Hematology

A machine-learning parsimonious multivariable predictive model of mortality risk in patients with Covid-19.

The COVID-19 pandemic is impressively challenging the healthcare system. Several prognostic models have been validated but few of them are implemented in daily practice. The objective of the study was to validate a machine-learning risk prediction model using easy-to-obtain parameters to help to identify patients with COVID-19 who are at higher risk of death. The training cohort included all patie...

A weakly supervised deep learning approach for label-free imaging flow-cytometry-based blood diagnostics.

The application of machine learning approaches to imaging flow cytometry (IFC) data has the potential to transform the diagnosis of hematological diseases. However, the need for manually labeled single-cell images for machine learning model training has severely limited its clinical application. To address this, we present iCellCnn, a weakly supervised deep learning approach for label-free IFC-bas...

Diagnostic AI Modeling and Pseudo Time Series Profiling of AD and PD Based on Individualized Serum Proteome Data.

Parkinson's disease (PD), Alzheimer's disease (AD) are common neurodegenerative disease, while mild cognitive impairment (MCI) may be happened in the...

Predictive value of red blood cell distribution width in septic shock patients with thrombocytopenia: A retrospective study using machine learning.

BACKGROUND: Sepsis-associated thrombocytopenia (SAT) is common in critical patients and results in the elevation of mortality. Red cell distribution w...

Early prediction of in-hospital death of COVID-19 patients: a machine-learning model based on age, blood analyses, and chest x-ray score.

An early-warning model to predict in-hospital mortality on admission of COVID-19 patients at an emergency department (ED) was developed and validated ...

Deep learning for bone marrow cell detection and classification on whole-slide images.

Bone marrow (BM) examination is an essential step in both diagnosing and managing numerous hematologic disorders. BM nucleated differential count (NDC...

LSPR based on-chip detection of dengue NS1 antigen in whole blood.

The development of a biosensor for rapid and quantitative detection of the dengue virus continues to remain a challenge. We report a lab-on-chip devic...

Characteristics of brain connectivity during verbal fluency test: Convolutional neural network for functional near-infrared spectroscopy analysis.

Human connectome describes the complicated connection matrix of nervous system among human brain. It also possesses high potential of assisting doctor...

Automated detection of leukemia by pretrained deep neural networks and transfer learning: A comparison.

Leukemia is usually diagnosed by viewing the smears of blood and bone marrow using microscopes and complex Cytochemical tests can be used to authorize...

Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood-Brain Barrier Passage.

A successful passage of the blood-brain barrier (BBB) is an essential prerequisite for the drug molecules designed to act on the central nervous syste...

Blood-Based Immune Profiling Combined with Machine Learning Discriminates Psoriatic Arthritis from Psoriasis Patients.

Psoriasis (Pso) is a chronic inflammatory skin disease, and up to 30% of Pso patients develop psoriatic arthritis (PsA), which can lead to irreversibl...

Development of monoclonal antibodies for quantification of bovine tumor necrosis factor-α.

The expression of the proinflammatory cytokine tumor necrosis factor-α (TNF-α) is associated with production losses in dairy cows and is a hallmark of...

Stem-cell based, machine learning approach for optimizing natural killer cell-based personalized immunotherapy for high-grade ovarian cancer.

Advanced high-grade serous ovarian cancer continues to be a therapeutic challenge for those affected using the current therapeutic interventions. Ther...

Artificial intelligence models in chronic lymphocytic leukemia - recommendations toward state-of-the-art.

Artificial intelligence (AI), machine learning and predictive modeling are becoming enabling technologies in many day-to-day applications. Translation...

Analysis of COVID-19 severity from the perspective of coagulation index using evolutionary machine learning with enhanced brain storm optimization.

Coronavirus 2019 (COVID-19) is an extreme acute respiratory syndrome. Early diagnosis and accurate assessment of COVID-19 are not available, resulting...

Allergen Risk Assessment for Specific Allergy to Small Ruminant's Milk: Development of Sensitive Immunoassays to Detect Goat's and Sheep's Milk Contaminations in Dairy Food Matrices.

Despite a high level of sequence identity between cow's, goat's, and sheep's milk (CM, GM, and SM, respectively) proteins, some patients tolerant to C...

Optimal Training Configurations of a CNN-LSTM-Based Tracker for a Fall Frame Detection System.

In recent years, there has been an immense amount of research into fall event detection. Generally, a fall event is defined as a situation in which a ...

Artificial Intelligence and Capsule Endoscopy: Automatic Detection of Small Bowel Blood Content Using a Convolutional Neural Network.

INTRODUCTION: Capsule endoscopy has revolutionized the management of patients with obscure gastrointestinal bleeding. Nevertheless, reading capsule en...

Using different machine learning models to classify patients into mild and severe cases of COVID-19 based on multivariate blood testing.

COVID-19 is a serious respiratory disease. The ever-increasing number of cases is causing heavier loads on the health service system. Using 38 blood t...

Risk prediction of clinical adverse outcomes with machine learning in a cohort of critically ill patients with atrial fibrillation.

Critically ill patients affected by atrial fibrillation are at high risk of adverse events: however, the actual risk stratification models for haemorr...