09 Automatic detection of adverse drug events in the geriatric care


Elderly people are particularly exposed to adverse drug events. Developing tools to help detecting these adverse effects along with preventive procedures will help optimise medication safety in the elderly hospitalized population.

Portrait / project description (ongoing research project)

The project is being conducted by an interdisciplinary team at five hospitals in German and French-speaking Switzerland. First, the list of anti-thrombotic drugs will be established along with their side effects, risk and confounding factors in order to develop detection tools. Second, clinical and biological information available in the form of free text and structured data will be extracted from the electronic medical files of elderly patients. Third, software algorithms will be developed allowing this information to be understood and processed in order to identify adverse events and their triggering factors. Finally, the validity of these algorithms and detection tools will be verified.


Adverse drug events are observed in approximatively one third of elderly people in hospital. The medications most at risk include anti-thrombotic drugs, which are widely used in geriatrics to prevent thrombosis. Tools that help detecting medication-related problems already exist in hospitals but are not well developed and could be improved to better identify factors likely to trigger adverse drug events.


This objective of the study is to optimise tools able to automatically detect undesired effects of medication based on information available in the patient’s electronic file. The objective is to quantify the number of haemorrhages and thromboses associated with prescription of anti-thrombotics, identify the triggering factors and propose improvements for clinical practice.

Relevance / Application

The project will allow the introduction of measures aiming at improving safety when prescribing anti-thrombotic medication. The findings will be implemented in clinical practice by means of indicators of adverse events for risk management, and training for healthcare professionals; the tools and methodologies developed will be disseminated for new research in this field.

Original title

Automated detection of adverse drug events from older inpatients’ electronic medical records using structured data mining and natural language processing

Project leaders


  • Prof. Chantal Csajka, Section des sciences pharmaceutiques, Centre Hospitalier Universitaire Vaudois


  • Prof. Christian Lovis, Service d'Informatique médicale, Hôpitaux Universitaires de Genève (HUG)
  • Dr. Marie Le Pogam, Unité de Prévention Communautaire (IUMSP), Université de Lausanne et Centre Hospitalier universitaire Vaudois (CHUV)
  • Dr. Patrick Beeler, Zentrum Alter und Mobilität, Klinik für Geriatrie, UniversitätsSpital Zürich
  • Prof. Pierre-Olivier Lang, Centre Hospitalier universitaire Vaudois (CHUV))

Project partners:

  • Dr. Nicole Vogt-Ferrier, Département de réhabilitation et gériatrie, Hôpitaux Universitaires de Genève (HUG)
  • Prof. Bernard Burnand, Institut Universitaire de Médecine Sociale et Préventive (IUMSP), Centre Hospitalier universitaire Vaudois (CHUV) et Université de Lausanne
  • Dr. Alex Gnaegi, Département Valaisan d'Oncologie, Institut Central des Hôpitaux Valaisans
  • Dr. Fabio Rinaldi, Institut für Computerlinguistik, Universität Zürich
  • Dr. Monika Lutters, Kantonsspital Baden



Further information on this content


Prof. Chantal Csajka Prof associée de pharmacie clinique
Section des sciences pharmaceutiques CMU
Rue Michel-Servet 1 1011 Lausanne +41 21 314 42 63

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