Mining clinical big data for drug safety Detecting inadequate treatment with a DNA sequence alignment algorithm

Abstract : Health data mining can bring valuable information for drug safety activities. We developed a visual analytics tool to find specific clinical event sequences within the data contained in a clinical data warehouse. To this aim, we adapted the Smith-Waterman DNA sequence alignment algorithm to retrieve clinical event sequences with a temporal pattern from the electronic health records included in a clinical data warehouse. A web interface facilitates interactive query specification and result visualization. We describe the adaptation of the Smith-Waterman algorithm, and the implemented user interface. The evaluation with pharmacovigilance use cases involved the detection of inadequate treatment decisions in patient sequences. The precision and recall results (F-measure = 0.87) suggest that our adaptation of the Smith-Waterman-based algorithm is well-suited for this type of pharmacovigilance activities. The user interface allowed the rapid identification of cases of inadequate treatment.
Document type :
Journal articles
Complete list of metadatas

https://hal-univ-rennes1.archives-ouvertes.fr/hal-02088238
Contributor : Laurent Jonchère <>
Submitted on : Tuesday, April 2, 2019 - 4:45:15 PM
Last modification on : Friday, August 2, 2019 - 2:30:10 PM

Identifiers

  • HAL Id : hal-02088238, version 1
  • PUBMED : 30815181

Collections

Citation

T. Ledieu, G. Bouzillé, C. Plaisant, F. Thiessard, E. Polard, et al.. Mining clinical big data for drug safety Detecting inadequate treatment with a DNA sequence alignment algorithm. AMIA .. Annual Symposium proceedings. AMIA Symposium, 2018, 2018, pp.1368-1376. ⟨hal-02088238⟩

Share

Metrics

Record views

14