AIMC Topic: Machine Learning

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Clinical Features of Emergency Department Patients from Early COVID-19 Pandemic that Predict SARS-CoV-2 Infection: Machine-learning Approach.

The western journal of emergency medicine
INTRODUCTION: Within a few months coronavirus disease 2019 (COVID-19) evolved into a pandemic causing millions of cases worldwide, but it remains challenging to diagnose the disease in a timely fashion in the emergency department (ED). In this study ...

Clinical presentation of COVID-19 - a model derived by a machine learning algorithm.

Journal of integrative bioinformatics
COVID-19 pandemic has flooded all triage stations, making it difficult to carefully select those most likely infected. Data on total patients tested, infected, and hospitalized is fragmentary making it difficult to easily select those most likely to ...

Potential neutralizing antibodies discovered for novel corona virus using machine learning.

Scientific reports
The fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody. The recent outbreak of COVID-19 infected and killed thousands of people in the world. Rapid methods in finding pe...

Semi-automated tracking of pain in critical care patients using artificial intelligence: a retrospective observational study.

Scientific reports
Monitoring the pain intensity in critically ill patients is crucial because intense pain can cause adverse events, including poor survival rates; however, continuous pain evaluation is difficult. Vital signs have traditionally been considered ineffec...

Machine learning-based classification of mitochondrial morphology in primary neurons and brain.

Scientific reports
The mitochondrial network continually undergoes events of fission and fusion. Under physiologic conditions, the network is in equilibrium and is characterized by the presence of both elongated and punctate mitochondria. However, this balanced, homeos...

Hands-on training about overfitting.

PLoS computational biology
Overfitting is one of the critical problems in developing models by machine learning. With machine learning becoming an essential technology in computational biology, we must include training about overfitting in all courses that introduce this techn...

Prediction of femoral osteoporosis using machine-learning analysis with radiomics features and abdomen-pelvic CT: A retrospective single center preliminary study.

PloS one
BACKGROUND: Osteoporosis has increased and developed into a serious public health concern worldwide. Despite the high prevalence, osteoporosis is silent before major fragility fracture and the osteoporosis screening rate is low. Abdomen-pelvic CT (AP...

Use of artificial intelligence in the imaging of sarcopenia: A narrative review of current status and perspectives.

Nutrition (Burbank, Los Angeles County, Calif.)
Sarcopenia is a muscle disease which previously was associated only with aging, but in recent days it has been gaining more attention for its predictive value in a vast range of conditions and its potential link with overall health. Up to this point,...

Exploring the spatial reasoning ability of neural models in human IQ tests.

Neural networks : the official journal of the International Neural Network Society
Although neural models have performed impressively well on various tasks such as image recognition and question answering, their reasoning ability has been measured in only few studies. In this work, we focus on spatial reasoning and explore the spat...

HistoNet: A Deep Learning-Based Model of Normal Histology.

Toxicologic pathology
We introduce HistoNet, a deep neural network trained on normal tissue. On 1690 slides with rat tissue samples from 6 preclinical toxicology studies, tissue regions were outlined and annotated by pathologists into 46 different tissue classes. From the...