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Identifying adverse drug event information in clinical notes with distributional semantic representations of context
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Karolinska Institutet, Sweden.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2015 (English)In: Journal of Biomedical Informatics, ISSN 1532-0464, E-ISSN 1532-0480, Vol. 57, 333-349 p.Article in journal (Refereed) Published
Abstract [en]

For the purpose of post-marketing drug safety surveillance, which has traditionally relied on the volun- tary reporting of individual cases of adverse drug events (ADEs), other sources of information are now being explored, including electronic health records (EHRs), which give us access to enormous amounts of longitudinal observations of the treatment of patients and their drug use. Adverse drug events, which can be encoded in EHRs with certain diagnosis codes, are, however, heavily underreported. It is therefore important to develop capabilities to process, by means of computational methods, the more unstructured EHR data in the form of clinical notes, where clinicians may describe and reason around suspected ADEs. In this study, we report on the creation of an annotated corpus of Swedish health records for the purpose of learning to identify information pertaining to ADEs present in clinical notes. To this end, three key tasks are tackled: recognizing relevant named entities (disorders, symptoms, drugs), labeling attributes of the recognized entities (negation, speculation, temporality), and relationships between them (indication, adverse drug event). For each of the three tasks, leveraging models of distributional semantics – i.e., unsupervised methods that exploit co-occurrence information to model, typically in vector space, the meaning of words – and, in particular, combinations of such models, is shown to improve the predictive performance. The ability to make use of such unsupervised methods is critical when faced with large amounts of sparse and high-dimensional data, especially in domains where annotated resources are scarce.

Place, publisher, year, edition, pages
2015. Vol. 57, 333-349 p.
Keyword [en]
adverse drug events, electronic health records, corpus annotation, machine learning, distributional semantics, relation extraction
National Category
Computer Science Language Technology (Computational Linguistics)
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-122464DOI: 10.1016/j.jbi.2015.08.013ISI: 000363437500028OAI: oai:DiVA.org:su-122464DiVA: diva2:866463
Projects
High-Performance Data Mining for Drug Effect Detection
Funder
Swedish Foundation for Strategic Research , IIS11-0053
Available from: 2015-11-02 Created: 2015-11-02 Last updated: 2015-11-23Bibliographically approved
In thesis
1. Ensembles of Semantic Spaces: On Combining Models of Distributional Semantics with Applications in Healthcare
Open this publication in new window or tab >>Ensembles of Semantic Spaces: On Combining Models of Distributional Semantics with Applications in Healthcare
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Distributional semantics allows models of linguistic meaning to be derived from observations of language use in large amounts of text. By modeling the meaning of words in semantic (vector) space on the basis of co-occurrence information, distributional semantics permits a quantitative interpretation of (relative) word meaning in an unsupervised setting, i.e., human annotations are not required. The ability to obtain inexpensive word representations in this manner helps to alleviate the bottleneck of fully supervised approaches to natural language processing, especially since models of distributional semantics are data-driven and hence agnostic to both language and domain.

All that is required to obtain distributed word representations is a sizeable corpus; however, the composition of the semantic space is not only affected by the underlying data but also by certain model hyperparameters. While these can be optimized for a specific downstream task, there are currently limitations to the extent the many aspects of semantics can be captured in a single model. This dissertation investigates the possibility of capturing multiple aspects of lexical semantics by adopting the ensemble methodology within a distributional semantic framework to create ensembles of semantic spaces. To that end, various strategies for creating the constituent semantic spaces, as well as for combining them, are explored in a number of studies.

The notion of semantic space ensembles is generalizable across languages and domains; however, the use of unsupervised methods is particularly valuable in low-resource settings, in particular when annotated corpora are scarce, as in the domain of Swedish healthcare. The semantic space ensembles are here empirically evaluated for tasks that have promising applications in healthcare. It is shown that semantic space ensembles – created by exploiting various corpora and data types, as well as by adjusting model hyperparameters such as the size of the context window and the strategy for handling word order within the context window – are able to outperform the use of any single constituent model on a range of tasks. The semantic space ensembles are used both directly for k-nearest neighbors retrieval and for semi-supervised machine learning. Applying semantic space ensembles to important medical problems facilitates the secondary use of healthcare data, which, despite its abundance and transformative potential, is grossly underutilized.

Place, publisher, year, edition, pages
Stockholm: Department of Computer and Systems Sciences, Stockholm University, 2015. 95 p.
Series
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 15-021
Keyword
natural language processing, machine learning, distributional semantics, ensemble learning, semantic space ensembles, medical informatics, electronic health records
National Category
Computer Science Language Technology (Computational Linguistics)
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-122465 (URN)978-91-7649-302-1 (ISBN)
Public defence
2015-12-17, Lilla hörsalen, NOD-huset, Borgarfjordsgatan 12, Kista, 13:00 (English)
Opponent
Supervisors
Projects
High-Performance Data Mining for Drug Effect Detection
Funder
Swedish Foundation for Strategic Research , IIS11-0053
Note

At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 4 and 5: Unpublished conference papers.

Available from: 2015-11-25 Created: 2015-11-02 Last updated: 2015-11-13Bibliographically approved

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