AIMC Topic: Young Adult

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Ethical aspects and user preferences in applying machine learning to adjust eHealth addressing substance use: A mixed-methods study.

International journal of medical informatics
BACKGROUND: Digital health interventions targeting substance use disorders are being increasingly implemented. Data science methodology has the potential to enhance involvement and efficacy of these interventions, though application may raise ethical...

Exploring artificial intelligence (AI) Chatbot usage behaviors and their association with mental health outcomes in Chinese university students.

Journal of affective disorders
Technology dependence has long been a critical public health issue, especially among young people. With the development of AI chatbots, many individuals are integrating these tools into their daily lives. However, we have limited knowledge about the ...

Construction and verification of risk prediction model for suicidal attempts of mood disorder based on machine learning.

Journal of affective disorders
BACKGROUND: Mood disorders (MD) are closely related to suicide attempt (SA). Developing an effective prediction model for SA in MD patients could play a crucial role in the early identification of high-risk groups.

A neural approach to the Turing Test: The role of emotions.

Neural networks : the official journal of the International Neural Network Society
As is well known, the Turing Test proposes the possibility of distinguishing the behavior of a machine from that of a human being through an experimental session. The Turing Test assesses whether a person asking questions to two different entities, c...

Blood-based proteomic profiling identifies OSMR as a novel biomarker of AML outcomes.

Blood
Inflammation is increasingly recognized as a critical factor in acute myeloid leukemia (AML) pathogenesis. We performed blood-based proteomic profiling of 251 inflammatory proteins in 543 patients with newly diagnosed AML. Using a machine learning mo...

Representation of locomotive action affordances in human behavior, brains, and deep neural networks.

Proceedings of the National Academy of Sciences of the United States of America
To decide how to move around the world, we must determine which locomotive actions (e.g., walking, swimming, or climbing) are afforded by the immediate visual environment. The neural basis of our ability to recognize locomotive affordances is unknown...

Microbial dysbiosis and its diagnostic potential in androgenetic alopecia: insights from multi-kingdom sequencing and machine learning.

mSystems
Androgenetic alopecia (AGA), the most common form of hair loss, has been linked to dysbiosis of the scalp microbiome. In this study, we collected microbiome samples from the frontal baldness and occipital regions of patients with varying stages of AG...

Machine Learning Prediction of Pancreatitis Risk With Antithyroid Drugs: A Nationwide Retrospective Observational Study.

The Journal of clinical endocrinology and metabolism
BACKGROUND: In recent years, there has been increasing data showing that the risk of acute pancreatitis (AP) is increased in patients using methimazole (MMI). The aim of this population-based study was to investigate the association between drugs use...

OCT in dermatology: a process for determining whether a fully diversified dataset is needed for AI model-building.

Optics letters
Optical coherence tomography (OCT) has sufficient depth penetration for detection of skin pathologies, but its detection effectiveness can be aided by the assistance of artificial intelligence (AI) modeling. AI model-building identifies pathologies b...

Clinical correlations of plasma sphingosine-1-phosphate and sphingolipid key enzymes in severe dengue using laboratory and machine learning approach.

Clinica chimica acta; international journal of clinical chemistry
BACKGROUND: Sphingolipids are crucial for vascular integrity and cellular homeostasis, with recent studies highlighting their role in viral diseases.