Alzheimer's Disease

Alzheimer's disease detection based on large language model prompt engineering

In light of the growing proportion of older individuals in our society, the timely diagnosis of Alzheimer's disease has become a crucial aspect of healthcare. In this paper, we propose a non-invasive and cost-effective detection method based on speech technology. The method employs a pre-trained language model in conjunction with techniques such as prompt fine-tuning and conditional learning, th...

The translational power of Alzheimer's-based organoid models in personalized medicine: an integrated biological and digital approach embodying patient clinical history.

Alzheimer's disease (AD) is a complex neurodegenerative condition characterized by a multifaceted interplay of genetic, environmental, and pathological factors. Traditional diagnostic and research methods, including neuropsychological assessments, imaging, and cerebrospinal fluid (CSF) biomarkers, have advanced our understanding but remain limited by late-stage detection and challenges in modeling...

Predicting brain age for veterans with traumatic brain injuries and healthy controls: an exploratory analysis.

BACKGROUND: Traumatic brain injury (TBI) is associated with increased dementia risk. This may be driven by underlying biological changes resulting fro...

Identification and validation of pyroptosis-related genes in Alzheimer's disease based on multi-transcriptome and machine learning.

BACKGROUND: Alzheimer's disease (AD) progression is characterized by persistent neuroinflammation, where pyroptosis-an inflammatory programmed cell de...

Nanocarrier-based targeted drug delivery for Alzheimer's disease: addressing neuroinflammation and enhancing clinical translation.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, amyloid-beta (Aβ) aggregation, tau pathology,...

Artificial intelligence based advancements in nanomedicine for brain disorder management: an updated narrative review.

Nanomedicines are nanoscale, biocompatible materials that offer promising alternatives to conventional treatment options for brain disorders. The rece...

Fully automated MRI-based analysis of the locus coeruleus in aging and Alzheimer's disease dementia using ELSI-Net.

INTRODUCTION: The locus coeruleus (LC) is linked to the development and pathophysiology of neurodegenerative diseases such as Alzheimer's disease (AD)...

Association of RDoC dimensions with brain transcriptional profiles in Alzheimer's disease.

INTRODUCTION: Neuropsychiatric symptoms are common in people with Alzheimer's disease (AD) across all severity stages. Their heterogeneous presentatio...

Comprehensive Machine Learning-Based Prediction Model for Delirium Risk in Older Patients with Dementia: Risk Factors Identification.

BACKGROUND: Delirium superimposed on dementia (DSD) is a severe complication in older adults with dementia, marked by fluctuating cognition, inattenti...

Artificial intelligence and omics-based autoantibody profiling in dementia.

INTRODUCTION: Dementia is a neurodegenerative syndrome marked by the accumulation of disease-specific proteins and immune dysregulation, including aut...

Leveraging transformers and explainable AI for Alzheimer's disease interpretability.

Alzheimer's disease (AD) is a progressive brain ailment that causes memory loss, cognitive decline, and behavioral changes. It is quite concerning tha...

Optimizing Neuroprotective Nano-structured Lipid Carriers for Transdermal Delivery through Artificial Neural Network.

BACKGROUND: Dementia associated with Alzheimer's disease (AD) is a neurological disorder. AD is a progressive neurodegenerative condition that predomi...

A Comprehensive Review on Deep Learning Techniques in Alzheimer's Disease Diagnosis.

Alzheimer's Disease (AD) is a serious neurological illness that causes memory loss gradually by destroying brain cells. This deadly brain illness prim...

Mini-mental status examination phenotyping for Alzheimer's disease patients using both structured and narrative electronic health record features.

OBJECTIVE: This study aims to automate the prediction of Mini-Mental State Examination (MMSE) scores, a widely adopted standard for cognitive assessme...

Machine learning-based prediction models in medical decision-making in kidney disease: patient, caregiver, and clinician perspectives on trust and appropriate use.

OBJECTIVES: This study aims to improve the ethical use of machine learning (ML)-based clinical prediction models (CPMs) in shared decision-making for ...

Combining Real-Time Neuroimaging With Machine Learning to Study Attention to Familiar Faces During Infancy: A Proof of Principle Study.

Looking at caregivers' faces is important for early social development, and there is a concomitant increase in neural correlates of attention to famil...

Biologically Enhanced Machine Learning Model to uncover Novel Gene-Drug Targets for Alzheimer's Disease.

Given the complexity and multifactorial nature of Alzheimer's disease, investigating potential drug-gene targets is imperative for developing effectiv...

Uncovering Important Diagnostic Features for Alzheimer's, Parkinson's and Other Dementias Using Interpretable Association Mining Methods.

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...

A Dynamic Model for Early Prediction of Alzheimer's Disease by Leveraging Graph Convolutional Networks and Tensor Algebra.

Alzheimer's disease (AD) is a neurocognitive disorder that deteriorates memory and impairs cognitive functions. Mild Cognitive Impairment (MCI) is gen...

A Machine Learning Model to Harmonize Volumetric Brain MRI Data for Quantitative Neuroradiologic Assessment of Alzheimer Disease.

Purpose To extend a previously developed machine learning algorithm for harmonizing brain volumetric data of individuals undergoing neuroradiologic as...