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Predicting Adverse Drug Events by Analyzing Electronic Patient Records
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2013 (English)In: Artificial Intelligence in Medicine: 14th Conference on Artificial Intelligence in Medicine, AIME 2013. Proceedings / [ed] Niels Peek, Roque Marín Morales, Mor Peleg, Springer Berlin/Heidelberg, 2013, Vol. 7885, 125-129 p.Conference paper, Published paper (Refereed)
Abstract [en]

Diagnosis codes for adverse drug events (ADEs) are sometimes missing from electronic patient records (EPRs). This may not only affect patient safety in the worst case, but also the number of reported ADEs, resulting in incorrect risk estimates of prescribed drugs. Large databases of electronic patient records (EPRs) are potentially valuable sources of information to support the identification of ADEs. This study investigates the use of machine learning for predicting one specific ADE based on information extracted from EPRs, including age, gender, diagnoses and drugs. Several predictive models are developed and evaluated using different learning algorithms and feature sets. The highest observed AUC is 0.87, obtained by the random forest algorithm. The resulting model can be used for screening EPRs that are not, but possibly should be, assigned a diagnosis code for the ADE under consideration. Preliminary results from using the model are presented.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2013. Vol. 7885, 125-129 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 7885
Keyword [en]
machine learning, electronic patient records, adverse drug events
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-96366DOI: 10.1007/978-3-642-38326-7_19ISBN: 978-3-642-38325-0 (print)ISBN: 978-3-642-38326-7 (print)OAI: oai:DiVA.org:su-96366DiVA: diva2:665511
Conference
14th Conference on Artificial Intelligence in Medicine, AIME 2013, Murcia, Spain, May 29 – June 1, 2013
Available from: 2013-11-20 Created: 2013-11-20 Last updated: 2017-04-24Bibliographically 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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