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Temporal weighting of clinical events in electronic health records for pharmacovigilance
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
2015 (engelsk)Inngår i: 2015 IEEE International Conference on Bioinformatics and Biomedicine: Proceedings / [ed] Jun (Luke) Huan et al., IEEE Computer Society, 2015, 375-381 s.Konferansepaper, Publicerat paper (Fagfellevurdert)
Resurstyp
Text
Abstract [en]

Electronic health records (EHRs) have recently been identified as a potentially valuable source for monitoring adverse drug events (ADEs). However, ADEs are heavily under- reported in EHRs. Using machine learning algorithms to automatically detect patients that should have had ADEs reported in their health records is an efficient and effective solution. One of the challenges to that end is how to take into account temporality when using clinical events, which are time stamped in EHRs, as features for machine learning algorithms to exploit. Previous research on this topic suggests that representing EHR data as a bag of temporally weighted clinical events is promising; however, how to assign weights in an optimal manner remains unexplored. In this study, nine different temporal weighting strategies are proposed and evaluated using data extracted from a Swedish EHR database, where the predictive performance of models constructed with the random forest learning algorithm is compared. Moreover, variable importance is analyzed to obtain a deeper understanding as to why a certain weighting strategy is favored over another, as well as which clinical events undergo the biggest changes in importance with the various weighting strategies. The results show that the choice of weighting strategy has a significant impact on the predictive performance for ADE detection, and that the best choice of weighting strategy depends on the target ADE and, specifically, on its dose-dependency.

sted, utgiver, år, opplag, sider
IEEE Computer Society, 2015. 375-381 s.
HSV kategori
Forskningsprogram
data- och systemvetenskap
Identifikatorer
URN: urn:nbn:se:su:diva-123971DOI: 10.1109/BIBM.2015.7359710ISBN: 978-1-4673-6798-1 (tryckt)OAI: oai:DiVA.org:su-123971DiVA: diva2:878615
Konferanse
2015 IEEE International Conference on Bioinformatics and Biomedicine, Washington DC, USA, 9-12 November 2015
Tilgjengelig fra: 2015-12-09 Laget: 2015-12-09 Sist oppdatert: 2017-01-23bibliografisk kontrollert
Inngår i avhandling
1. Learning Predictive Models from Electronic Health Records
Åpne denne publikasjonen i ny fane eller vindu >>Learning Predictive Models from Electronic Health Records
2017 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

The ongoing digitization of healthcare, which has been much accelerated by the widespread adoption of electronic health records, generates unprecedented amounts of clinical data in a readily computable form. This, in turn, affords great opportunities for making meaningful secondary use of clinical data in the endeavor to improve healthcare, as well as to support epidemiology and medical research. To that end, there is a need for techniques capable of effectively and efficiently analyzing large amounts of clinical data. While machine learning provides the necessary tools, learning effective predictive models from electronic health records comes with many challenges due to the complexity of the data. Electronic health records contain heterogeneous and longitudinal data that jointly provides a rich perspective of patient trajectories in the healthcare process. The diverse characteristics of the data need to be properly accounted for when learning predictive models from clinical data. However, how best to represent healthcare data for predictive modeling has been insufficiently studied. This thesis addresses several of the technical challenges involved in learning effective predictive models from electronic health records.

Methods are developed to address the challenges of (i) representing heterogeneous types of data, (ii) leveraging the concept hierarchy of clinical codes, and (iii) modeling the temporality of clinical events. The proposed methods are evaluated empirically in the context of detecting adverse drug events in electronic health records. Various representations of each type of data that account for its unique characteristics are investigated and it is shown that combining multiple representations yields improved predictive performance. It is also demonstrated how the information embedded in the concept hierarchy of clinical codes can be exploited, both for creating enriched feature spaces and for decomposing the predictive task. Moreover, incorporating temporal information leads to more effective predictive models by distinguishing between event occurrences in the patient history. Both single-point representations, using pre-assigned or learned temporal weights, and multivariate time series representations are shown to be more informative than representations in which temporality is ignored. Effective methods for representing heterogeneous and longitudinal data are key for enhancing and truly enabling meaningful secondary use of electronic health records through large-scale analysis of clinical data.

sted, utgiver, år, opplag, sider
Stockholm: Department of Computer and Systems Sciences, Stockholm University, 2017. 82 s.
Serie
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 17-001
Emneord
Data Science, Machine Learning, Predictive Modeling, Data Representation, Health Informatics, Electronic Health Records
HSV kategori
Forskningsprogram
data- och systemvetenskap
Identifikatorer
urn:nbn:se:su:diva-137936 (URN)978-91-7649-682-4 (ISBN)978-91-7649-683-1 (ISBN)
Disputas
2017-03-02, Lilla hörsalen, NOD-huset, Borgarfjordsgatan 12, Kista, 13:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2017-02-07 Laget: 2017-01-13 Sist oppdatert: 2017-02-08bibliografisk kontrollert

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