Dementia

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 that one in nine adults over the age of 65 have AD. Currently there is almost no cure for AD except very few experimental treatments. However, early detection offers chances to take part in clinical trials or other investigations looking at potential ne...

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 predominantly impacts the elderly population, although it can also manifest in younger people through the impairment of cognitive functions, such as memory, cognition, and behaviour. Donepezil HCl and Memantine HCl are encapsulated in Nanostructured Lipid C...

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...

A Multi-Label Deep Learning Model for Detailed Classification of Alzheimer's Disease.

BACKGROUND: Accurate diagnosis and classification of Alzheimer's disease (AD) are crucial for effective treatment and management. Traditional diagnost...

Creating Chemiluminescence Signature Arrays Coupled with Machine Learning for Alzheimer's Disease Serum Diagnosis.

Although omics and multi-omics approaches are the most used methods to create signature arrays for liquid biopsy, the high cost of omics technologies ...

Dynamic and concordance-assisted learning for risk stratification with application to Alzheimer's disease.

Dynamic prediction models capable of retaining accuracy by evolving over time could play a significant role for monitoring disease progression in clin...

Leveraging Self-Training and Variational Autoencoder for Agitation Detection in People with Dementia Using Wearable Sensors

Dementia is a neurodegenerative disorder that has been growing among elder people over the past decades. This growth profoundly impacts the quality ...

AlzheimerRAG: Multimodal Retrieval Augmented Generation for PubMed articles

Recent advancements in generative AI have flourished the development of highly adept Large Language Models (LLMs) that integrate diverse data types ...

Linguistic Features Extracted by GPT-4 Improve Alzheimer's Disease Detection based on Spontaneous Speech

Alzheimer's Disease (AD) is a significant and growing public health concern. Investigating alterations in speech and language patterns offers a prom...

Accurate and Efficient Algorithm for Detection of Alzheimer Disability Based on Deep Learning.

BACKGROUND/AIMS: Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that severely affects cognitive functions and memory. Early dete...

A Mapper Algorithm with implicit intervals and its optimization

The Mapper algorithm is an essential tool for visualizing complex, high dimensional data in topology data analysis (TDA) and has been widely used in...

segcsvd: A Convolutional Neural Network-Based Tool for Quantifying White Matter Hyperintensities in Heterogeneous Patient Cohorts.

White matter hyperintensities (WMH) of presumed vascular origin are a magnetic resonance imaging (MRI)-based biomarker of cerebral small vessel diseas...

The effectiveness of care robots in alleviating physical burden and pain for caregivers: Non-randomized prospective interventional study - Preliminary study.

BACKGROUND: Caregiver burden significantly affects both patients and caregivers but is often overlooked in clinical practice. Physical and emotional s...