Encoding Medication Episodes for Adverse Drug Event Prediction

Honghan Wu, Zina M. Ibrahim, Ehtesham Iqbal, Richard J. B. Dobson, Max Bramer (Editor), Miltos Petridis (Editor)

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Understanding the interplay among the multiple factors leading to Adverse Drug Reactions (ADRs) is crucial to increasing drug effectiveness, individualising drug therapy and reducing incurred cost. In this paper, we propose a flexible encoding mechanism that can effectively capture the dynamics of multiple medication episodes of a patient at any given time. We enrich the encoding with a drug ontology and patient demographics data and use it as a base for an ADR prediction model. We evaluate the resulting predictive approach under different settings using real anonymised patient data obtained from the EHR of the South London and Maudsley (SLaM), the largest mental health provider in Europe. Using the profiles of 38,000 mental health patients, we identified 240,000 affirmative mentions of dry mouth, constipation and enuresis and 44,000 negative ones. Our approach achieved 93 % prediction accuracy and 93 % F-Measure.
Original languageEnglish
Title of host publicationResearch and Development in Intelligent Systems XXXIII
PublisherSpringer
Pages245-250
Number of pages6
ISBN (Electronic)978-3-319-47175-4
ISBN (Print)978-3-319-47174-7
DOIs
Publication statusPublished - 2016
EventThe Thirty-Sixth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence - Cambridge, United Kingdom
Duration: 13 Dec 201615 Dec 2016

Conference

ConferenceThe Thirty-Sixth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence
Abbreviated titleAI-2016
CountryUnited Kingdom
CityCambridge
Period13/12/1615/12/16

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