Dementia

Using Natural Language Processing to Identify Home Health Care Patients at Risk for Diagnosis of Alzheimer's Disease and Related Dementias.

This study aimed to: (1) validate a natural language processing (NLP) system developed for the home health care setting to identify signs and symptoms of Alzheimer's disease and related dementias (ADRD) documented in clinicians' free-text notes; (2) determine whether signs and symptoms detected via NLP help to identify patients at risk of a new ADRD diagnosis within four years after admission. Thi...

Inferring Parameters of Pyramidal Neuron Excitability in Mouse Models of Alzheimer's Disease Using Biophysical Modeling and Deep Learning.

Alzheimer's disease (AD) is believed to occur when abnormal amounts of the proteins amyloid beta and tau aggregate in the brain, resulting in a progressive loss of neuronal function. Hippocampal neurons in transgenic mice with amyloidopathy or tauopathy exhibit altered intrinsic excitability properties. We used deep hybrid modeling (DeepHM), a recently developed parameter inference technique that ...

Comparing a pre-defined versus deep learning approach for extracting brain atrophy patterns to predict cognitive decline due to Alzheimer's disease in patients with mild cognitive symptoms.

BACKGROUND: Predicting future Alzheimer's disease (AD)-related cognitive decline among individuals with subjective cognitive decline (SCD) or mild cog...

Age-related changes in human brain functional connectivity using graph theory and machine learning techniques in resting-state fMRI data.

Aging is the basis of neurodegeneration and dementia that affects each endemic in the body. Normal aging in the brain is associated with progressive s...

Utilizing Siamese 4D-AlzNet and Transfer Learning to Identify Stages of Alzheimer's Disease.

Alzheimer's disease (AD) is the general form of dementia, leading to a progressive neurological disorder characterized by memory loss due to brain cel...

Can an AI-carebot be filial? Reflections from Confucian ethics.

This article discusses the application of artificially intelligent robots within eldercare and explores a series of ethical considerations, including ...

Socially Assistive Robots for patients with Alzheimer's Disease: A scoping review.

BACKGROUND: The most common form of dementia, Alzheimer's Disease (AD), is challenging for both those affected as well as for their care providers, an...

Facilitators and barriers to codesigning social robots with older adults with dementia: a scoping review protocol.

INTRODUCTION: Social robots including telepresence robots have emerged as potential support in dementia care. However, the effectiveness of these robo...

Presynaptic Dopaminergic Imaging Characterizes Patients with REM Sleep Behavior Disorder Due to Synucleinopathy.

OBJECTIVE: To apply a machine learning analysis to clinical and presynaptic dopaminergic imaging data of patients with rapid eye movement (REM) sleep ...

Predicting long-term progression of Alzheimer's disease using a multimodal deep learning model incorporating interaction effects.

BACKGROUND: Identifying individuals with mild cognitive impairment (MCI) at risk of progressing to Alzheimer's disease (AD) provides a unique opportun...

Identifying Protein Phosphorylation Site-Disease Associations Based on Multi-Similarity Fusion and Negative Sample Selection by Convolutional Neural Network.

As one of the most important post-translational modifications (PTMs), protein phosphorylation plays a key role in a variety of biological processes. M...

Deep Learning-Driven Exploration of Pyrroloquinoline Quinone Neuroprotective Activity in Alzheimer's Disease.

Alzheimer's disease (AD) is a pressing concern in neurodegenerative research. To address the challenges in AD drug development, especially those targe...

Patient portal messages to support an age-friendly health system for persons with dementia.

BACKGROUND: Patient portal secure messaging can support age-friendly dementia care, yet little is known about care partner use of the portal and how m...

miRNAs in cerebrospinal fluid associated with Alzheimer's disease: A systematic review and pathway analysis using a data mining and machine learning approach.

Alzheimer's disease (AD) is the most common type and accounts for 60%-70% of the reported cases of dementia. MicroRNAs (miRNAs) are small non-coding R...

Robotic Care Equipment Improves Communication between Care Recipient and Caregiver in a Nursing Home as Revealed by Gaze Analysis: A Case Study.

The use of robotic nursing care equipment is an important option for solving the shortage of nursing care personnel, but the effects of its introducti...

A multimodal deep learning approach for the prediction of cognitive decline and its effectiveness in clinical trials for Alzheimer's disease.

Alzheimer's disease is one of the most important health-care challenges in the world. For decades, numerous efforts have been made to develop therapeu...

Predictive deep learning models for cognitive risk using accessible data.

The early detection of mild cognitive impairment (MCI) is crucial to preventing the progression of dementia. However, it necessitates that patients vo...

Ranking and filtering of neuropathology features in the machine learning evaluation of dementia studies.

Early diagnosis of dementia diseases, such as Alzheimer's disease, is difficult because of the time and resources needed to perform neuropsychological...

Greater accuracy of radiomics compared to deep learning to discriminate normal subjects from patients with dementia: a whole brain 18FDG PET analysis.

METHODS: 18F-FDG brain PET and clinical score were collected in 85 patients with dementia and 125 healthy controls (HC). Patients were assigned to var...

Intelligent decision support systems for dementia care: A scoping review.

In the context of dementia care, Artificial Intelligence (AI) powered clinical decision support systems have the potential to enhance diagnosis and ma...