AIMC Topic: Humans

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Application of Machine Learning and Deep Neural Visual Features for Predicting Adult Obesity Prevalence in Missouri.

International journal of environmental research and public health
This research study investigates and predicts the obesity prevalence in Missouri, utilizing deep neural visual features extracted from medium-resolution satellite imagery (Sentinel-2). By applying a deep convolutional neural network (DCNN), the study...

A Transcriptomics-Based Machine Learning Model Discriminating Mild Cognitive Impairment and the Prediction of Conversion to Alzheimer's Disease.

Cells
The clinical spectrum of Alzheimer's disease (AD) ranges dynamically from asymptomatic and mild cognitive impairment (MCI) to mild, moderate, or severe AD. Although a few disease-modifying treatments, such as lecanemab and donanemab, have been develo...

Anomaly-based threat detection in smart health using machine learning.

BMC medical informatics and decision making
BACKGROUND: Anomaly detection is crucial in healthcare data due to challenges associated with the integration of smart technologies and healthcare. Anomaly in electronic health record can be associated with an insider trying to access and manipulate ...

Comment about 'Medical, dental, and nursing students' attitudes and knowledge towards artificial intelligence: a systematic review and meta-analysis'.

BMC medical education
We read with great interest the recently published article by Amiri et al., titled "Medical, Dental, and Nursing Students' Attitudes and Knowledge Toward Artificial Intelligence: A Systematic Review and Meta-Analysis." We would like to offer comments...

DAPNet: multi-view graph contrastive network incorporating disease clinical and molecular associations for disease progression prediction.

BMC medical informatics and decision making
BACKGROUND: Timely and accurate prediction of disease progress is crucial for facilitating early intervention and treatment for various chronic diseases. However, due to the complicated and longitudinal nature of disease progression, the capacity and...

Enhancing puncture skills training with generative AI and digital technologies: a parallel cohort study.

BMC medical education
BACKGROUND: Traditional puncture skills training for refresher doctors faces limitations in effectiveness and efficiency. This study explored the application of generative AI (ChatGPT), templates, and digital imaging to enhance puncture skills traini...

Analysis and prediction of infectious diseases based on spatial visualization and machine learning.

Scientific reports
Infectious diseases are a global public health problem that poses a threat to human society. Since the 1970s, constantly mutated new infectious viruses have been quietly attacking humanity, and at least one new type of infectious disease is discovere...

Application of social media communication for museum based on the deep mediatization and artificial intelligence.

Scientific reports
Based on deep mediatization theory and artificial intelligence (AI) technology, this study explores the effective improvement of museums' social media communication by applying Convolutional Neural Network (CNN) technology. Firstly, the social media ...

XAI-driven CatBoost multi-layer perceptron neural network for analyzing breast cancer.

Scientific reports
Early diagnosis of breast cancer is exceptionally important in signifying the treatment results, of women's health. The present study outlines a novel approach for analyzing breast cancer data by using the CatBoost classification model with a multi-l...

Convolutional neural network for oral cancer detection combined with improved tunicate swarm algorithm to detect oral cancer.

Scientific reports
Early Diagnosis of oral cancer is very important and can save you from some oral malignancies. However, while this approach aids in the rapid healing of patients and the preservation of their lives, there are several causes for poor and wrong diagnos...