AIMC Topic: Middle Aged

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Application of Artificial Intelligence Software to Identify Emotions of Lung Cancer Patients in Preoperative Health Education: A Cross-Sectional Study.

Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing
AIM(S): To determine the correlation between preoperative health education and the emotions of lung cancer patients, artificial intelligence software was used.

Recurrence patterns and prediction of survival after recurrence for gallbladder cancer.

Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract
BACKGROUND: Gallbladder cancer (GBC) is associated with a poor prognosis. Recurrence patterns and their effect on survival remain ill-defined. This study aimed to analyze recurrence patterns and develop a machine learning (ML) model to predict surviv...

Supervised machine learning compared to large language models for identifying functional seizures from medical records.

Epilepsia
OBJECTIVE: The Functional Seizures Likelihood Score (FSLS) is a supervised machine learning-based diagnostic score that was developed to differentiate functional seizures (FS) from epileptic seizures (ES). In contrast to this targeted approach, large...

Robot-Assisted Approach to Diabetes Care Consultations: Enhancing Patient Engagement and Identifying Therapeutic Issues.

Medicina (Kaunas, Lithuania)
: Diabetes is a rapidly increasing global health challenge compounded by a critical shortage of diabetes care and education specialists. Robot-assisted diabetes care offers a cost-effective and scalable alternative to traditional methods such as trai...

Identification of 17 novel epigenetic biomarkers associated with anxiety disorders using differential methylation analysis followed by machine learning-based validation.

Clinical epigenetics
BACKGROUND: The changes in DNA methylation patterns may reflect both physical and mental well-being, the latter being a relatively unexplored avenue in terms of clinical utility for psychiatric disorders. In this study, our objective was to identify ...

Prediction of depressive disorder using machine learning approaches: findings from the NHANES.

BMC medical informatics and decision making
BACKGROUND: Depressive disorder, particularly major depressive disorder (MDD), significantly impact individuals and society. Traditional analysis methods often suffer from subjectivity and may not capture complex, non-linear relationships between ris...

Comparative analysis of intestinal tumor segmentation in PET CT scans using organ based and whole body deep learning.

BMC medical imaging
BACKGROUND: 18-Fluoro-deoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) is a valuable imaging tool widely used in the management of cancer patients. Deep learning models excel at segmenting highly metabolic tumors but face ch...

An effective multi-step feature selection framework for clinical outcome prediction using electronic medical records.

BMC medical informatics and decision making
BACKGROUND: Identifying key variables is essential for developing clinical outcome prediction models based on high-dimensional electronic medical records (EMR). However, despite the abundance of feature selection (FS) methods available, challenges re...

A predictive model for recurrence in patients with borderline ovarian tumor based on neural multi-task logistic regression.

BMC cancer
BACKGROUND: Effective management of patients with borderline ovarian tumor (BOT) requires the timely identification of those at a higher risk of recurrence. Artificial neural networks have been successfully used in many areas of clinical event predic...

Prediction of tuberculosis treatment outcomes using biochemical makers with machine learning.

BMC infectious diseases
BACKGROUND: Tuberculosis (TB) continues to pose a significant threat to global public health. Enhancing patient prognosis is essential for alleviating the disease burden.