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Temporality and Context for detecting adverse drug reactions from longitudinal data

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Abstract

This paper introduces a method for mining co-occurring events from longitudinal data, and applies this method to detecting adverse drug reactions (ADRs) from patient data. Electronic health records are richer than older data sources (such as spontaneous report records) and thus are ideal for ADR mining. However, current data mining methods, such as disproportionality ratios and temporal itemset mining, ignore certain important aspects of the longitudinal data in patient records. In this paper, we highlight two specific problems with current methods, which we name temporal and contextual sensitivity, and discuss why these two properties are vital to mining patterns from longitudinal data. We also propose two sensitive longitudinal rate comparison measures, which utilize condition occurrence rates and length of drug eras, for mining ADRs from this type of data. These novel methods are then used to rank potential ADRs, along with existing state-of-the-art methods, under many simulated yet realistic datasets. In 48 out of 60 experiments, the proposed longitudinal rate comparison methods significantly outperform other methods in mining known ADRs from other drug / condition pairs.

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Acknowledgments

The authors would like to thank MITRE corporation for its support of this project in 2013.

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Authors and Affiliations

  1. Department of Computer Science, University of Massachusetts, 100 Morrissey Blvd., Boston, MA, 02125-3393, USA

    Henry Lo & Wei Ding

  2. The MITRE Corporation, 7515 Colshire Drive, Mclean, VA, USA

    Zohreh Nazeri

Authors
  1. Henry Lo
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  2. Wei Ding
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  3. Zohreh Nazeri
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Correspondence to Wei Ding.

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Lo, H., Ding, W. & Nazeri, Z. Temporality and Context for detecting adverse drug reactions from longitudinal data. Appl Intell 41, 1069–1080 (2014). https://doi.org/10.1007/s10489-014-0568-3

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