AIMC Topic: Aged

Clear Filters Showing 1231 to 1240 of 13246 articles

Leveraging Artificial Intelligence to Uncover Symptom Burden in Palliative Care: Analysis of Nonscheduled Visits Using a Phi-3 Small Language Model.

JCO global oncology
PURPOSE: This study aimed to differentiate nonscheduled visits (NSVs) in an outpatient palliative care setting that are driven by or accompanied by uncontrolled symptoms from those that are administrative or routine, such as prescription refills and ...

Using Real-World Data for Machine-Learning Algorithms to Predict the Treatment Response in Advanced Melanoma: A Pilot Study for Personalizing Cancer Care.

JCO clinical cancer informatics
PURPOSE: The use of real-world data (RWD) in oncology is becoming increasingly important for clinical decision making and tailoring treatment. Despite the significant success of targeted therapy and immunotherapy in advanced melanoma, substantial var...

A semantic segmentation model for automatic precise identification of pituitary microadenomas with preoperative MRI.

Neuroradiology
PURPOSE: Magnetic resonance imaging (MRI) is an essential technique for diagnosing pituitary adenomas; however, it is also challenging for neurosurgeons to use it to precisely identify some types of microadenomas. A novel neural network model was dev...

DEMENTIA: A Hybrid Attention-Based Multimodal and Multi-Task Learning Framework With Expert Knowledge for Alzheimer's Disease Assessment From Speech.

IEEE journal of biomedical and health informatics
The prevalence of Alzheimer's disease (AD) is rising annually, imposing a severe burden on patients and society. Therefore, assisted AD assessment is crucial. The decline in language function and the cognitive impairment it reflects are key external ...

LGG-NeXt: A Next Generation CNN and Transformer Hybrid Model for the Diagnosis of Alzheimer's Disease Using 2D Structural MRI.

IEEE journal of biomedical and health informatics
Incurable Alzheimer's disease (AD) plagues many elderly people and families. It is important to accurately diagnose and predict it at an early stage. However, the existing methods have shortcomings, such as inability to learn local and global informa...

Deep Geometric Learning With Monotonicity Constraints for Alzheimer's Disease Progression.

IEEE transactions on neural networks and learning systems
Alzheimer's disease (AD) is a devastating neurodegenerative condition that precedes progressive and irreversible dementia; thus, predicting its progression over time is vital for clinical diagnosis and treatment. For this, numerous studies have imple...

Machine learning based association between inflammation indicators (NLR, PLR, NPAR, SII, SIRI, and AISI) and all-cause mortality in arthritis patients with hypertension: NHANES 1999-2018.

Frontiers in public health
BACKGROUND: This study aimed to evaluate the relationship between CBC-derived inflammatory markers (NLR, PLR, NPAR, SII, SIRI, and AISI) and all-cause mortality (ACM) risk in arthritis (AR) patients with hypertensive (HTN) using data from the NHANES.

Direct-acting antivirals (DAA) positively affect depression and cognitive function in patients with chronic hepatitis C.

PloS one
The aim of the study was to determine how depression and cognitive dysfunction in patients with chronic hepatitis C virus (HCV) infection are affected by treatment with direct-acting antivirals (DAA). Fifty-two chronic hepatitis C patients underwent ...