A data-driven method to detect adverse drug events from prescription data

Publication date: September 2018Source: Journal of Biomedical Informatics, Volume 85Author(s): Chen Zhan, Elizabeth Roughead, Lin Liu, Nicole Pratt, Jiuyong LiAbstractDrug safety issues such as Adverse Drug Events (ADEs) can cause serious consequences for the public. The clinical trials that are undertaken to assess medicine efficacy and safety prior to marketing, generally, may provide sufficient samples for discovering common ADEs. However, more samples are needed to detect infrequent and rare events. Additionally, clinical trials may not include all subgroups of patients. For these reasons, post-marketing surveillance of medicines is necessary for identifying drug safety issues. Most regulatory agencies use the Spontaneous Reporting Systems to identify associations between medicines and suspected ADEs. Data mining with effective analytical frameworks and large-scale medical data is potentially an alternative method to discover and monitor ADEs. In the present paper, we aim to detect potential ADEs from prescription data by discovering ADE associated prescription sequences. In an ADE associated prescription sequence 〈Dp→Ds〉, the prior medicine Dp leads to an ADE for which the succeeding medicine Ds is dispensed to treat. We propose a data-driven method which integrates (1) a constrained sequential pattern mining to uncover prescription sequences as potential signals of ADEs, (2) domain constraints to eliminate interference signals and (3) an adapted Self-Controlled Ca...
Source: Journal of Biomedical Informatics - Category: Information Technology Source Type: research