Rao, A;
Monteiro, J;
Mourao-Miranda, J;
(2016)
Prediction of Clinical Scores from Neuroimaging Data with Censored Likelihood Gaussian Processes.
In:
Proceedings of 2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI).
(pp. pp. 129-132).
IEEE: Trento, Italy.
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Abstract
In this paper, we explore the use of Censored Likelihoods in Gaussian Process Regression when predicting bounded clinical scores from neuroimaging data. The standard approach, which uses a Gaussian Likelihood, does not respect the fact that the clinical scores are bounded, and so may produce suboptimal models. Conversely, Censored Likelihoods explicitly model the restricted range of such clinical scores and carry this property through inference. We apply both the standard approach and the Censored Likelihood approach to the prediction of the MMSE score from structural MRI. Overall, we find small improvements in mean squared error when using the Censored Likelihood and in addition, the censored models are more favoured from a Bayesian perspective. We also discuss the qualitative nature of the predictions of the two approaches.
| Type: | Proceedings paper |
|---|---|
| Title: | Prediction of Clinical Scores from Neuroimaging Data with Censored Likelihood Gaussian Processes |
| Event: | 6th International Workshop on Pattern Recognition in Neuroimaging (PRNI) 2016 |
| Location: | Trento, ITALY |
| Dates: | 22 June 2016 - 24 June 2016 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://doi.org/10.1109/PRNI.2016.7552358 |
| Language: | English |
| Additional information: | Copyright © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Keywords: | gaussian processes, clinical scores |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/1513292 |
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