Predicting Adverse Drug Events from Electronic Medical Records

  • Jesse DavisEmail author
  • Vítor Santos Costa
  • Peggy Peissig
  • Michael Caldwell
  • David Page
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9521)


Learning from electronic medical records (EMR) poses many challenges from a knowledge representation point of view. This chapter focuses on how to cope with two specific challenges: the relational nature of EMRs and the uncertain dependence between a patient’s past and future health status. We discuss three different approaches for allowing standard propositional learners to incorporate relational information. We evaluate these approaches on three real-world tasks where the goal is to use EMRs to predict whether a patient will have an adverse reaction to a medication.


Bayesian Network Electronic Medical Record Inductive Logic Programming Conditional Mutual Information Electronic Medical Record Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



JD is partially supported by the Research Fund K.U.Leuven (CREA/11/015 and OT/11/051), EU FP7 Marie Curie Career Integration Grant (\(\#\)294068) and FWO-Vlaanderen (G.0356.12). VSC is funded by ERDF through Programme COMPETE and by the Portuguese Government through FCT Foundation for Science and Technology projects LEAP (PTDC/EIA-CCO/112158/2009) and ADE (PTDC/EIA-EIA/121686/2010). MC, PP, EB and DP gratefully acknowledge the support of NIGMS grant R01GM097618-01.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jesse Davis
    • 1
    Email author
  • Vítor Santos Costa
    • 4
  • Peggy Peissig
    • 2
  • Michael Caldwell
    • 2
  • David Page
    • 3
  1. 1.Department of Computer ScienceKu LeuvenHeverleeBelgium
  2. 2.Marshfield ClinicMarshfieldUSA
  3. 3.University of WisconsinMadisonUSA
  4. 4.CRACS INESC-TEC and FCUPUniversidade do PortoPortoPortugal

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