AIMC Topic: Machine Learning

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Predicting the Risk of Driving Under the Influence of Alcohol Using EEG-Based Machine Learning.

Computers in biology and medicine
Driving under the influence of alcohol (DUIA) is closely associated with alcohol use disorder (AUD). Our previous study on machine learning (ML) algorithms revealed a very high accuracy of decision trees with neuropsychological features in predicting...

Development and Validation of a Machine Learning-Based Nomogram for Prediction of Unplanned Reoperation Postspinal Surgery Within 30 Days.

World neurosurgery
BACKGROUND: Unplanned reoperation postspinal surgery (URPS) leads to prolonged hospital stays, higher costs, decreased patient satisfaction, and adversely affects postoperative rehabilitation. This study aimed to develop and validate prediction model...

Protein-Protein Interaction Networks Derived from Classical and Machine Learning-Based Natural Language Processing Tools.

Journal of proteome research
The study of protein-protein interactions (PPIs) provides insight into various biological mechanisms, including the binding of antibodies to antigens, enzymes to inhibitors or promoters, and receptors to ligands. Recent studies of PPIs have led to si...

A review on the role of various machine learning algorithms in microwave-assisted pyrolysis of lignocellulosic biomass waste.

Journal of environmental management
The fourth industrial revolution will heavily rely on machine learning (ML). The rationale is that these strategies make various business operations in many sectors easier. ML modeling is the discovery of hidden patterns between multiple process para...

Comprehensive assessment of cascading dams-induced hydrological alterations in the lancang-mekong river using machine learning technique.

Journal of environmental management
The development of cascading hydropower dams in river basins has significantly altered natural flow regimes in recent decades. This study investigates hydrological alterations caused by cascading hydropower dams in the Lancang-Mekong River Basin (LMR...

Assessing COVID-19 Vaccine Effectiveness and Risk Factors for Severe Outcomes through Machine Learning Techniques: A Real-World Data Study in Andalusia, Spain.

Journal of epidemiology and global health
BACKGROUND: COVID-19 vaccination has become a pivotal global strategy in managing the pandemic. Despite COVID-19 no longer being classified as a Public Health Emergency of International Concern, the virus continues affecting people worldwide. This st...

Prediction of dialysis adequacy using data-driven machine learning algorithms.

Renal failure
BACKGROUND: Adequate delivery of hemodialysis (HD), measured by the spKt/V derived from urea reduction, is an important determinant of clinical outcomes in chronic hemodialysis patients. However, the need for pre- and postdialysis blood samples preve...

Prediction of clinical pregnancy outcome after single fresh blastocyst transfer during in vitro fertilization: an ensemble learning perspective.

Human fertility (Cambridge, England)
To establish a predictive model for clinical pregnancy outcomes following the transfer of a single fresh blastocyst in vitro fertilization (IVF). 615 patients (492 in training set and 123 in test set) who underwent the first single and fresh blastocy...

Machine Learning for Mental Health: Applications, Challenges, and the Clinician's Role.

Current psychiatry reports
PURPOSE OF REVIEW: This review aims to evaluate the current psychiatric applications and limitations of machine learning (ML), defined as techniques used to train algorithms to improve performance at a task based on data. The review emphasizes the cl...

Synergizing Machine Learning, Conceptual Density Functional Theory, and Biochemistry: No-Code Explainable Predictive Models for Mutagenicity in Aromatic Amines.

Journal of chemical information and modeling
This study synergizes machine learning (ML) with conceptual density functional theory (CDFT) to develop OECD-compliant predictive models for the mutagenic activity of aromatic amines (AAs) with a fully No-Code methodology using a comprehensive data s...