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Predictive Intelligence in Medicine

Overview of attention for book
Cover of 'Predictive Intelligence in Medicine'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 TADPOLE Challenge: Accurate Alzheimer’s Disease Prediction Through Crowdsourced Forecasting of Future Data
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    Chapter 2 Inter-fractional Respiratory Motion Modelling from Abdominal Ultrasound: A Feasibility Study
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    Chapter 3 Adaptive Neuro-Fuzzy Inference System-Based Chaotic Swarm Intelligence Hybrid Model for Recognition of Mild Cognitive Impairment from Resting-State fMRI
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    Chapter 4 Deep Learning via Fused Bidirectional Attention Stacked Long Short-Term Memory for Obsessive-Compulsive Disorder Diagnosis and Risk Screening
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    Chapter 5 Modeling Disease Progression in Retinal OCTs with Longitudinal Self-supervised Learning
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    Chapter 6 Predicting Response to the Antidepressant Bupropion Using Pretreatment fMRI
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    Chapter 7 Progressive Infant Brain Connectivity Evolution Prediction from Neonatal MRI Using Bidirectionally Supervised Sample Selection
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    Chapter 8 Computed Tomography Image-Based Deep Survival Regression for Metastatic Colorectal Cancer Using a Non-proportional Hazards Model
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    Chapter 9 7 Years of Developing Seed Techniques for Alzheimer’s Disease Diagnosis Using Brain Image and Connectivity Data Largely Bypassed Prediction for Prognosis
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    Chapter 10 Generative Adversarial Irregularity Detection in Mammography Images
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    Chapter 11 Hierarchical Adversarial Connectomic Domain Alignment for Target Brain Graph Prediction and Classification from a Source Graph
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    Chapter 12 Predicting High-Resolution Brain Networks Using Hierarchically Embedded and Aligned Multi-resolution Neighborhoods
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    Chapter 13 Catheter Synthesis in X-Ray Fluoroscopy with Generative Adversarial Networks
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    Chapter 14 Prediction of Clinical Scores for Subjective Cognitive Decline and Mild Cognitive Impairment
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    Chapter 15 Diagnosis of Parkinson’s Disease in Genetic Cohort Patients via Stage-Wise Hierarchical Deep Polynomial Ensemble Learning
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    Chapter 16 Automatic Detection of Bowel Disease with Residual Networks
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    Chapter 17 Support Vector Based Autoregressive Mixed Models of Longitudinal Brain Changes and Corresponding Genetics in Alzheimer’s Disease
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    Chapter 18 Treatment Response Prediction of Hepatocellular Carcinoma Patients from Abdominal CT Images with Deep Convolutional Neural Networks
Attention for Chapter 1: TADPOLE Challenge: Accurate Alzheimer’s Disease Prediction Through Crowdsourced Forecasting of Future Data
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

news
1 news outlet
twitter
4 X users

Citations

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2 Dimensions

Readers on

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40 Mendeley
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Chapter title
TADPOLE Challenge: Accurate Alzheimer’s Disease Prediction Through Crowdsourced Forecasting of Future Data
Chapter number 1
Book title
Predictive Intelligence in Medicine
Published in
arXiv, October 2019
DOI 10.1007/978-3-030-32281-6_1
Pubmed ID
Book ISBNs
978-3-03-032280-9, 978-3-03-032281-6
Authors

Răzvan V. Marinescu, Neil P. Oxtoby, Alexandra L. Young, Esther E. Bron, Arthur W. Toga, Michael W. Weiner, Frederik Barkhof, Nick C. Fox, Polina Golland, Stefan Klein, Daniel C. Alexander, Razvan V. Marinescu, Marinescu, Răzvan V., Oxtoby, Neil P., Young, Alexandra L., Bron, Esther E., Toga, Arthur W., Weiner, Michael W., Barkhof, Frederik, Fox, Nick C., Golland, Polina, Klein, Stefan, Alexander, Daniel C., Marinescu, RV, Oxtoby, NP, Young, AL, Bron, EE, Toga, AW, Weiner, MW, Barkhof, F, Fox, NC, Golland, P, Klein, S, Alexander, DC

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users 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 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 40 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 23%
Researcher 5 13%
Other 3 8%
Student > Master 2 5%
Professor 1 3%
Other 1 3%
Unknown 19 48%
Readers by discipline Count As %
Computer Science 7 18%
Neuroscience 4 10%
Medicine and Dentistry 3 8%
Engineering 2 5%
Mathematics 2 5%
Other 1 3%
Unknown 21 53%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 14 January 2021.
All research outputs
#2,999,754
of 23,168,000 outputs
Outputs from arXiv
#55,399
of 953,740 outputs
Outputs of similar age
#65,207
of 354,133 outputs
Outputs of similar age from arXiv
#1,891
of 29,311 outputs
Altmetric has tracked 23,168,000 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 953,740 research outputs from this source. They receive a mean Attention Score of 3.9. This one has done particularly well, scoring higher than 94% 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 354,133 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 81% of its contemporaries.
We're also able to compare this research output to 29,311 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.