AIMC Topic: Machine Learning

Clear Filters Showing 32361 to 32370 of 34417 articles

Routine Laboratory Blood Tests Predict SARS-CoV-2 Infection Using Machine Learning.

Clinical chemistry
BACKGROUND: Accurate diagnostic strategies to identify SARS-CoV-2 positive individuals rapidly for management of patient care and protection of health care personnel are urgently needed. The predominant diagnostic test is viral RNA detection by RT-PC...

The Development and Validation of a Machine Learning Model to Predict Bacteremia and Fungemia in Hospitalized Patients Using Electronic Health Record Data.

Critical care medicine
OBJECTIVES: Bacteremia and fungemia can cause life-threatening illness with high mortality rates, which increase with delays in antimicrobial therapy. The objective of this study is to develop machine learning models to predict blood culture results ...

Artificial Intelligence in Pathology: A Simple and Practical Guide.

Advances in anatomic pathology
Artificial intelligence (AI) is having an increasing impact on the field of pathology, as computation techniques allow computers to perform tasks previously performed by people. Here, we offer a simple and practical guide to AI methods used in pathol...

ModelTeller: Model Selection for Optimal Phylogenetic Reconstruction Using Machine Learning.

Molecular biology and evolution
Statistical criteria have long been the standard for selecting the best model for phylogenetic reconstruction and downstream statistical inference. Although model selection is regarded as a fundamental step in phylogenetics, existing methods for this...

Detecting and Classifying Self-injurious Behavior in Autism Spectrum Disorder Using Machine Learning Techniques.

Journal of autism and developmental disorders
Traditional self-injurious behavior (SIB) management can place compliance demands on the caregiver and have low ecological validity and accuracy. To support an SIB monitoring system for autism spectrum disorder (ASD), we evaluated machine learning me...

Artificial intelligence in medical imaging: A radiomic guide to precision phenotyping of cardiovascular disease.

Cardiovascular research
Rapid technological advances in non-invasive imaging, coupled with the availability of large data sets and the expansion of computational models and power, have revolutionized the role of imaging in medicine. Non-invasive imaging is the pillar of mod...

Red blood cell classification in lensless single random phase encoding using convolutional neural networks.

Optics express
Rapid cell identification is achieved in a compact and field-portable system employing single random phase encoding to record opto-biological signatures of living biological cells of interest. The lensless, 3D-printed system uses a diffuser to encode...

Bio-inspired gas sensing: boosting performance with sensor optimization guided by "machine learning".

Faraday discussions
The performance of existing gas sensors often degrades in field conditions because of the loss of measurement accuracy in the presence of interferences. Thus, new sensing approaches are required with improved sensor selectivity. We are developing a n...

Towards development of a novel universal medical diagnostic method: Raman spectroscopy and machine learning.

Chemical Society reviews
Many problems exist within the myriad of currently employed screening and diagnostic methods. Further, an incredibly wide variety of procedures are used to identify an even greater number of diseases which exist in the world. There is a definite unme...