Dr. Samuel Wehrli

Projects

• Deep Learning-Based Real-Time EEG Monitoring for Clinical Interventions / Project leader / laufend
• Digital Health Zurich: a practice lab for patient-centred clinical innovation / Team member / laufend
• Smart Hospital – Integrated Framework, Tools & Solutions (SHIFT) / Project leader / laufend
• Predictive chance modelling: Exploration of a new approach to professional decision-making in child protection / Team member / abgeschlossen
• Machine learning and algorithms in social work / Team member / abgeschlossen
• Fine motor skills in school-aged Children with ADHD: A case-control study using digital pens (FAST) / Team member / abgeschlossen

Publications

• Stoll, Dragan; Wehrli, Samuel; Lätsch, David,

  2024.

  Case reports unlocked : harnessing large language models to advance research on child maltreatment.

  Child Abuse & Neglect.

  160(107202).

  Available from: https://doi.org/10.1016/j.chiabu.2024.107202

• Wehrli, Samuel; Hertweck, Corinna; Amirian, Mohammadreza; Glüge, Stefan; Stadelmann, Thilo,

  2021.

  Bias, awareness, and ignorance in deep-learning-based face recognition.

  AI and Ethics.

  2(3), pp. 509-522.

  Available from: https://doi.org/10.1007/s43681-021-00108-6

• Biberstein, Lorenz; Girschik, Katja; Wehrli, Samuel,

  2024.

  Twitter-Analysen der Polizei : Erkenntnisse für die Soziale Arbeit.

  Zürich:

  ZHAW Zürcher Hochschule für Angewandte Wissenschaften.

  Available from: https://doi.org/10.21256/zhaw-29968

• Truninger, Dominique; Zysset, Annina; Wehrli, Samuel; Albermann, Kurt; von Rhein, Michael; Wieber, Frank,

  2022.

  Digital pens for measuring fine motor skills in school-aged children with and without ADHD [poster].

  In:

  4. Digital Health Lab Day, Winterthur, Schweiz, 12. September 2022.

  ZHAW Zürcher Hochschule für Angewandte Wissenschaften.

  Available from: https://doi.org/10.21256/zhaw-25701

• Stoll, Dragan; Lätsch, David; Wehrli, Samuel,

  2024.

  Applying machine learning text classification methods and large language models for data collection of mentions of child maltreatment.

  In:

  ISPCAN Congress, Uppsala, Sweden, 18-21 August 2024.

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