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Dimensionality Reduction with Random Indexing: An Application on Adverse Drug Event Detection using Electronic Health Records
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
2014 (engelsk)Inngår i: IEEE 27th International Symposium on Computer-Based Medical Systems, New York: IEEE Computer Society, 2014, 304-307 s.Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Although electronic health records (EHRs) have recently become an important data source for drug safety signals detection, which is usually evaluated in clinical trials, the use of such data is often prohibited by dimensionality and available computer resources. Currently, several methods for reducing dimensionality are developed, used and evaluated within the medical domain. While these methods perform well, the computational cost tends to increase with growing dimensionality. An alternative solution is random indexing, a technique commonly employed in text classification to reduce the dimensionality of large and sparse documents. This study aims to explore how the predictive performance of random forest is affected by dimensionality reduction through random indexing to predict adverse drug reactions (ADEs). Data are extracted from EHRs and the task is to predict whether or not a patient should be assigned an ADE related diagnosis code. Four different dimensionality settings are investigated and their sensitivity, specificity and area under ROC curve are reported for 14 data sets. The results show that for the investigated data sets, the predictive performance is not negatively affected by dimensionality reduction, however, the computational cost is significantly reduced. Therefore, this study concludes that applying random indexing on EHR data reduces the computational cost, while retaining the predictive performance.

sted, utgiver, år, opplag, sider
New York: IEEE Computer Society, 2014. 304-307 s.
Serie
IEEE International Symposium on Computer-Based Medical Systems, ISSN 1063-7125
Emneord [en]
dimensionality reduction, random forest, random indexing, electronic health records, adverse drug events
HSV kategori
Forskningsprogram
data- och systemvetenskap
Identifikatorer
URN: urn:nbn:se:su:diva-110975DOI: 10.1109/CBMS.2014.22ISI: 000345222200060ISBN: 978-1-4799-4435-4 (tryckt)OAI: oai:DiVA.org:su-110975DiVA: diva2:773749
Konferanse
27th IEEE International Symposium on Computer-Based Medical Systems (CBMS), New York, USA, May 27-29, 2014
Tilgjengelig fra: 2014-12-19 Laget: 2014-12-19 Sist oppdatert: 2017-04-24bibliografisk kontrollert
Inngår i avhandling
1. Order in the random forest
Åpne denne publikasjonen i ny fane eller vindu >>Order in the random forest
2017 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Stockholm: Department of Computer and Systems Sciences, Stockholm University, 2017. 76 s.
Serie
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 17-004
Emneord
Machine learning, random forest, ensemble, time series, data series, sequential data, sparse data, high-dimensional data
HSV kategori
Forskningsprogram
data- och systemvetenskap
Identifikatorer
urn:nbn:se:su:diva-142052 (URN)978-91-7649-827-9 (ISBN)978-91-7649-828-6 (ISBN)
Disputas
2017-06-08, L30, NOD-huset, Borgarfjordsgatan 12, Stockholm, 13:00 (engelsk)
Opponent
Veileder
Forskningsfinansiär
Swedish Foundation for Strategic Research , IIS11-0053
Tilgjengelig fra: 2017-05-16 Laget: 2017-04-24 Sist oppdatert: 2017-05-15bibliografisk kontrollert

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