AIMC Topic: Female

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Improving the Detection of Potential Cases of Familial Hypercholesterolemia: Could Machine Learning Be Part of the Solution?

Journal of the American Heart Association
BACKGROUND: Familial hypercholesterolemia (FH), while highly prevalent, is a significantly underdiagnosed monogenic disorder. Improved detection could reduce the large number of cardiovascular events attributable to poor case finding. We aimed to ass...

Using AI-Based Virtual Companions to Assist Adolescents with Autism in Recognizing and Addressing Cyberbullying.

Sensors (Basel, Switzerland)
Social media platforms and online gaming sites play a pervasive role in facilitating peer interaction and social development for adolescents, but they also pose potential threats to health and safety. It is crucial to tackle cyberbullying issues with...

Assessment of the impacts of artificial intelligence (AI) on intercultural communication among postgraduate students in a multicultural university environment.

Scientific reports
Artificial intelligence (AI) broadly influences different aspects of human life, especially human communication. One of the main concerns of the broad use of AI in daily interactions among different people could be whether it helps them interact easi...

Examining customer intentions to purchase intelligent robotic products and services in Taiwan using the theory of planned behaviour.

BMC psychology
BACKGROUND: The literature for assessing online and offline shopping behaviours that are linked to intelligent robotic goods and services is inadequate. In this study, we applied the Theory of Planned Behaviour model for guidance regarding how consum...

CT-based radiomics of machine-learning to screen high-risk individuals with kidney stones.

Urolithiasis
Screening high-risk populations is crucial for the prevention and treatment of kidney stones. Here, we employed radiomics to screen high-risk patients for kidney stones. A total of 513 independent kidneys from our hospital between 2020 and 2022 were ...

Detection of Parkinson disease using multiclass machine learning approach.

Scientific reports
Parkinson's Disease (PD) is a prevalent neurological condition characterized by motor and cognitive impairments, typically manifesting around the age of 50 and presenting symptoms such as gait difficulties and speech impairments. Although a cure rema...

Predictive model for assessing malnutrition in elderly hospitalized cancer patients: A machine learning approach.

Geriatric nursing (New York, N.Y.)
BACKGROUND: Malnutrition is prevalent among elderly cancer patients. This study aims to develop a predictive model for malnutrition in hospitalized elderly cancer patients.

Predicting Dental General Anesthesia Use among Children with Behavioral Health Conditions.

JDR clinical and translational research
OBJECTIVES: To evaluate how different data sources affect the performance of machine learning algorithms that predict dental general anesthesia use among children with behavioral health conditions.

A Data-Driven Approach to Predicting Recreational Activity Participation Using Machine Learning.

Research quarterly for exercise and sport
With the popularity of recreational activities, the study aimed to develop prediction models for recreational activity participation and explore the key factors affecting participation in recreational activities. A total of 12,712 participants, exc...

Analysis of convolutional neural networks for fronto-temporal dementia biomarker discovery.

International journal of computer assisted radiology and surgery
PURPOSE: Frontotemporal lobe dementia (FTD) results from the degeneration of the frontal and temporal lobes. It can manifest in several different ways, leading to the definition of variants characterised by their distinctive symptomatologies. As thes...