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Predicting Adverse Drug Events using Heterogeneous Event Sequences
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2016 (English)In: 2016 IEEE International Conference on Healthcare Informatics (ICHI), IEEE Computer Society, 2016, 356-362 p.Conference paper, Published paper (Refereed)
Abstract [en]

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. 356-362 p.
Keyword [en]
Adverse drug events, temporal patterns, data series, ensemble methods, random forest
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-135439DOI: 10.1109/ICHI.2016.64ISBN: 978-1-5090-6117-4 (electronic)OAI: oai:DiVA.org:su-135439DiVA: diva2:1045223
Conference
IEEE International Conference on Health Care Informatics, Chicago, Illinois, USA, October 4-7, 2016
Available from: 2016-11-08 Created: 2016-11-08 Last updated: 2017-04-28Bibliographically approved
In thesis
1. Order in the random forest
Open this publication in new window or tab >>Order in the random forest
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In many domains, repeated measurements are systematically collected to obtain the characteristics of objects or situations that evolve over time or other logical orderings. Although the classification of such data series shares many similarities with traditional multidimensional classification, inducing accurate machine learning models using traditional algorithms are typically infeasible since the order of the values must be considered.

In this thesis, the challenges related to inducing predictive models from data series using a class of algorithms known as random forests are studied for the purpose of efficiently and effectively classifying (i) univariate, (ii) multivariate and (iii) heterogeneous data series either directly in their sequential form or indirectly as transformed to sparse and high-dimensional representations. In the thesis, methods are developed to address the challenges of (a) handling sparse and high-dimensional data, (b) data series classification and (c) early time series classification using random forests. The proposed algorithms are empirically evaluated in large-scale experiments and practically evaluated in the context of detecting adverse drug events.

In the first part of the thesis, it is demonstrated that minor modifications to the random forest algorithm and the use of a random projection technique can improve the effectiveness of random forests when faced with discrete data series projected to sparse and high-dimensional representations. In the second part of the thesis, an algorithm for inducing random forests directly from univariate, multivariate and heterogeneous data series using phase-independent patterns is introduced and shown to be highly effective in terms of both computational and predictive performance. Then, leveraging the notion of phase-independent patterns, the random forest is extended to allow for early classification of time series and is shown to perform favorably when compared to alternatives. The conclusions of the thesis not only reaffirm the empirical effectiveness of random forests for traditional multidimensional data but also indicate that the random forest framework can, with success, be extended to sequential data representations.

Place, publisher, year, edition, pages
Stockholm: Department of Computer and Systems Sciences, Stockholm University, 2017. 76 p.
Series
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 17-004
Keyword
Machine learning, random forest, ensemble, time series, data series, sequential data, sparse data, high-dimensional data
National Category
Computer and Information Science
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-142052 (URN)978-91-7649-827-9 (ISBN)978-91-7649-828-6 (ISBN)
Public defence
2017-06-08, L30, NOD-huset, Borgarfjordsgatan 12, Stockholm, 13:00 (English)
Opponent
Supervisors
Funder
Swedish Foundation for Strategic Research , IIS11-0053
Available from: 2017-05-16 Created: 2017-04-24 Last updated: 2017-05-15Bibliographically approved

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