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Modeling Heterogeneous Clinical Sequence Data in Semantic Space for Adverse Drug Event Detection
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.
(English)In: IEEE International Conference on Data Science and Advanced Analytics (DSAA), IEEE conference proceedingsConference paper, Published paper (Refereed)
Abstract [en]

The enormous amounts of data that are continuously recorded in electronic health record systems offer ample opportunities for data science applications to improve healthcare. There are, however, challenges involved in using such data for machine learning, such as high dimensionality and sparsity, as well as an inherent heterogeneity that does not allow the distinct types of clinical data to be treated in an identical manner. On the other hand, there are also similarities across data types that may be exploited, e.g., the possibility of representing some of them as sequences. Here, we apply the notions underlying distributional semantics, i.e., methods that model the meaning of words in semantic (vector) space on the basis of co-occurrence information, to four distinct types of clinical data: free-text notes, on the one hand, and clinical events, in the form of diagnosis codes, drug codes and measurements, on the other hand. Each semantic space contains continuous vector representations for every unique word and event, which can then be used to create representations of, e.g., care episodes that, in turn, can be exploited by the learning algorithm. This approach does not only reduce sparsity, but also takes into account, and explicitly models, similarities between various items, and it does so in an entirely data-driven fashion. Here, we report on a series of experiments using the random forest learning algorithm that demonstrate the effectiveness, in terms of accuracy and area under ROC curve, of the proposed representation form over the commonly used bag-of-items counterpart. The experiments are conducted on 27 real datasets that each involves the (binary) classification task of detecting a particular adverse drug event. It is also shown that combining structured and unstructured data leads to significant improvements over using only one of them.

Place, publisher, year, edition, pages
IEEE conference proceedings.
Keyword [en]
distributional semantics, semantic space ensembles, heterogeneous data, electronic health records, adverse drug events, predictive modeling
National Category
Computer Science Language Technology (Computational Linguistics)
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-122462OAI: oai:DiVA.org:su-122462DiVA: diva2:866460
Conference
IEEE International Conference on Data Science and Advanced Analytics (DSAA), 19-21 October, Paris
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: 2015-11-03
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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