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Related Concept Videos

Dementia01:30

Dementia

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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.
The progression of dementia is generally gradual....
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Alzheimer's Disease: Overview01:26

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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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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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Using Markov chains and temporal alignment to identify clinical patterns in Dementia.

Luísa Marote Costa1, João Colaço2, Alexandra M Carvalho3

  • 1Instituto Superior Técnico, Avenida Rovisco Pais, 1, Lisbon, 1049-001, Portugal; INESC-ID, Rua Alves Redol 9, Lisbon, 1000-029, Portugal.

Journal of Biomedical Informatics
|March 16, 2023
PubMed
Summary
This summary is machine-generated.

This study uses big data analytics to map patient journeys for individuals with dementia. Identifying common medical appointment patterns helps personalize treatment and predict clinical pathways.

Keywords:
ClusteringDementiaElectronic medical recordsMarkov chainsMultimorbidityTemporal sequence alignment

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Area of Science:

  • Healthcare Analytics
  • Computational Medicine
  • Gerontology

Background:

  • Big data and advanced analytics offer significant advantages in managing complex patient groups with comorbidities.
  • Dementia is a growing concern, especially with an aging population, and understanding its heterogeneous factors is crucial.

Purpose of the Study:

  • To identify key features and clinical pathways of patients with dementia and multimorbidity.
  • To stratify patients into subgroups based on similar medical appointment patterns.
  • To develop a tool for early signaling of likely clinical pathways and support treatment decisions.

Main Methods:

  • Analysis of medical appointment patterns in a cohort of 1924 dementia patients (2007-2021).
  • Application of Markov Chains to identify prevalent medical appointments and transitions.
  • Utilized AliClu, a temporal sequence alignment algorithm, for clustering longitudinal clinical data.

Main Results:

  • Successfully identified prevailing medical appointments and recurring transitions between them for dementia patients.
  • Stratified patients into distinct subgroups with similar medical appointment activity using AliClu.
  • Feature analysis per cluster revealed unique patient characteristics and patterns.

Conclusions:

  • The developed pipeline effectively maps clinical pathways and transitions in dementia care.
  • This methodology can serve as a valuable support tool for healthcare providers in personalized treatment planning.
  • Integrating this approach with demographic and clinical data enhances early signaling of patient trajectories.