AIMC Topic: Machine Learning

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Prediction of venous thromboembolism with machine learning techniques in young-middle-aged inpatients.

Scientific reports
Accumulating studies appear to suggest that the risk factors for venous thromboembolism (VTE) among young-middle-aged inpatients are different from those among elderly people. Therefore, the current prediction models for VTE are not applicable to you...

Microbiome Preprocessing Machine Learning Pipeline.

Frontiers in immunology
BACKGROUND: 16S sequencing results are often used for Machine Learning (ML) tasks. 16S gene sequences are represented as feature counts, which are associated with taxonomic representation. Raw feature counts may not be the optimal representation for ...

Predicting malaria epidemics in Burkina Faso with machine learning.

PloS one
Accurately forecasting the case rate of malaria would enable key decision makers to intervene months before the onset of any outbreak, potentially saving lives. Until now, methods that forecast malaria have involved complicated numerical simulations ...

Applications of Artificial Intelligence and Machine Learning in Disasters and Public Health Emergencies.

Disaster medicine and public health preparedness
Indexed literature (from 2015 to 2020) on artificial intelligence (AI) technologies and machine learning algorithms (ML) pertaining to disasters and public health emergencies were reviewed. Search strategies were developed and conducted for PubMed an...

COVID-19 cough classification using machine learning and global smartphone recordings.

Computers in biology and medicine
We present a machine learning based COVID-19 cough classifier which can discriminate COVID-19 positive coughs from both COVID-19 negative and healthy coughs recorded on a smartphone. This type of screening is non-contact, easy to apply, and can reduc...

Development of Crime Scene Intelligence Using a Hand-Held Raman Spectrometer and Transfer Learning.

Analytical chemistry
The classification of ignitable liquids, such as gasoline, is critical crime scene intelligence to assist arson investigations. Rapid field gasoline classification is challenging because the current forensic testing standard requires gas chromatograp...

Uplink vs. Downlink: Machine Learning-Based Quality Prediction for HTTP Adaptive Video Streaming.

Sensors (Basel, Switzerland)
Streaming video is responsible for the bulk of Internet traffic these days. For this reason, Internet providers and network operators try to make predictions and assessments about the streaming quality for an end user. Current monitoring solutions ar...

New Results on Radioactive Mixture Identification and Relative Count Contribution Estimation.

Sensors (Basel, Switzerland)
Detecting nuclear materials in mixtures is challenging due to low concentration, environmental factors, sensor noise, source-detector distance variations, and others. This paper presents new results on nuclear material identification and relative cou...

Identifying Patient-Ventilator Asynchrony on a Small Dataset Using Image-Based Transfer Learning.

Sensors (Basel, Switzerland)
Mechanical ventilation is an essential life-support treatment for patients who cannot breathe independently. Patient-ventilator asynchrony (PVA) occurs when ventilatory support does not match the needs of the patient and is associated with a series o...

Multivariable mortality risk prediction using machine learning for COVID-19 patients at admission (AICOVID).

Scientific reports
In Coronavirus disease 2019 (COVID-19), early identification of patients with a high risk of mortality can significantly improve triage, bed allocation, timely management, and possibly, outcome. The study objective is to develop and validate individu...