AIMC Topic: Adult

Clear Filters Showing 3891 to 3900 of 15606 articles

AI model using CT-based imaging biomarkers to predict hepatocellular carcinoma in patients with chronic hepatitis B.

Journal of hepatology
BACKGROUND & AIMS: Various hepatocellular carcinoma (HCC) prediction models have been proposed for patients with chronic hepatitis B (CHB) using clinical variables. We aimed to develop an artificial intelligence (AI)-based HCC prediction model by inc...

Pre-trained artificial intelligence language model represents pragmatic language variability central to autism and genetically related phenotypes.

Autism : the international journal of research and practice
Many individuals with autism experience challenges using language in social contexts (i.e., pragmatic language). Characterizing and understanding pragmatic variability is important to inform intervention strategies and the etiology of communication c...

Neural networks for predicting etiological diagnosis of uveitis.

Eye (London, England)
BACKGROUND/OBJECTIVES: The large number and heterogeneity of causes of uveitis make the etiological diagnosis a complex task. The clinician must consider all the information concerning the ophthalmological and extra-ophthalmological features of the p...

Machine learning based radiomics model to predict radiotherapy induced cardiotoxicity in breast cancer.

Journal of applied clinical medical physics
PURPOSE: Cardiotoxicity is one of the major concerns in breast cancer treatment, significantly affecting patient outcomes. To improve the likelihood of favorable outcomes for breast cancer survivors, it is essential to carefully balance the potential...

Effectiveness of Machine Learning-Based Adjustments to an eHealth Intervention Targeting Mild Alcohol Use.

European addiction research
INTRODUCTION: This study aimed to evaluate effects of three machine learning based adjustments made to an eHealth intervention for mild alcohol use disorder, regarding (a) early dropout, (b) participation duration, and (c) success in reaching persona...

Automated feature selection for early keratoconus screening optimization.

Biomedical physics & engineering express
In this paper, an automated feature selection (FS) method is presented to optimize machine learning (ML) models' performances, enhancing early keratoconus screening. A total of 448 parameters were analyzed from a dataset comprising 3162 observations ...

Efficient analysis of drug interactions in liver injury: a retrospective study leveraging natural language processing and machine learning.

BMC medical research methodology
BACKGROUND: Liver injury from drug-drug interactions (DDIs), notably with anti-tuberculosis drugs such as isoniazid, poses a significant safety concern. Electronic medical records contain comprehensive clinical information and have gained increasing ...