AIMC Topic: Machine Learning

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Identification of novel biomarkers for atherosclerosis using single-cell RNA sequencing and machine learning.

Mammalian genome : official journal of the International Mammalian Genome Society
Atherosclerosis (AS) is a predominant etiological factor in numerous cardiovascular diseases, with its associated complications such as myocardial infarction and stroke serving as major contributors to worldwide mortality rates. Here, we devised depe...

Machine learning prediction of unexpected readmission or death after discharge from intensive care: A retrospective cohort study.

Journal of clinical anesthesia
BACKGROUND: Intensive care units (ICUs) harbor the sickest patients with the utmost needs of medical care. Discharge from ICU needs to consider the reason for admission and stability after ICU care. Organ dysfunction or instability after ICU discharg...

Machine Learning Early Detection of SARS-CoV-2 High-Risk Variants.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evolved many high-risk variants, resulting in repeated COVID-19 waves over the past years. Therefore, accurate early warning of high-risk variants is vital for epidemic prevention a...

Conversion of hazardous waste into thermal conductive polymer: A prediction and guidance from machine learning.

Journal of environmental management
The preparation methods and thermal conductivity (TC) of the reported thermal conductive polymers vary significantly. A method to clarify the relationship between TC and influencing factors and to reach consistent conclusions is needed. In this study...

Uncovering the features of industrial odors-derived environmental complaints and proactive countermeasures by using machine-learning.

Journal of environmental management
Industrial odor-derived environmental complaints pose an emerging and far-reaching challenge in cities worldwide with intensive industries. Developing effective odor complaint management strategies is essential for mitigating the public impact of ind...

Predicting maintenance lithium response for bipolar disorder from electronic health records-a retrospective study.

PeerJ
BACKGROUND: Optimising maintenance drug treatment selection for people with bipolar disorder is challenging. There is some evidence that clinical and demographic features may predict response to lithium. However, attempts to personalise treatment cho...

Comparing ensemble learning algorithms and severity of illness scoring systems in cardiac intensive care units: a retrospective study.

Einstein (Sao Paulo, Brazil)
BACKGROUND: Beatriz Nistal-Nuño designed a machine learning system type of ensemble learning for patients undergoing cardiac surgery and intensive care unit cardiology patients, based on sequences of cardiovascular physiological measurements and othe...

An Ensemble Learning Algorithm for Cognitive Evaluation by an Immersive Virtual Reality Supermarket.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Early screening for Mild Cognitive Impairment (MCI) is crucial in delaying cognitive deterioration and treating dementia. Conventional neuropsychological tests, commonly used for MCI detection, often lack ecological validity due to their simplistic a...

Determining domestic violence against women using machine learning methods: The case of Türkiye.

Journal of evaluation in clinical practice
BACKGROUND: Domestic violence against women is a pervasive issue globally, representing a severe violation of human rights and a significant public health concern. The hidden nature of such violence and its frequent underreporting make it a critical ...

Machine learning and explainable artificial intelligence to predict pathologic stage in men with localized prostate cancer.

The Prostate
BACKGROUND: Though several nomograms exist, machine learning (ML) approaches might improve prediction of pathologic stage in patients with prostate cancer. To develop ML models to predict pathologic stage that outperform existing nomograms that use r...