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A machine learning approach for identifying anatomical biomarkers of early mild cognitive impairment.

PeerJ
BACKGROUND: Alzheimer's Disease (AD) poses a major challenge as a neurodegenerative disorder, and early detection is critical for effective intervention. Magnetic resonance imaging (MRI) is a critical tool in AD research due to its availability and c...

Random survival forest model for early prediction of Alzheimer's disease conversion in early and late Mild cognitive impairment stages.

PloS one
With a clinical trial failure rate of 99.6% for Alzheimer's Disease (AD), early diagnosis is critical. Machine learning (ML) models have shown promising results in early AD prediction, with survival ML models outperforming typical classifiers by prov...

Hybrid SEM-ANN model for predicting undergraduates' e-learning continuance intention based on perceived educational and emotional support.

PloS one
Based on the Expectation Confirmation Model (ECM), this study explores the impact of perceived educational and emotional support on university students' continuance intention to engage in e-learning. Researchers conducted a survey using structured qu...

Can an Algorithm Tell How Spiritual You Are? Using Generative Pretrained Transformers for Sophisticated Forms of Text Analysis.

Journal of personality
OBJECTIVE: Text analysis is a form of psychological assessment that involves converting qualitative information (text) into quantitative data. We tested whether automated text analysis using Generative Pre-trained Transformers (GPTs) can match the "g...

Prediction of mucinous adenocarcinoma in colorectal cancer with mucinous components detected in preoperative biopsy diagnosis.

Abdominal radiology (New York)
OBJECTIVES: Endoscopic biopsy diagnosis for the preoperative assessment of mucinous components in patients with colorectal cancer is limited. This study investigated a radiomics model and established an explainable prediction model by using machine l...

Performance of image processing analysis and a deep convolutional neural network for the classification of oral cancer in fluorescence visualization.

International journal of oral and maxillofacial surgery
The aim of this prospective study was to determine the effectiveness of screening using image processing analysis and a deep convolutional neural network (DCNN) to classify oral cancers using non-invasive fluorescence visualization. The study include...

Medical language matters: impact of clinical summary composition on a generative artificial intelligence's diagnostic accuracy.

Diagnosis (Berlin, Germany)
OBJECTIVES: Evaluate the impact of problem representation (PR) characteristics on Generative Artificial Intelligence (GAI) diagnostic accuracy.

Raising awareness may increase the likelihood of hematopoietic stem cell donation: a nationwide survey using artificial intelligence.

International journal of hematology
BACKGROUND: In Italy, the demand for allogeneic transplantation exceeds the number of compatible donors in the Italian Bone Marrow Donor Registry (IBMDR). This study aimed to explore the knowledge, beliefs, opinions, values, and feelings of the Itali...

Artificial intelligence improves mammography-based breast cancer risk prediction.

Trends in cancer
Artificial intelligence (AI) is enabling us to delve deeply into the information inherent in a mammogram and identify novel features associated with high risk of a future breast cancer diagnosis. Here, we discuss how AI is improving mammographic dens...

People expect artificial moral advisors to be more utilitarian and distrust utilitarian moral advisors.

Cognition
As machines powered by artificial intelligence increase in their technological capacities, there is a growing interest in the theoretical and practical idea of artificial moral advisors (AMAs): systems powered by artificial intelligence that are expl...