Details

Autor(en) / Beteiligte

• Ledieu, Thibault
• Bouzillé, Guillaume
• Polard, Elisabeth
• Plaisant, Catherine
• Thiessard, Frantz
• Cuggia, Marc

Titel

Clinical Data Analytics With Time-Related Graphical User Interfaces: Application to Pharmacovigilance

Ist Teil von

• Frontiers in pharmacology, 2018-08, Vol.9, p.717-717

Ort / Verlag

Switzerland: Frontiers Research Foundation

Erscheinungsjahr

2018

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Pharmacovigilance consists in monitoring and preventing the occurrence of adverse drug reactions. This activity can be time-consuming because it requires the collection of both patient and medication information. In this paper, we present two visualization and data mining applications to make this task easier for the practitioner. These tools have been developed and tested using the biomedical data warehouse eHOP (Hospital Biomedical Data Warehouse) of the Rennes University Hospital Centre. The first application is a tool to visualize the patient electronic health record in the form of a timeline. All patient data is collected and displayed chronologically. The usability test of the timeline has been very positive (SUS score: 82.5) and the tool is now available for practitioners in their daily practice. The second application is a tool to visualize and search the sequences of a patient cohort. The visual interface allow user to quickly visualize sequences. A query builder allows user to search for sequences in relation with a reference sequence, such as a prescription sequence followed by an abnormal biological value. The sequences are then visually aligned with this reference sequence and ranked by similarity. The GSP (Generalized Sequential Pattern) and Apriori algorithms allow us to display a summary of the sequences list by searching for common sequences and associations. The tool was tested on a use case which consisted in detection of inappropriate drug administration. Compared to a random order, we showed this ranking system saved the practitioner time in this task (to analyze one sequence, 3.49 ± 3.54 vs. 2.26 ± 2.86 s, = 0.0003). These two visualization and data mining applications will help the daily practice of pharmacovigilance.