ReviewBMC medicine2020
Shaping a data-driven era in dementia care pathway through computational neurology approaches.
Review in BMC medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
- State-of-the-Art Sensors for Remote Care of People with Dementia during a Pandemic: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Predicting rates of cognitive and functional decline in Alzheimer's disease and mild cognitive impairment.Communications medicine · 2026Article
- Evidence-based care pathways for people with dementia and neurodegenerative disorders in Europe: a systematic review of models, integration of digital technologies, and quality indicators.Frontiers in medicine · 2026Review
- Autoencoder imputation of missing heterogeneous data for Alzheimer's disease classification.Healthcare technology letters · 2024Article
- Transforming Neurology Care Delivery Through a Population Health Data Strategy.Neurology. Clinical practice · 2024Article
- Article
- Data-driven care for patients with neurodegenerative disorders.Nature reviews. Neurology · 2023Article
- Alzheimer's disease classification using cluster-based labelling for graph neural network on heterogeneous data.Healthcare technology letters · 2022Article
- Multi-dimensional relationships among dementia, depression and prescribed drugs in England and Wales hospitals.BMC medical informatics and decision making · 2022Article
- 6G and Artificial Intelligence Technologies for Dementia Care: Literature Review and Practical Analysis.Journal of medical Internet research · 2022Review
- A Pragmatic Ensemble Strategy for Missing Values Imputation in Health Records.Entropy (Basel, Switzerland) · 2022Article
- Personalised treatment for cognitive impairment in dementia: development and validation of an artificial intelligence model.BMC medicine · 2022Article
- Alzheimer's Disease Assessments Optimized for Diagnostic Accuracy and Administration Time.IEEE journal of translational engineering in health and medicine · 2022Article
- Computational Psychiatry and Computational Neurology: Seeking for Mechanistic Modeling in Cognitive Impairment and Dementia.Frontiers in computational neuroscience · 2022Article
- Improved Alzheimer's Disease Detection by MRI Using Multimodal Machine Learning Algorithms.Diagnostics (Basel, Switzerland) · 2021Article
- The characteristics and performance of health data domains in supporting dementia identification: A systematic review.Alzheimer's & dementia (Amsterdam, Netherlands)Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundDementia is caused by a variety of neurodegenerative diseases and is associated with a decline in memory and other cognitive abilities, while inflicting an enormous socioeconomic burden. The complexity of dementia and its associated comorbidities presents immense challenges for dementia research and care, particularly in clinical decision-making. MAIN BODY: Despite the lack of disease-modifying therapies, there is an increasing and urgent need to make timely and accurate clinical decisions in dementia diagnosis and prognosis to allow appropriate care and treatment. However, the dementia care pathway is currently suboptimal. We propose that through computational approaches, understanding of dementia aetiology could be improved, and dementia assessments could be more standardised, objective and efficient. In particular, we suggest that these will involve appropriate data infrastructure, the use of data-driven computational neurology approaches and the development of practical clinical decision support systems. We also discuss the technical, structural, economic, political and policy-making challenges that accompany such implementations.
conclusionThe data-driven era for dementia research has arrived with the potential to transform the healthcare system, creating a more efficient, transparent and personalised service for dementia.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.