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Use of machine learning techniques to predict poor survival after hematopoietic cell transplantation for myelofibrosis.

Blood
With the incorporation of effective therapies for myelofibrosis (MF), accurately predicting outcomes after allogeneic hematopoietic cell transplantation (allo-HCT) is crucial for determining the optimal timing for this procedure. Using data from 5183...

Deep learning-based forecasting of daily maximum ozone levels and assessment of socioeconomic and health impacts in South Korea.

The Science of the total environment
Accurate forecasting of ground-level ozone (O) is essential for assessing its public health and socioeconomic impacts. This study evaluates the performance of three deep learning models-Deep Convolutional Neural Networks (Deep-CNN), Long Short-Term M...

Humans program artificial delegates to accurately solve collective-risk dilemmas but lack precision.

Proceedings of the National Academy of Sciences of the United States of America
In an era increasingly influenced by autonomous machines, it is only a matter of time before strategic individual decisions that impact collective goods will also be made virtually through the use of artificial delegates. Through a series of behavior...

FTIR-based machine learning for prediction of malignant transformation in oral epithelial dysplasia.

The Analyst
Oral squamous cell carcinoma (OSCC) is an aggressive cancer with a poor prognosis. Oral epithelial dysplasia (OED) is a precancerous lesion associated with an increased risk of malignant transformation (MT) into OSCC. However, current histopathologic...

Deep learning-quantified body composition from positron emission tomography/computed tomography and cardiovascular outcomes: a multicentre study.

European heart journal
BACKGROUND AND AIMS: Positron emission tomography (PET)/computed tomography (CT) myocardial perfusion imaging (MPI) is a vital diagnostic tool, especially in patients with cardiometabolic syndrome. Low-dose CT scans are routinely performed with PET f...

Cost-effectiveness of a novel AI technology to quantify coronary inflammation and cardiovascular risk in patients undergoing routine coronary computed tomography angiography.

European heart journal. Quality of care & clinical outcomes
AIMS: Coronary computed tomography angiography (CCTA) is a first-line investigation for chest pain in patients with suspected obstructive coronary artery disease (CAD). However, many acute cardiac events occur in the absence of obstructive CAD. We as...

Neuroimaging pattern interactions for suicide risk in depression captured by ensemble learning over transcriptome-defined parcellation.

Progress in neuro-psychopharmacology & biological psychiatry
BACKGROUND: For suicide in major depression disorder, it is urgent to seek for a reliable neuroimaging biomarker with interpretable links to molecular tissue signatures. Accordingly, we developed an ensemble learning scheme over transcriptome-defined...

Blood-based proteomic profiling identifies OSMR as a novel biomarker of AML outcomes.

Blood
Inflammation is increasingly recognized as a critical factor in acute myeloid leukemia (AML) pathogenesis. We performed blood-based proteomic profiling of 251 inflammatory proteins in 543 patients with newly diagnosed AML. Using a machine learning mo...

Prediction of bacterial and fungal bloodstream infections using machine learning in patients undergoing chemotherapy.

European journal of cancer (Oxford, England : 1990)
PURPOSE: This study aimed to develop a machine learning (ML) model to predict bloodstream infection (BSI) in chemotherapy patients.

Fetal growth disorders detection during first trimester gestation through comprehensive maternal circulating DNA profiling.

Human molecular genetics
BACKGROUND: Early diagnosis, close follow-up and timely delivery constitute the main elements for appropriate detection and management of Fetal Growth Disorders (FGD). We hypothesized that fetoplacental FGD-associated alterations can be detected in c...