Christian Lovis

Raphaël Chevrier

M. Raphaël Chevrier


Campus Biotech G6
+41 22 379 08 17

Curriculum vitae

Raphaël studied medicine at the University of Geneva. During that time he developed an interest in medical informatics and wrote his master thesis under the supervision of Prof C. Lovis. Later, he had the opportunity to undertake a research internship in the laboratory of S. Meystre MD, PhD at the Department of Biomedical Informatics at the University of Utah, USA. After obtaining his medical degree in 2011, Raphaël worked at Morges Hospital in internal medicine before moving to London where he continued his clinical training in acute medicine and intensive care at King's College Hospital.

At the end of 2016, he returned to Switzerland and works now as a clinician (50%) in the department of internal medicine (SMIG) and as a researcher (50%) in the field of medical informatics (SIMED).

Research domains

Natural Language Processing  (NLP)

Data science: information extraction (Big data)

Anonymization and de-identification of medical data

Hospital Information System (HIS)

Clinical Decision Support Systems (CDSS)


Anonymization: scoping review




Foufi V., Gaudet-Blavignac C., Chevrier R. & Lovis C.: De-Identification of Medical Narrative Data. Stud Health Technol Inform. 2017;244:23-27.

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.

Medical Semantics and Data Analysis