Automated, Efficient, and Accelerated Knowledge Modeling of the Cognitive Neuroimaging Literature Using the ATHENA Toolkit
| Autor: | Taylor Salo, Jason Hays, Matthew T. Sutherland, Jessica A. Turner, Michael C. Riedel, Angela R. Laird, Matthew D. Turner |
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| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Computer science Ontology (information science) computer.software_genre lcsh:RC321-571 03 medical and health sciences Naive Bayes classifier Annotation Knowledge modeling 0302 clinical medicine Neuroimaging Selection (linguistics) ontology lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry Original Research neuroimaging business.industry General Neuroscience text-mining machine-learning Cognition 030104 developmental biology annotation classification Artificial intelligence business Classifier (UML) computer 030217 neurology & neurosurgery Natural language processing Neuroscience |
| Zdroj: | Frontiers in Neuroscience Frontiers in Neuroscience, Vol 13 (2019) |
| ISSN: | 1662-4548 |
| Popis: | Neuroimaging research is growing rapidly, providing expansive resources for synthesizing data. However, navigating these dense resources is complicated by the volume of research articles and variety of experimental designs implemented across studies. The advent of machine learning algorithms and text-mining techniques has advanced automated labeling of published articles in biomedical research to alleviate such obstacles. As of yet, a comprehensive examination of document features and classifier techniques for annotating neuroimaging articles has yet to be undertaken. Here, we evaluated which combination of corpus (abstract-only or full-article text), features (bag-of-words or Cognitive Atlas terms), and classifier (Bernoulli naïve Bayes, k-nearest neighbors, logistic regression, or support vector classifier) resulted in the highest predictive performance in annotating a selection of 2,633 manually annotated neuroimaging articles. We found that, when utilizing full article text, data-driven features derived from the text performed the best, whereas if article abstracts were used for annotation, features derived from the Cognitive Atlas performed better. Additionally, we observed that when features were derived from article text, anatomical terms appeared to be the most frequently utilized for classification purposes and that cognitive concepts can be identified based on similar representations of these anatomical terms. Optimizing parameters for the automated classification of neuroimaging articles may result in a larger proportion of the neuroimaging literature being annotated with labels supporting the meta-analysis of psychological constructs. |
| Databáze: | OpenAIRE |
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