Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Predicting Adverse Drug Events using Heterogeneous Event Sequences
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
2016 (Engelska)Ingår i: 2016 IEEE International Conference on Healthcare Informatics (ICHI), IEEE Computer Society, 2016, 356-362 s.Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
IEEE Computer Society, 2016. 356-362 s.
Nyckelord [en]
Adverse drug events, temporal patterns, data series, ensemble methods, random forest
Nationell ämneskategori
Systemvetenskap, informationssystem och informatik
Forskningsämne
data- och systemvetenskap
Identifikatorer
URN: urn:nbn:se:su:diva-135439DOI: 10.1109/ICHI.2016.64ISBN: 978-1-5090-6117-4 (digital)OAI: oai:DiVA.org:su-135439DiVA: diva2:1045223
Konferens
IEEE International Conference on Health Care Informatics, Chicago, Illinois, USA, October 4-7, 2016
Tillgänglig från: 2016-11-08 Skapad: 2016-11-08 Senast uppdaterad: 2017-04-28Bibliografiskt granskad
Ingår i avhandling
1. Order in the random forest
Öppna denna publikation i ny flik eller fönster >>Order in the random forest
2017 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
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
Nyckelord
Machine learning, random forest, ensemble, time series, data series, sequential data, sparse data, high-dimensional data
Nationell ämneskategori
Data- och informationsvetenskap
Forskningsämne
data- och systemvetenskap
Identifikatorer
urn:nbn:se:su:diva-142052 (URN)978-91-7649-827-9 (ISBN)978-91-7649-828-6 (ISBN)
Disputation
2017-06-08, L30, NOD-huset, Borgarfjordsgatan 12, Stockholm, 13:00 (Engelska)
Opponent
Handledare
Forskningsfinansiär
Stiftelsen för strategisk forskning (SSF), IIS11-0053
Tillgänglig från: 2017-05-16 Skapad: 2017-04-24 Senast uppdaterad: 2017-05-15Bibliografiskt granskad

Open Access i DiVA

Fulltext saknas

Övriga länkar

Förlagets fulltext

Sök vidare i DiVA

Av författaren/redaktören
Karlsson, IsakBoström, Henrik
Av organisationen
Institutionen för data- och systemvetenskap
Systemvetenskap, informationssystem och informatik

Sök vidare utanför DiVA

GoogleGoogle Scholar

Altmetricpoäng

Totalt: 58 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf