Expertise

I am interested in applying (deep) machine learning models to questions regarding cognition and health. Projects that I am involved in included a computational psychiatry  with Erasmus MC employing clustering approaches for population cohort, prediction of recovery after trauma in collaboration with ETZ hospital and the Brabant Trauma Registry and prediction of deterioration with the ETZ vitally challenged work group. 

 

Courses

Collaboration

Elizabeth-Tweesteden ziekenhuis

ErasmusMC

Esculine

Recent publications

  1. Uncovering psychiatric phenotypes using unsupervised machine learning…

    Hofman, A., Lier, I., Ikram, M. A., van Wingerden, M., & Luik, A. I. (2023). Uncovering psychiatric phenotypes using unsupervised machine learning: A data-driven symptoms approach. European Psychiatry, 66(1), 1-9. Article e27.
  2. 50-kHz ultrasonic vocalizations do not signal social anhedonia in tra…

    Seidisarouei, M., Schäble, S., van Wingerden, M., Trossbach, S. V., Korth, C., & Kalenscher, T. (2023). 50-kHz ultrasonic vocalizations do not signal social anhedonia in transgenic DISC1 rats. Brain and Behavior, 13(5), 1-10. Article e2984.
  3. Effect of social instability stress in adolescence or adulthood on se…

    Herlehy, R. A., Lim, S., Murray, S. H., Baumbach, J. L., van Wingerden, M., & McCormick, C. M. (2022). Effect of social instability stress in adolescence or adulthood on sensitivity to sucrose concentration in a social context in male and female Long-Evans rats. Developmental Psychobiology, 64(6), e22293. Article e22293.
  4. Social anhedonia as a Disrupted-in-Schizophrenia 1-dependent phenotype

    Seidisarouei, M., Schäble, S., van Wingerden, M., Trossbach, S. V., Korth, C., & Kalenscher, T. (2022). Social anhedonia as a Disrupted-in-Schizophrenia 1-dependent phenotype. Scientific Reports, 12(1), 10182. Article 10182.
  5. Clustering of trauma patients based on longitudinal data and the appl…

    Stoitsas, K., Bahulikar, S., de Munter, L., de Jongh, M. A. C., Jansen, M. A. C., Jung, M. M., van Wingerden, M., & Van Deun, K. (2022). Clustering of trauma patients based on longitudinal data and the application of machine learning to predict recovery. Scientific Reports, 12(1), Article 16990.

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