Science is more than my profession, I want to bring tangible progress to the life of people with a mental disorder. Through a focus on individuals as opposed to the disorder as a category, I hope to reduce stigmatization and in the long run improve our capacity to treat mechanisms causing suffering. In doing so, I apply and develop machine learning methods.
In our replication study preprint (Mai, 2020) we show that the average patient in psychiatry reliably falls apart when we map heterogeneity at the level of individuals with the same mental disorder.
Wolfers, T., Buitelaar, J. K., Beckmann, C. F., Franke, B., & Marquand, A. F. (2015). From estimating activation locality to predicting disorder: A review of pattern recognition for neuroimaging-based psychiatric diagnostics. Neuroscience and Biobehavioral Reviews.
Wolfers, T., Beckmann, C.F., Hoogman, M., Buitelaar, J.K., Franke, B., Marquand, A.F. (2019). Individual differences v the average patient: mapping the heterogeneity in ADHD using normative models. Psychological Medicine.
Wolfers, T., Trung, N.T., Kaufmann, T., Alnæs, D., Moberget, T., Agartz, I., Buitelaar, J.K., Ueland, T., Melle, I., Franke, B., Andreassen, O.A., Beckmann, C.F., Westlye, L.T., Marquand, A.F. (2018). Mapping the heterogeneous phenotype of schizophrenia and bipolar disorder using normative models. JAMA Psychiatry.
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