AIMC Topic: Dementia
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Applying Data Mining to Predict Perceived Benefits Risks of Robotics at Home for Dementia Caregiving Among African American Families.
Studies in health technology and informatics
Apr 8, 2025
We used data mining to predict the attitudes of 527 caregivers towards the pros and cons of using robotics and artificial intelligence (AI) for dementia care in African American families, with a focus on family-level factors. African American family ...
Comparing Emotional Valence from Human Quantitative Ratings and Qualitative Narrative Data on Using Artificial Intelligence to Reduce Caregiving Disparity.
Studies in health technology and informatics
Apr 8, 2025
We compared emotional valence scores as determined via machine vs human ratings from a survey conducted from April to May 2024 on perceived attitudes on the use of artificial intelligence (AI) for African American family caregivers of persons with Al...
Predicting explainable dementia types with LLM-aided feature engineering.
Bioinformatics (Oxford, England)
Mar 29, 2025
MOTIVATION: The integration of Machine Learning and Artificial Intelligence (AI) into healthcare has immense potential due to the rapidly growing volume of clinical data. However, existing AI models, particularly Large Language Models (LLMs) like GPT...
A deep-learning retinal aging biomarker for cognitive decline and incident dementia.
Alzheimer's & dementia : the journal of the Alzheimer's Association
Mar 1, 2025
INTRODUCTION: The utility of retinal photography-derived aging biomarkers for predicting cognitive decline remains under-explored.
Sleep efficiency in community-dwelling persons living with dementia: exploratory analysis using machine learning.
Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
Feb 1, 2025
STUDY OBJECTIVES: Sleep disturbances lead to negative health outcomes and caregiver burden, particularly in community settings. This study aimed to investigate a predictive model for sleep efficiency and its associated features in older adults living...
Artificial intelligence and omics-based autoantibody profiling in dementia.
Frontiers in immunology
Jan 1, 2025
INTRODUCTION: Dementia is a neurodegenerative syndrome marked by the accumulation of disease-specific proteins and immune dysregulation, including autoimmune mechanisms involving autoantibodies. Current diagnostic methods are often invasive, time-con...
Comprehensive Machine Learning-Based Prediction Model for Delirium Risk in Older Patients with Dementia: Risk Factors Identification.
Clinical interventions in aging
Jan 1, 2025
BACKGROUND: Delirium superimposed on dementia (DSD) is a severe complication in older adults with dementia, marked by fluctuating cognition, inattention, and altered consciousness. Detection is challenging due to symptom overlap, yet it contributes t...
Uncovering Important Diagnostic Features for Alzheimer's, Parkinson's and Other Dementias Using Interpretable Association Mining Methods.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2025
Alzheimer's Disease and Related Dementias (ADRD) afflict almost 7 million people in the USA alone. The majority of research in ADRD is conducted using post-mortem samples of brain tissue or carefully recruited clinical trial patients. While these res...
Optimizing Neuroprotective Nano-structured Lipid Carriers for Transdermal Delivery through Artificial Neural Network.
Pharmaceutical nanotechnology
Jan 1, 2025
BACKGROUND: Dementia associated with Alzheimer's disease (AD) is a neurological disorder. AD is a progressive neurodegenerative condition that predominantly impacts the elderly population, although it can also manifest in younger people through the i...
Comparing machine learning and deep learning models to predict cognition progression in Parkinson's disease.
Clinical and translational science
Nov 1, 2024
Cognitive decline in Parkinson's disease (PD) varies widely. While models to predict cognitive progression exist, comparing traditional probabilistic models to deep learning methods remains understudied. This study compares sequential modeling techni...
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