AIMC Topic: Machine Learning

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Forecasting tuberculosis using diabetes-related google trends data.

Pathogens and global health
Online activity-based data can be used to aid infectious disease forecasting. Our aim was to exploit the converging nature of the tuberculosis (TB) and diabetes epidemics to forecast TB case numbers. Thus, we extended TB prediction models based on tr...

Real-time Inference and Detection of Disruptive EEG Networks for Epileptic Seizures.

Scientific reports
Recent studies in brain science and neurological medicine paid a particular attention to develop machine learning-based techniques for the detection and prediction of epileptic seizures with electroencephalogram (EEG). As a noninvasive monitoring met...

The compatibility of theoretical frameworks with machine learning analyses in psychological research.

Current opinion in psychology
Supervised machine learning has been increasingly used in psychology and psychiatry research. Machine learning offers an important advantage over traditional statistical analyses: statistical model training in example data to enhance predictions in e...

Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review.

Journal of medical systems
Coronaviruses (CoVs) are a large family of viruses that are common in many animal species, including camels, cattle, cats and bats. Animal CoVs, such as Middle East respiratory syndrome-CoV, severe acute respiratory syndrome (SARS)-CoV, and the new v...

Identifying barley pan-genome sequence anchors using genetic mapping and machine learning.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
We identified 1.844 million barley pan-genome sequence anchors from 12,306 genotypes using genetic mapping and machine learning. There is increasing evidence that genes from a given crop genotype are far to cover all genes in that species; thus, buil...

A Machine Learning-Based Approach for Predicting Patient Punctuality in Ambulatory Care Centers.

International journal of environmental research and public health
Late-arriving patients have become a prominent concern in several ambulatory care clinics across the globe. Accommodating them could lead to detrimental ramifications such as schedule disruption and increased waiting time for forthcoming patients, wh...

Predicting Survival After Extracorporeal Membrane Oxygenation by Using Machine Learning.

The Annals of thoracic surgery
BACKGROUND: Venoarterial (VA) extracorporeal membrane oxygenation (ECMO) undoubtedly saves many lives, but it is associated with a high degree of patient morbidity, mortality, and resource use. This study aimed to develop a machine learning algorithm...

Prediction of mortality in Intensive Care Units: a multivariate feature selection.

Journal of biomedical informatics
CONTEXT: The critical nature of patients in Intensive Care Units (ICUs) demands intensive monitoring of their vital signs as well as highly qualified professional assistance. The combination of these needs makes ICUs very expensive, which requires in...

Development of a machine learning-based multimode diagnosis system for lung cancer.

Aging
As an emerging technology, artificial intelligence has been applied to identify various physical disorders. Here, we developed a three-layer diagnosis system for lung cancer, in which three machine learning approaches including decision tree C5.0, ar...