AIMC Topic: Humans

Clear Filters Showing 20151 to 20160 of 95995 articles

Combining clinical and molecular data for personalized treatment in acute myeloid leukemia: A machine learning approach.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The standard of care in Acute Myeloid Leukemia patients has remained essentially unchanged for nearly 40 years. Due to the complicated mutational patterns within and between individual patients and a lack of targeted agents ...

Predicting prolonged length of stay following revision total knee arthroplasty: A national database analysis using machine learning models.

International journal of medical informatics
BACKGROUND: As the number of revision total knee arthroplasty (TKA) continues to rise, close attention has been paid to factors influencing postoperative length of stay (LOS). The aim of this study is to develop generalizable machine learning (ML) al...

Electrocardiogram-Based Deep Learning to Predict Mortality in Repaired Tetralogy of Fallot.

JACC. Clinical electrophysiology
BACKGROUND: Artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis shows promise to predict mortality in adults with acquired cardiovascular diseases. However, its application to the growing repaired tetralogy of Fallot (rTOF) populatio...

A machine learning-based method for feature reduction of methylation data for the classification of cancer tissue origin.

International journal of clinical oncology
BACKGROUND: Genome DNA methylation profiling is a promising yet costly method for cancer classification, involving substantial data. We developed an ensemble learning model to identify cancer types using methylation profiles from a limited number of ...

FGTN: Fragment-based graph transformer network for predicting reproductive toxicity.

Archives of toxicology
Reproductive toxicity is one of the important issues in chemical safety. Traditional laboratory testing methods are costly and time-consuming with raised ethical issues. Only a few in silico models have been reported to predict human reproductive tox...

Opportunity and Opportunism in Artificial Intelligence-Powered Data Extraction: A Value-Centered Approach.

AJR. American journal of roentgenology
Radiologists' traditional role in the diagnostic process is to respond to specific clinical questions and reduce uncertainty enough to permit treatment decisions to be made. This charge is rapidly evolving due to forces such as artificial intelligenc...

Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis.

Psychiatry and clinical neurosciences
BACKGROUND: Recent advances in multivariate pattern recognition have fostered the search for reliable neuroimaging-based biomarkers in psychiatric conditions, including schizophrenia. These approaches consider the complex pattern of alterations in br...

Learning by thinking in natural and artificial minds.

Trends in cognitive sciences
Canonical cases of learning involve novel observations external to the mind, but learning can also occur through mental processes such as explaining to oneself, mental simulation, analogical comparison, and reasoning. Recent advances in artificial in...

Machine Learning on 50,000 Manuscripts Shows Increased Clinical Research by Academic Cardiac Surgeons.

The Journal of surgical research
INTRODUCTION: Academic cardiac surgeons are productive researchers and innovators. We sought to perform a comprehensive machine learning (ML)-based characterization of cardiac surgery research over the past 40 y to identify trends in research pursuit...

DentAge: Deep learning for automated age prediction using panoramic dental X-ray images.

Journal of forensic sciences
Age estimation plays a crucial role in various fields, including forensic science and anthropology. This study aims to develop and validate DentAge, a deep-learning model for automated age prediction using panoramic dental X-ray images. DentAge was t...