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Recommended citation: Parsons, O., Barlow, N. E., Baxter, J., Paraschin, K., Derix, A., Hein, P., & Dürichen, R. (2023). Enabling scalable clinical interpretation of machine learning (ML)-based phenotypes using real world data. Journal of Medical Artificial Intelligence. https://jmai.amegroups.org/article/view/7585/html
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Recommended citation: Parsons, O., & Baron-Cohen, S. (2023). Extraction and generalisation of category-level information during visual statistical learning in autistic people. PLoS One. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0286018
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Recommended citation: Jassim, N., Owen, A. M., Smith, P., Suckling, J., Lawson, R. P., Baron-Cohen, S., & Parsons, O. (2022). Perceptual decision-making in autism as assessed by “spot the difference” visual cognition tasks. Scientific Reports. https://www.nature.com/articles/s41598-022-19640-4
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Recommended citation: Javer, A., Parsons, O., Carr, O., Baxter, J., Diedrich, C., Elçi, E., ... & Dürichen, R. (2021). Compensating trajectory bias for unsupervised patient stratification using adversarial recurrent neural networks. arXiv preprint. https://arxiv.org/abs/2112.07239
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Recommended citation: Carr, O., Javer, A., Rockenschaub, P., Parsons, O., & Durichen, R. (2021). "Longitudinal patient stratification of electronic health records with flexible adjustment for clinical outcomes." Machine Learning for Health. PMLR. https://proceedings.mlr.press/v158/carr21a.html
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Recommended citation: J.D. Kist, R.A.I. Bethlehem, Barnaby Stonier, ... , Jan Freyberg, Simon Baron-Cohen & O. E. Parsons. (2020). Assessing the relationship between cognitive empathy, age, and face perception using a sequential Bayesian analysis. https://www.biorxiv.org/content/10.1101/2020.09.11.292706v1
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Recommended citation: Parsons, O. E. (2020). A Gaussian Mixture Model Approach to Classifying Response Types. (Chapter) Mixture Models and Applications (pp. 3-22). Springer, Cham. https://www.springer.com/gp/book/9783030238759
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Recommended citation: Lubitz, S. A., Parsons, O. E., Anderson, C. D., Benjamin, E. J., Malik, R., Weng, L. C., ... & Ellinor, P. T. (2017). Atrial fibrillation genetic risk and ischemic stroke mechanisms. Stroke, 48(6), 1451-1456. https://pubmed.ncbi.nlm.nih.gov/28468926/
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Recommended citation: Bethlehem, R., Falkiewicz, M., Freyberg, J., Parsons, O.E., Farahibozorg, S. R., Pretzsch, C., ... & Margulies, D. (2017). Gradients of cortical hierarchy in Autism. Research Ideas and Outcomes. https://riojournal.com/article/13391/
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Recommended citation: Traylor, M., Zhang, C. R., Adib-Samii, P., Devan, W. J., Parsons, O. E., Lanfranconi, S., ... & Fitzpatrick, K. (2016). Genome-wide meta-analysis of cerebral white matter hyperintensities in patients with stroke. Neurology, 86(2), 146-153. https://n.neurology.org/content/86/2/146.long
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Recommended citation: Parsons, O.E. & Baron-Cohen, S. (2018). Statistical Learning of Dynamic Contingencies in Autism Spectrum Conditions. International Society for Autism Research (INSAR).
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Recommended citation: Parsons, O.E., Bethlehem, R., Freyberg, & Baron-Cohen, S. (2017). Using steady state visual evoked potentials to measure face processing in autism. Conference on Cognitive Computational Neuroscience (CCN).
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Recommended citation: Parsons, O.E., Bethlehem, R., Freyberg, J.,Sluijters, O., Stonier, B., & Baron-Cohen, S. (2016). Predicting cognitive empathy from face specific neural signatures. Society for Neuroscience (SfN) annual meeting.