Predicting Adverse Drug Events using Heterogeneous Event Sequences
2016 (English)In: International Conference on Healthcare Informatics, IEEE Computer Society, 2016Conference paper, Poster (Refereed)
Adverse drug events (ADEs) are known to be severely under-reported in electronic health record (EHR) systems. One approach to mitigate this problem is to employ machine learning methods to detect and signal for potentially missing ADEs, with the aim of increasing reporting rates. There are, however, many challenges involved in constructing prediction models for this task, since data present in health care records is heterogeneous, high dimensional, sparse and temporal. Previous approaches typically employ bag-of-items representations of clinical events that are present in a record, ignoring the temporal aspects. In this paper, we study the problem of classifying heterogeneous and multivariate event sequences using a novel algorithm building on the well known concept of ensemble learning. The proposed approach is empirically evaluated using 27 datasets extracted from a real EHR database with different ADEs present. The results indicate that the proposed approach, which explicitly models the temporal nature of clinical data, can be expected to outperform, in terms of the trade-off between precision and specificity, models that do no consider the temporal aspects.
Place, publisher, year, edition, pages
IEEE Computer Society, 2016.
Adverse drug events, temporal patterns, data series, ensemble methods, random forest
Research subject Computer and Systems Sciences
IdentifiersURN: urn:nbn:se:su:diva-135439OAI: oai:DiVA.org:su-135439DiVA: diva2:1045223
IEEE International Conference on Health Care Informatics, Chicago, Illinois, USA, October 4-7, 2016