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

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...

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Bibliographic Details
Published in:AMIA ... Annual Symposium proceedings 2018, Vol.2018, p.1368-1376
Main Authors: Ledieu, Thibault, Bouzille, Guillaume, Plaisant, Catherine, Thiessard, Frantz, Polard, Elisabeth, Cuggia, Marc
Format: Article
Language:English
Subjects:
Algorithms
Base Sequence
Big Data
Bioengineering
Data Mining - methods
Electronic Health Records
Humans
Life Sciences
Pharmacovigilance
Sequence Alignment
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Summary: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.
ISSN:1942-597X
1559-4076