AIMC Topic: Humans

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Automated Neural Architecture Search for Cardiac Amyloidosis Classification from [18F]-Florbetaben PET Images.

Journal of imaging informatics in medicine
Medical image classification using convolutional neural networks (CNNs) is promising but often requires extensive manual tuning for optimal model definition. Neural architecture search (NAS) automates this process, reducing human intervention signifi...

Exploring key factors influencing depressive symptoms among middle-aged and elderly adult population: A machine learning-based method.

Archives of gerontology and geriatrics
OBJECTIVE: This paper aims to investigate the key factors, including demographics, socioeconomics, physical well-being, lifestyle, daily activities and loneliness that can impact depressive symptoms in the middle-aged and elderly population using mac...

Prognosis of major bleeding based on residual variables and machine learning for critical patients with upper gastrointestinal bleeding: A multicenter study.

Journal of critical care
BACKGROUND: Upper gastrointestinal bleeding (UGIB) is a significant cause of morbidity and mortality worldwide. This study investigates the use of residual variables and machine learning (ML) models for predicting major bleeding in patients with seve...

Applications of artificial intelligence in interventional oncology: An up-to-date review of the literature.

Japanese journal of radiology
Interventional oncology provides image-guided therapies, including transarterial tumor embolization and percutaneous tumor ablation, for malignant tumors in a minimally invasive manner. As in other medical fields, the application of artificial intell...

Controversies in Artificial Intelligence in Neurosurgery.

Neurosurgery clinics of North America
Artificial intelligence (AI) has evolved from science fiction to a technology infiltrating everyday life. In neurosurgery, clinicians and researchers are exploring ways to implement this powerful tool to improve the safety and efficiency of the perio...

Machine learning based classification of excessive smartphone users via neuronal cue reactivity.

Psychiatry research. Neuroimaging
Excessive Smartphone Use (ESU) poses a significant challenge in contemporary society, yet its recognition as a distinct disorder remains ambiguous. This study aims to address this gap by leveraging functional magnetic resonance imaging (fMRI) data an...

Self-adaptive label discovery and multi-view fusion for complementary label learning.

Neural networks : the official journal of the International Neural Network Society
Unlike traditional supervised classification, complementary label learning (CLL) operates under a weak supervision framework, where each sample is annotated by excluding several incorrect labels, known as complementary labels (CLs). Despite reducing ...

A machine learning algorithm for the identification elevated Lp(a) in patients with, or high-risk of having, coronary heart disease.

International journal of cardiology
BACKGROUND: Decision tree algorithms, obtained by machine learning, provide clusters of patients with similar clinical patterns by the identification of variables that best merge with a given dependent variable.

People's judgments of humans and robots in a classic moral dilemma.

Cognition
How do ordinary people evaluate robots that make morally significant decisions? Previous work has found both equal and different evaluations, and different ones in either direction. In 13 studies (N = 7670), we asked people to evaluate humans and rob...

De Novo Natural Language Processing Algorithm Accurately Identifies Myxofibrosarcoma From Pathology Reports.

Clinical orthopaedics and related research
BACKGROUND: Available codes in the ICD-10 do not accurately reflect soft tissue sarcoma diagnoses, and this can result in an underrepresentation of soft tissue sarcoma in databases. The National VA Database provides a unique opportunity for soft tiss...