AIMC Topic: Aged, 80 and over
Clear Filters Showing 471 to 480 of 3430 articles
Random survival forest model for early prediction of Alzheimer's disease conversion in early and late Mild cognitive impairment stages.
PloS one
Dec 13, 2024
With a clinical trial failure rate of 99.6% for Alzheimer's Disease (AD), early diagnosis is critical. Machine learning (ML) models have shown promising results in early AD prediction, with survival ML models outperforming typical classifiers by prov...
Blood Biomarker Signatures for Slow Gait Speed in Older Adults: An Explainable Machine Learning Approach.
Brain, behavior, and immunity
Dec 12, 2024
Maintaining physical function is crucial for independent living in older adults, with gait speed being a key predictor of health outcomes. Blood biomarkers may potentially monitor older adults' mobility, yet their association with slow gait speed sti...
Prediction of stroke-associated hospital-acquired pneumonia: Machine learning approach.
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Dec 12, 2024
BACKGROUND: Stroke-associated Hospital Acquired Pneumonia (HAP) significantly impacts patient outcomes. This study explores the utility of machine learning models in predicting HAP in stroke patients, leveraging national registry data and SHapley Add...
Smartphone pupillometry with machine learning differentiates ischemic from hemorrhagic stroke: A pilot study.
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Dec 12, 2024
OBJECTIVES: Similarities between acute ischemic and hemorrhagic stroke make diagnosis and triage challenging. We studied a smartphone-based quantitative pupillometer for differentiation of acute ischemic and hemorrhagic stroke.
Screening for Depression and Anxiety Using a Nonverbal Working Memory Task in a Sample of Older Brazilians: Observational Study of Preliminary Artificial Intelligence Model Transferability.
JMIR formative research
Dec 12, 2024
BACKGROUND: Anxiety and depression represent prevalent yet frequently undetected mental health concerns within the older population. The challenge of identifying these conditions presents an opportunity for artificial intelligence (AI)-driven, remote...
Retinal fluid quantification using a novel deep learning algorithm in patients treated with faricimab in the TRUCKEE study.
Eye (London, England)
Dec 11, 2024
BACKGROUND: Investigate retinal fluid changes via a novel deep-learning algorithm in real-world patients receiving faricimab for the treatment of neovascular age-related macular degeneration (nAMD).
Residual risk prediction in anticoagulated patients with atrial fibrillation using machine learning: A report from the GLORIA-AF registry phase II/III.
European journal of clinical investigation
Dec 11, 2024
BACKGROUND: Although oral anticoagulation decreases the risk of thromboembolism in patients with atrial fibrillation (AF), a residual risk of thrombotic events still exists. This study aimed to construct machine learning (ML) models to predict the re...
Aged
Aged, 80 and over
Male
Middle Aged
Risk Assessment
Humans
Machine Learning
Algorithms
Female
Risk Factors
Atrial Fibrillation
Ischemic Attack, Transient
Thromboembolism
Registries
ROC Curve
Stroke
Logistic Models
Myocardial Infarction
Ischemic Stroke
Thrombosis
Area Under Curve
Administration, Oral
Anticoagulants
Breath-hold diffusion-weighted MR imaging (DWI) using deep learning reconstruction: Comparison with navigator triggered DWI in patients with malignant liver tumors.
Radiography (London, England : 1995)
Dec 11, 2024
INTRODUCTION: This study investigated the feasibility of single breath-hold (BH) diffusion-weighted MR imaging (DWI) using deep learning reconstruction (DLR) compared to navigator triggered (NT) DWI in patients with malignant liver tumors.
Early Detection of Dementia in Populations With Type 2 Diabetes: Predictive Analytics Using Machine Learning Approach.
Journal of medical Internet research
Dec 11, 2024
BACKGROUND: The possible association between diabetes mellitus and dementia has raised concerns, given the observed coincidental occurrences.
Machine Learning Reveals Demographic Disparities in Palliative Care Timing Among Patients With Traumatic Brain Injury Receiving Neurosurgical Consultation.
Neurocritical care
Dec 10, 2024
BACKGROUND: Timely palliative care (PC) consultations offer demonstrable benefits for patients with traumatic brain injury (TBI), yet their implementation remains inconsistent. This study employs machine learning methods to identify distinct patient ...
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