COVID-19

Testing Machine Learning Models to Predict Postoperative Ileus after Colorectal Surgery.

Postoperative ileus (POI) is a common complication after colorectal surgery, leading to increased h...

Leveraging permutation testing to assess confidence in positive-unlabeled learning applied to high-dimensional biological datasets.

BACKGROUND: Compared to traditional supervised machine learning approaches employing fully labeled s...

A high-accuracy lightweight network model for X-ray image diagnosis: A case study of COVID detection.

The Coronavirus Disease 2019(COVID-19) has caused widespread and significant harm globally. In order...

Evolutionary Strategies AI Addresses Multiple Technical Challenges in Deep Learning Deployment: Proof-of-Principle Demonstration for Neuroblastoma Brain Metastasis Detection.

Two significant obstacles hinder the advancement of Radiology AI. The first is the challenge of over...

Prediction of short-term progression of COVID-19 pneumonia based on chest CT artificial intelligence: during the Omicron epidemic.

BACKGROUND AND PURPOSE: The persistent progression of pneumonia is a critical determinant of adverse...

Analysis of convolutional neural networks for fronto-temporal dementia biomarker discovery.

PURPOSE: Frontotemporal lobe dementia (FTD) results from the degeneration of the frontal and tempora...

Development of a real-time cattle lameness detection system using a single side-view camera.

Recent advancements in machine learning and deep learning have revolutionized various computer visio...

Use of machine learning to identify protective factors for death from COVID-19 in the ICU: a retrospective study.

BACKGROUND: Patients in serious condition due to COVID-19 often require special care in intensive ca...

Exome sequence analysis identifies rare coding variants associated with a machine learning-based marker for coronary artery disease.

Coronary artery disease (CAD) exists on a spectrum of disease represented by a combination of risk f...

Time-Series MR Images Identifying Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using a Deep Learning Approach.

BACKGROUND: Pathological complete response (pCR) is an essential criterion for adjusting follow-up t...

Detecting DoS Attacks through Synthetic User Behavior with Long Short-Term Memory Network.

With the escalation in the size and complexity of modern Denial of Service attacks, there is a need ...

Machine learning prediction of malaria vaccine efficacy based on antibody profiles.

Immunization through repeated direct venous inoculation of Plasmodium falciparum (Pf) sporozoites (P...

Cancer diagnosis in the post-coronavirus disease era: the promising role of telepathology and artificial intelligence.

Real concerns, artificial intelligence: Reality testing for psychiatrists.

The use of augmented or artificial intelligence (AI) in healthcare promises groundbreaking advanceme...

Two-part predictive modeling for COVID-19 cases and deaths in the U.S.

COVID-19 prediction has been essential in the aid of prevention and control of the disease. The moti...

Functional Neural Networks for High-Dimensional Genetic Data Analysis.

Artificial intelligence (AI) is a thriving research field with many successful applications in areas...

TSVM: Transfer Support Vector Machine for Predicting MPRA Validated Regulatory Variants.

Genome-wide association studies have shown that common genetic variants associated with complex dise...

Phenotype clustering of hospitalized high-risk patients with COVID-19 - a machine learning approach within the multicentre, multinational PCHF-COVICAV registry.

IMTRODUCTION: The high-risk population of patients with cardiovascular (CV) disease or risk factors ...

MiniSeg: An Extremely Minimum Network Based on Lightweight Multiscale Learning for Efficient COVID-19 Segmentation.

The rapid spread of the new pandemic, i.e., coronavirus disease 2019 (COVID-19), has severely threat...

Classifying Routine Clinical Electroencephalograms With Multivariate Iterative Filtering and Convolutional Neural Networks.

Electroencephalogram (EEG) is widely used in basic and clinical neuroscience to explore neural state...

Identification of drug responsive enhancers by predicting chromatin accessibility change from perturbed gene expression profiles.

Individual may response to drug treatment differently due to their genetic variants located in enhan...