Encoding Medication Episodes for Adverse Drug Event Prediction
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.
KeywordsAI Languages Programming Techniques and Tools Bayesian Networks and Stochastic Reasoning Genetic Algorithms Machine Learning
This work has received support from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 644753 (KConnect) and National Institute for Health Research (NIHR) Biomedical Research Centre and Dementia Unit at South London and Maudsley NHS Foundation Trust and Kings College London.
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