Raphaël Chevrier


Raphaël Chevrier, MDPhysician
Working group: Medical semantics and linguistic analysis
E-Mail: Raphael.Chevrier@hcuge.ch
Web: https://ch.linkedin.com/in/raphaël-chevrier-a376781b



Curriculum vitae

Raphael studied medicine at the University of Geneva. During that time he developed an interest in medical informatics and he had the opportunity to conduct a research internship under the supervision of S. Meystre MD, PhD in the Department of Biomedical Informatics at the University of Utah, USA. After obtaining his doctor's degree in 2011, Raphaël worked at the hospital in Morges in the internal medicine and emergencies unit before moving to London where he continued his clinical training in acute and then intensive care at King's College Hospital.
At the end of 2016, he returned to Switzerland and began his current employment at HUG, where he is working 50% as a researcher in the field of medical informatics (SIMED) and 50% as a clinician in the department of internal medicine (SMIG).

Research domains

  • Sharing of clinical data: Development of a web-based platform (i2b2) for the improvement of the Swiss Personalized Health Network (SPHN) by enabling the simultaneous interrogation of multiple hospital databases
  • Anonymizing and protecting the confidentiality of medical data
  • Natural Language Processing (NLP)
  • Decision Support Systems (Clinical Decision Support Systems (CDSS))


  • Chevrier, R.D., Child, K., Shah, S., Best, T. & Hopkins, P (2015): Baseline observation of performance and interprofessional utilisation of institutional hospital electronic technologies to access and communicate key clinical information in a central london teaching hospital critical care unit. Int Care Med Exp.
  • Chevrier, R.D., Jaques, D. & Lovis, C (2011): Architecture of a decision support system to improve clinicians' interpretation of abnormal liver function tests. Stud Health Technol Inform, Vol. 169, pp. 195-199.
  • Meystre, S.M., Lee, S., Jung, C.Y. & Chevrier, R.D. (2011): Common data model for natural language processing based on two existing standard information models: CDA+GrAF. J Biomed Inform.

More information

Last update : 08/03/2017