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Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows

Overview of attention for article published in BMC Medical Informatics and Decision Making, July 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (74th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

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1 X user
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2 patents

Citations

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37 Dimensions

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86 Mendeley
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Title
Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows
Published in
BMC Medical Informatics and Decision Making, July 2017
DOI 10.1186/s12911-017-0497-2
Pubmed ID
Authors

Telma Pereira, Luís Lemos, Sandra Cardoso, Dina Silva, Ana Rodrigues, Isabel Santana, Alexandre de Mendonça, Manuela Guerreiro, Sara C. Madeira

Abstract

Predicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepted that cognition declines with a continuum between MCI and dementia. As such, cohorts of MCI patients are usually heterogeneous, containing patients at different stages of the neurodegenerative process. This hampers the prognostic task. Nevertheless, when learning prognostic models, most studies use the entire cohort of MCI patients regardless of their disease stages. In this paper, we propose a Time Windows approach to predict conversion to dementia, learning with patients stratified using time windows, thus fine-tuning the prognosis regarding the time to conversion. In the proposed Time Windows approach, we grouped patients based on the clinical information of whether they converted (converter MCI) or remained MCI (stable MCI) within a specific time window. We tested time windows of 2, 3, 4 and 5 years. We developed a prognostic model for each time window using clinical and neuropsychological data and compared this approach with the commonly used in the literature, where all patients are used to learn the models, named as First Last approach. This enables to move from the traditional question "Will a MCI patient convert to dementia somewhere in the future" to the question "Will a MCI patient convert to dementia in a specific time window". The proposed Time Windows approach outperformed the First Last approach. The results showed that we can predict conversion to dementia as early as 5 years before the event with an AUC of 0.88 in the cross-validation set and 0.76 in an independent validation set. Prognostic models using time windows have higher performance when predicting progression from MCI to dementia, when compared to the prognostic approach commonly used in the literature. Furthermore, the proposed Time Windows approach is more relevant from a clinical point of view, predicting conversion within a temporal interval rather than sometime in the future and allowing clinicians to timely adjust treatments and clinical appointments.

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The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 86 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 16 19%
Researcher 9 10%
Student > Ph. D. Student 8 9%
Student > Bachelor 8 9%
Other 6 7%
Other 17 20%
Unknown 22 26%
Readers by discipline Count As %
Computer Science 15 17%
Psychology 11 13%
Medicine and Dentistry 8 9%
Neuroscience 7 8%
Nursing and Health Professions 4 5%
Other 16 19%
Unknown 25 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 June 2020.
All research outputs
#4,971,886
of 24,411,829 outputs
Outputs from BMC Medical Informatics and Decision Making
#432
of 2,075 outputs
Outputs of similar age
#81,567
of 318,887 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#8
of 40 outputs
Altmetric has tracked 24,411,829 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,075 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has done well, scoring higher than 78% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 318,887 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.
We're also able to compare this research output to 40 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.