AIMC Topic: Aged, 80 and over
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Traditional Methods Hold Their Ground Against Machine Learning in Predicting Potentially Inappropriate Medication Use in Older Adults.
Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
Jul 6, 2024
OBJECTIVES: Machine learning methods have gained much attention in health sciences for predicting various health outcomes but are scarcely used in pharmacoepidemiology. The ability to identify predictors of suboptimal medication use is essential for ...
Deep learning prediction of survival in patients with heart failure using chest radiographs.
The international journal of cardiovascular imaging
Jul 5, 2024
Heart failure (HF) is associated with high rates of morbidity and mortality. The value of deep learning survival prediction models using chest radiographs in patients with heart failure is currently unclear. The aim of our study is to develop and val...
Image quality of whole-body diffusion MR images comparing deep-learning accelerated and conventional sequences.
European radiology
Jul 4, 2024
OBJECTIVES: To compare the image quality of deep learning accelerated whole-body (WB) with conventional diffusion sequences.
AI-based differential diagnosis of dementia etiologies on multimodal data.
Nature medicine
Jul 4, 2024
Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an artificial intelligence (AI) model that har...
Risk factors for prediabetes in community-dwelling adults: A generalized estimating equation logistic regression approach with natural language processing insights.
Research in nursing & health
Jul 3, 2024
The global prevalence of prediabetes is expected to reach 8.3% (587 million people) by 2045, with 70% of people with prediabetes developing diabetes during their lifetimes. We aimed to classify community-dwelling adults with a high risk for prediabet...
Predictive modeling for identification of older adults with high utilization of health and social services.
Scandinavian journal of primary health care
Jul 3, 2024
AIM: Machine learning techniques have demonstrated success in predictive modeling across various clinical cases. However, few studies have considered predicting the use of multisectoral health and social services among older adults. This research aim...
Predicting Alzheimer's disease from cognitive footprints in mid and late life: How much can register data and machine learning help?
International journal of medical informatics
Jul 3, 2024
BACKGROUND: Real-world data with decades-long medical records are increasingly available alongside the growing adoption of machine learning in healthcare research. We evaluated the performance of machine learning models in predicting the risk of Alzh...
Super-resolution deep learning reconstruction approach for enhanced visualization in lumbar spine MR bone imaging.
European journal of radiology
Jul 3, 2024
OBJECTIVES: This study aims to assess the effectiveness of super-resolution deep-learning-based reconstruction (SR-DLR), which leverages k-space data, on the image quality of lumbar spine magnetic resonance (MR) bone imaging using a 3D multi-echo in-...
An artificial intelligence system for comprehensive pathologic outcome prediction in early gastric cancer through endoscopic image analysis (with video).
Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
Jul 2, 2024
BACKGROUND: Accurate prediction of pathologic results for early gastric cancer (EGC) based on endoscopic findings is essential in deciding between endoscopic and surgical resection. This study aimed to develop an artificial intelligence (AI) model to...
Predicting recovery following stroke: Deep learning, multimodal data and feature selection using explainable AI.
NeuroImage. Clinical
Jul 2, 2024
Machine learning offers great potential for automated prediction of post-stroke symptoms and their response to rehabilitation. Major challenges for this endeavour include the very high dimensionality of neuroimaging data, the relatively small size of...
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