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Related Concept Videos

Alzheimer's Disease: Overview01:26

Alzheimer's Disease: Overview

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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
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Dementia is a collective term for cognitive disorders primarily affecting memory, thinking, and reasoning. It is not a specific disease but a syndrome, with Alzheimer's disease being the most common cause, accounting for approximately 60-80% of cases. Other types include vascular dementia, Lewy body dementia, and frontotemporal dementia. Dementia affects millions worldwide, particularly older adults, though it is not a normal part of aging.
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Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...
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Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
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Neurodegenerative disorders are progressive diseases that cause irreversible damage and loss to neurons in specific brain areas. Examples of these disorders include Parkinson's disease, Alzheimer's disease, Multiple Sclerosis (MS), and Amyotrophic Lateral Sclerosis (ALS). These disorders share characteristics such as proteinopathies, selective neuronal vulnerability, and a complex interplay between genetic and environmental factors. The primary therapeutic goal for these conditions is...
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Model-Driven Dementia Prevention and Intervention Platform.

Markus Schinle1, Michael Dietrich1, Simon Stock2

  • 1FZI Research Center for Information Technologies, Germany.

Studies in Health Technology and Informatics
|May 19, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a digital platform using smart devices to monitor dementia risk factors. Graphical models aid in managing personalized treatments, improving clarity but requiring simpler setup methods.

Keywords:
Alzheimer’s diseaseFHIRIoMTdigital preventioninternet of medical thingsmodel-driven intervention planningprecision caretreatment platform

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Area of Science:

  • Gerontology
  • Medical Informatics
  • Digital Health

Background:

  • Dementia, including Alzheimer's disease, is largely incurable, but modifiable risk factors like obesity and hypertension can influence its development.
  • Holistic management of these risk factors offers a strategy for dementia prevention or delaying its progression.
  • Individualized treatment approaches are crucial for effectively managing dementia risk factors.

Purpose of the Study:

  • To present a model-driven digital platform for personalized dementia risk factor management.
  • To enable monitoring of patient biomarkers via Internet of Medical Things (IoMT) devices.
  • To facilitate data interoperability with existing healthcare systems using standards like FHIR.

Main Methods:

  • Development of a model-driven digital platform integrating IoMT devices (e.g., Google Fit, Withings).
  • Utilization of a custom domain-specific language and associated diagram editor for configuring personalized treatment processes.
  • Implementation of a usability study with twelve participants to evaluate the graphical model editor.

Main Results:

  • The digital platform facilitates patient biomarker monitoring and personalized treatment optimization in a patient-in-the-loop manner.
  • Graphical models derived from the domain-specific language enhance system review clarity for healthcare providers.
  • Usability study indicated that while graphical representations improve clarity, wizard-style systems offer easier initial setup.

Conclusions:

  • The developed digital platform shows promise for supporting individualized dementia risk factor management through IoMT data integration.
  • Graphical modeling aids in understanding complex treatment processes but requires further refinement for optimal user experience.
  • Future work should focus on enhancing the ease of setup for such systems to maximize adoption and effectiveness in clinical practice.