Abstract
This paper proposes a new paradigm to discover biomarkers capable of characterizing obsessive-compulsive disorder (OCD). These biomarkers, named neuromarkers, will be obtained through the analysis of sets of magnetic resonance images (MRI) of OCD patients and control subjects.
The design of the neuromarkers stems from a method for the automatic discovery of clusters of voxels relevant to OCD recently published by the authors. With these clusters as starting point, we will define the neuromarkers as a set of measurements describing features of these individual regions. The principal goal of the project is to come up with a set of about 50 neuromarkers for OCD characterization that are easy to interpret and handle by the psychiatric community.
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Keywords
- Support Vector Machine
- Obsessive Compulsive Disorder
- Automatic Design
- Magnetic Resonance Image Data
- Recursive Feature Elimination
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Hinde, O.G., Parrado-Hernández, E., Gómez-Verdejo, V., Martínez-Ramón, M., Soriano-Mas, C. (2014). Automatic Design of Neuromarkers for OCD Characterization. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2014. Lecture Notes in Computer Science(), vol 8724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44848-9_29
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