AIMC Topic: Machine Learning

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Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for detecting, characterizing and monitoring brain tumors but definitive di...

Diagnosis of Acute Poisoning using explainable artificial intelligence.

Computers in biology and medicine
INTRODUCTION: Medical toxicology is the clinical specialty that treats the toxic effects of substances, for example, an overdose, a medication error, or a scorpion sting. The volume of toxicological knowledge and research has, as with other medical s...

Applications of artificial intelligence and machine learning approaches in echocardiography.

Echocardiography (Mount Kisco, N.Y.)
Artificial intelligence and machine learning approaches have become increasingly applied in the field of echocardiography to streamline diagnostic and prognostic assessments, and to support treatment decisions. Artificial intelligence and machine lea...

Prediction of functional outcomes of schizophrenia with genetic biomarkers using a bagging ensemble machine learning method with feature selection.

Scientific reports
Genetic variants such as single nucleotide polymorphisms (SNPs) have been suggested as potential molecular biomarkers to predict the functional outcome of psychiatric disorders. To assess the schizophrenia' functional outcomes such as Quality of Life...

Kernel machine SNP set analysis finds the association of BUD13, ZPR1, and APOA5 variants with metabolic syndrome in Tehran Cardio-metabolic Genetics Study.

Scientific reports
Metabolic syndrome (MetS) is one of the most important risk factors for cardiovascular disease. The 11p23.3 chromosomal region plays a potential role in the pathogenesis of MetS. The present study aimed to assess the association between 18 single nuc...

OASIS +: leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality.

BMC medical informatics and decision making
BACKGROUND: Severity scores assess the acuity of critical illness by penalizing for the deviation of physiologic measurements from normal and aggregating these penalties (also called "weights" or "subscores") into a final score (or probability) for q...

Current and emerging artificial intelligence applications for pediatric interventional radiology.

Pediatric radiology
Artificial intelligence in medicine can help improve the accuracy and efficiency of diagnostics, selection of therapies and prediction of outcomes. Machine learning describes a subset of artificial intelligence that utilizes algorithms that can learn...

Commentary: the ethical challenges of machine learning in psychiatry: a focus on data, diagnosis, and treatment.

Psychological medicine
The clinical interview is the psychiatrist's data gathering procedure. However, the clinical interview is not a defined entity in the way that 'vitals' are defined as measurements of blood pressure, heart rate, respiration rate, temperature, and oxyg...

DeepBSP-a Machine Learning Method for Accurate Prediction of Protein-Ligand Docking Structures.

Journal of chemical information and modeling
In recent years, machine-learning-based scoring functions have significantly improved the scoring power. However, many of these methods do not perform well in distinguishing the native structure from docked decoy poses due to the lack of decoy struct...