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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A Bayesian Model for the Prediction and Early Diagnosis of Alzheimer's Disease
|
|---|---|
| Published in |
Frontiers in Aging Neuroscience, March 2017
|
| DOI | 10.3389/fnagi.2017.00077 |
| Pubmed ID | |
| Authors |
Athanasios Alexiou, Vasileios D. Mantzavinos, Nigel H. Greig, Mohammad A. Kamal |
| Abstract |
Alzheimer's disease treatment is still an open problem. The diversity of symptoms, the alterations in common pathophysiology, the existence of asymptomatic cases, the different types of sporadic and familial Alzheimer's and their relevance with other types of dementia and comorbidities, have already created a myth-fear against the leading disease of the twenty first century. Many failed latest clinical trials and novel medications have revealed the early diagnosis as the most critical treatment solution, even though scientists tested the amyloid hypothesis and few related drugs. Unfortunately, latest studies have indicated that the disease begins at the very young ages thus making it difficult to determine the right time of proper treatment. By taking into consideration all these multivariate aspects and unreliable factors against an appropriate treatment, we focused our research on a non-classic statistical evaluation of the most known and accepted Alzheimer's biomarkers. Therefore, in this paper, the code and few experimental results of a computational Bayesian tool have being reported, dedicated to the correlation and assessment of several Alzheimer's biomarkers to export a probabilistic medical prognostic process. This new statistical software is executable in the Bayesian software Winbugs, based on the latest Alzheimer's classification and the formulation of the known relative probabilities of the various biomarkers, correlated with Alzheimer's progression, through a set of discrete distributions. A user-friendly web page has been implemented for the supporting of medical doctors and researchers, to upload Alzheimer's tests and receive statistics on the occurrence of Alzheimer's disease development or presence, due to abnormal testing in one or more biomarkers. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 13% |
| United States | 1 | 13% |
| Chile | 1 | 13% |
| Greece | 1 | 13% |
| Unknown | 4 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 63% |
| Scientists | 3 | 38% |
Mendeley readers
The data shown below were compiled from readership statistics for 119 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 1 | <1% |
| Unknown | 118 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 19 | 16% |
| Student > Ph. D. Student | 15 | 13% |
| Researcher | 12 | 10% |
| Student > Bachelor | 8 | 7% |
| Professor > Associate Professor | 5 | 4% |
| Other | 24 | 20% |
| Unknown | 36 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 12 | 10% |
| Psychology | 11 | 9% |
| Nursing and Health Professions | 8 | 7% |
| Computer Science | 8 | 7% |
| Neuroscience | 6 | 5% |
| Other | 31 | 26% |
| Unknown | 43 | 36% |
Attention Score in Context
This research output has an Altmetric Attention Score of 16. 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 01 April 2018.
All research outputs
#2,003,294
of 22,962,258 outputs
Outputs from Frontiers in Aging Neuroscience
#579
of 4,832 outputs
Outputs of similar age
#40,733
of 309,402 outputs
Outputs of similar age from Frontiers in Aging Neuroscience
#24
of 110 outputs
Altmetric has tracked 22,962,258 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,832 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.1. This one has done well, scoring higher than 87% 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 309,402 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 110 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.