AIMC Topic: Middle Aged

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Beyond total scores: Enhancing psychotherapy outcome prediction with item-level scores.

Journal of consulting and clinical psychology
OBJECTIVE: This study aims at improving dropout and treatment nonresponse prevention by optimizing the performance of models for their prediction through the integration of item-level data.

Clinical advantages in providing artificial intelligence-assisted prostate cancer diagnosis: A pilot study.

Pathology, research and practice
Prostate cancer is a prevalent male malignancy, with increasing incidence rates placing significant diagnostic burdens on pathology services worldwide. Artificial intelligence (AI) is emerging as a promising aid in enhancing diagnostic efficiency and...

AI-driven simplification of surgical reports in gynecologic oncology: A potential tool for patient education.

Acta obstetricia et gynecologica Scandinavica
INTRODUCTION: The emergence of large language models heralds a new chapter in natural language processing, with immense potential for improving medical care and especially medical oncology. One recent and publicly available example is Generative Pret...

Vedolizumab in inflammatory bowel disease: Real-world outcomes and their prediction with machine learning-the IG-IBD LIVE study.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
BACKGROUND AND AIMS: Real-world studies on vedolizumab in inflammatory bowel disease (IBD) are often limited by small sample size and short follow-up. In this study, we investigated the 2-year effectiveness and safety of vedolizumab in patients with ...

A lightweight graph neural network to predict long-term mortality in coronary artery disease patients: an interpretable causality-aware approach.

Journal of biomedical informatics
BACKGROUND: Coronary artery disease (CAD) causes substantial death toll in the United States and worldwide. While traditional methods for CAD mortality prediction are based on established risk factors, they have significant limitations in accuracy, a...

Prediction of post stroke depression with machine learning: A national multicenter cohort study.

Journal of psychiatric research
OBJECTIVE: Post-stroke depression (PSD) is a common psychiatric complication following stroke, with low clinical detection rates and delayed diagnosis. Most existing PSD prediction models suffer from incomplete data inclusion, which limits their clin...

Impact of spectrum bias on deep learning-based stroke MRI analysis.

European journal of radiology
PURPOSE: To evaluate spectrum bias in stroke MRI analysis by excluding cases with uncertain acute ischemic lesions (AIL) and examining patient, imaging, and lesion factors associated with these cases.

Predicting cognitive function among Chinese community-dwelling older adults: A supervised machine learning approach.

Preventive medicine
OBJECTIVE: Identifying cognitive impairment early enough could support timely intervention of cognitive impairment and facilitate successful cognitive aging. We aimed to build more precise prediction models for cognitive function using less variable ...

Behavioral biases and Fintech adoption: Investigating the role of financial literacy.

Acta psychologica
This paper studies the influence of behavioral biases on Fintech adoption. Additionally, the role of financial literacy in adaptation of Fintech services is evaluated. Primary data from customers in the banking sector is gathered using a structured q...

Novel artificial intelligence approach in neurointerventional practice: Preliminary findings on filter movement and ischemic lesions in carotid artery stenting.

Clinical neurology and neurosurgery
BACKGROUND AND OBJECTIVES: Embolic protection devices (EPDs) used during carotid artery stenting (CAS) are crucial in reducing ischemic complications. Although minimizing the filter-type EPD movement is considered important, limited research has demo...