytic anemia.

Combining microfluidics technology with machine learning represents an innovative approach to conduct massive quantitative cell behavior study and implement smart decision-making systems in support of clinical diagnostics. The spleen plays a key-role in rare hereditary hemolytic anemia (RHHA), being the organ responsible for the premature removal of defective red blood cells (RBCs). The goal is to...

ABO blood group system and risk of positive surgical margins in patients treated with robot-assisted radical prostatectomy: results in 1114 consecutive patients.

To test the hypothesis of associations between the ABO blood group system (ABO-bg) and prostate cancer (PCa) features in the surgical specimen of pati...

Optical mesoscopy, machine learning, and computational microscopy enable high information content diagnostic imaging of blood films.

Automated image-based assessment of blood films has tremendous potential to support clinical haematology within overstretched healthcare systems. To a...

Combined deep CNN-LSTM network-based multitasking learning architecture for noninvasive continuous blood pressure estimation using difference in ECG-PPG features.

The pulse arrival time (PAT), the difference between the R-peak time of electrocardiogram (ECG) signal and the systolic peak of photoplethysmography (...

Risk prediction for delayed clearance of high-dose methotrexate in pediatric hematological malignancies by machine learning.

This study aimed to establish a predictive model to identify children with hematologic malignancy at high risk for delayed clearance of high-dose meth...

Thiol modified bimodal mesoporous silica nanoparticles for removal and determination toxic vanadium from air and human biological samples in petrochemical workers.

Investigation of exposure to toxic vanadium (V) in petrochemical workers is very important for human health, and it must be removed and determined in ...

Deepometry, a framework for applying supervised and weakly supervised deep learning to imaging cytometry.

Deep learning offers the potential to extract more than meets the eye from images captured by imaging flow cytometry. This protocol describes the appl...

Prediction of venous thromboembolism with machine learning techniques in young-middle-aged inpatients.

Accumulating studies appear to suggest that the risk factors for venous thromboembolism (VTE) among young-middle-aged inpatients are different from th...

Profiling single-cell level phagocytic activity distribution with blood lactate levels.

The ability to kill infecting microbes is an essential facet of our immune response to an infection. However, phagocytic ability is often overlooked a...

Severe intraoperative bleeding predicts the risk of perioperative blood transfusion after robot-assisted radical prostatectomy.

To evaluate potential factors associated with the risk of perioperative blood transfusion (PBT) with implications on length of hospital stay (LOHS) an...

Human breast milk-based nutritherapy: A blueprint for pediatric healthcare.

Human Breast Milk (HBM) is a storehouse of micronutrients, macronutrients, immune factors, microbiota and numerous other bioactive macromolecules. Ful...

Fermented medicinal herbs improve hematological and physiological profile of Striped catfish .

This study sought to determine the effect of fermented medicinal herbs (FMH), cutchery ( ), turmeric ( ) and curcuma ( ) in combination with molasses...

Cyclic arginine-glycine-aspartic acid-modified red blood cells for drug delivery: Synthesis and evaluation.

Red blood cells (RBCs) are an excellent choice for cell preparation research because of their biocompatibility, high drug loading, and long half-life....

Deep learning-based automated detection of pulmonary embolism on CT pulmonary angiograms: No significant effects on report communication times and patient turnaround in the emergency department nine months after technical implementation.

OBJECTIVES: Rapid communication of CT exams positive for pulmonary embolism (PE) is crucial for timely initiation of anticoagulation and patient outco...

coupleCoC+: An information-theoretic co-clustering-based transfer learning framework for the integrative analysis of single-cell genomic data.

Technological advances have enabled us to profile multiple molecular layers at unprecedented single-cell resolution and the available datasets from mu...

Improved liquid-liquid extraction by modified magnetic nanoparticles for the detection of eight drugs in human blood by HPLC-MS.

Magnetic nanoparticles modified with porous titanium dioxide were used as clean-up nanospheres for the detection of eight drug poisons in human blood ...

Blood Biomarkers Predict Cardiac Workload Using Machine Learning.

INTRODUCTION: Rate pressure product (the product of heart rate and systolic blood pressure) is a measure of cardiac workload. Resting rate pressure pr...

Machine learning for predicting preoperative red blood cell demand.

BACKGROUND: The paucity of accurate quantitative standards for determining the quantity of red blood cells (RBCs) needed for perioperative patients an...

COVID-19 diagnosis by routine blood tests using machine learning.

Physicians taking care of patients with COVID-19 have described different changes in routine blood parameters. However, these changes hinder them from...