AIMC Topic: Machine Learning

Clear Filters Showing 30911 to 30920 of 34417 articles

Healthcare provider evaluation of machine learning-directed care: reactions to deployment on a randomised controlled study.

BMJ health & care informatics
OBJECTIVES: Clinical artificial intelligence and machine learning (ML) face barriers related to implementation and trust. There have been few prospective opportunities to evaluate these concerns. System for High Intensity EvaLuation During Radiothera...

Editorial Commentary: Knowledge is Power: A Primer for Machine Learning.

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association
Machine learning (ML) has become an increasingly common statistical methodology in medical research. In recent years, ML techniques have been used with greater frequency to evaluate orthopaedic data. ML allows for the creation of adaptive predictive ...

Improving Methods of Identifying Anaphylaxis for Medical Product Safety Surveillance Using Natural Language Processing and Machine Learning.

American journal of epidemiology
We sought to determine whether machine learning and natural language processing (NLP) applied to electronic medical records could improve performance of automated health-care claims-based algorithms to identify anaphylaxis events using data on 516 pa...

Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling.

Toxicological sciences : an official journal of the Society of Toxicology
Physiologically based pharmacokinetic (PBPK) models are useful tools in drug development and risk assessment of environmental chemicals. PBPK model development requires the collection of species-specific physiological, and chemical-specific absorptio...

TBMaLT, a flexible toolkit for combining tight-binding and machine learning.

The Journal of chemical physics
Tight-binding approaches, especially the Density Functional Tight-Binding (DFTB) and the extended tight-binding schemes, allow for efficient quantum mechanical simulations of large systems and long-time scales. They are derived from ab initio density...

Data-driven enzyme engineering to identify function-enhancing enzymes.

Protein engineering, design & selection : PEDS
Identifying function-enhancing enzyme variants is a 'holy grail' challenge in protein science because it will allow researchers to expand the biocatalytic toolbox for late-stage functionalization of drug-like molecules, environmental degradation of p...

Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding.

Briefings in bioinformatics
Anticancer peptides (ACPs) are the types of peptides that have been demonstrated to have anticancer activities. Using ACPs to prevent cancer could be a viable alternative to conventional cancer treatments because they are safer and display higher sel...