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  • 1.
    Dalianis, Hercules
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Henriksson, Aron
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Kvist, Maria
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Velupillai, Sumithra
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Weegar, Rebecka
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    HEALTH BANK - A Workbench for Data Science Applications in Healthcare2015In: Industry Track Workshop, CEUR Workshop Proceedings , 2015, Vol. 1381, 1-18 p.Conference paper (Refereed)
    Abstract [en]

    The enormous amounts of data that are generated in the healthcare process and stored in electronic health record (EHR) systems are an underutilized resource that, with the use of data science applica- tions, can be exploited to improve healthcare. To foster the development and use of data science applications in healthcare, there is a fundamen- tal need for access to EHR data, which is typically not readily available to researchers and developers. A relatively rare exception is the large EHR database, the Stockholm EPR Corpus, comprising data from more than two million patients, that has been been made available to a lim- ited group of researchers at Stockholm University. Here, we describe a number of data science applications that have been developed using this database, demonstrating the potential reuse of EHR data to support healthcare and public health activities, as well as facilitate medical re- search. However, in order to realize the full potential of this resource, it needs to be made available to a larger community of researchers, as well as to industry actors. To that end, we envision the provision of an in- frastructure around this database called HEALTH BANK – the Swedish Health Record Research Bank. It will function both as a workbench for the development of data science applications and as a data explo- ration tool, allowing epidemiologists, pharmacologists and other medical researchers to generate and evaluate hypotheses. Aggregated data will be fed into a pipeline for open e-access, while non-aggregated data will be provided to researchers within an ethical permission framework. We believe that HEALTH BANK has the potential to promote a growing industry around the development of data science applications that will ultimately increase the efficiency and effectiveness of healthcare.

  • 2.
    Ludovici, Michelle
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Weegar, Rebecka
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    A Sentiment model for Swedish with automatically created training data and handlers for language specific traits2016Conference paper (Refereed)
  • 3. Perez, Alicia
    et al.
    Weegar, Rebecka
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Casillas, Arantza
    Gojenola, Koldo
    Oronoz, Maite
    Dalianis, Hercules
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Semi-supervised medical entity recognition: A study on Spanish and Swedish clinical corpora2017In: Journal of Biomedical Informatics, ISSN 1532-0464, E-ISSN 1532-0480, Vol. 71, 16-30 p.Article in journal (Refereed)
    Abstract [en]

    Objective: The goal of this study is to investigate entity recognition within Electronic Health Records (EHRs) focusing on Spanish and Swedish. Of particular importance is a robust representation of the entities. In our case, we utilized unsupervised methods to generate such representations. Methods: The significance of this work stands on its experimental layout. The experiments were carried out under the same conditions for both languages. Several classification approaches were explored: maximum probability, CRF, Perceptron and SVM. The classifiers were enhanced by means of ensembles of semantic spaces and ensembles of Brown trees. In order to mitigate sparsity of data, without a significant increase in the dimension of the decision space, we propose the use of clustered approaches of the hierarchical Brown clustering represented by trees and vector quantization for each semantic space. Results: The results showed that the semi-supervised approaches significantly improved standard supervised techniques for both languages. Moreover, clustering the semantic spaces contributed to the quality of the entity recognition while keeping the dimension of the feature-space two orders of magnitude lower than when directly using the semantic spaces. Conclusions: The contributions of this study are: (a) a set of thorough experiments that enable comparisons regarding the influence of different types of features on different classifiers, exploring two languages other than English; and (b) the use of ensembles of clusters of Brown trees and semantic spaces on EHRs to tackle the problem of scarcity of available annotated data.

  • 4.
    Velupillai, Sumithra
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Weegar, Rebecka
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Kvist, Maria
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Temporal Annotation of Swedish Intensive Care Notes2016Conference paper (Refereed)
    Abstract [en]

    We describe the creation of a corpus of Swedish intensive care unit (ICU) notes annotated for temporal expressions. Clinical notes from an ICU in Stockholm, Sweden were used. The HeidelTime system was adapted to develop Swedish clinical time expression (TIMEX3) resources. Overall micro-average Inter-Annotator Agreement is high (86% F1). We have created Swedish lexical resources with clinically specific time expressions that will be useful for the development of a Swedish clinical text temporal reasoning system.

  • 5.
    Weegar, Rebecka
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Casillas, Arantza
    Diaz de Ilarraza, Arantza
    Oronoz, Maite
    Pérez, Alicia
    Gojenola, Koldo
    The impact of simple feature engineering in multilingual medical NER2016In: Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP), 2016, W16-4201Conference paper (Refereed)
    Abstract [en]

    The goal of this paper is to examine the impact of simple feature engineering mechanisms before applying more sophisticated techniques to the task of medical NER. Sometimes papers using scientifically sound techniques present raw baselines that could be improved adding simple and cheap features. This work focuses on entity recognition for the clinical domain for three languages: English, Swedish and Spanish. The task is tackled using simple features, starting from the window size, capitalization, prefixes, and moving to POS and semantic tags. This work demonstrates that a simple initial step of feature engineering can improve the baseline results significantly. Hence, the contributions of this paper are: first, a short list of guidelines well supported with experimental results on three languages and, second, a detailed description of the relevance of these features for medical NER.

  • 6.
    Weegar, Rebecka
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Kvist, Maria
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Karolinska Institutet, Sweden.
    Sundström, Karin
    Brunak, Søren
    Dalianis, Hercules
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Finding Cervical Cancer Symptoms in Swedish Clinical Text using a Machine Learning Approach and NegEx2015In: AMIA Annual Symposium Proceedings, American Medical Informatics Association , 2015, 1296-1305 p.Conference paper (Refereed)
    Abstract [en]

    Detection of early symptoms in cervical cancer is crucial for early treatment and survival. To find symptoms of cervical cancer in clinical text, Named Entity Recognition is needed. In this paper the Clinical Entity Finder, a machine-learning tool trained on annotated clinical text from a Swedish internal medicine emergency unit, is evaluated on cervical cancer records. The Clinical Entity Finder identifies entities of the types body part, finding and disorder and is extended with negation detection using the rule-based tool NegEx, to distinguish between negated and non-negated entities. To measure the performance of the tools on this new domain, two physicians annotated a set of clinical notes from the health records of cervical cancer patients. The inter-annotator agreement for finding, disorder and body part obtained an average F-score of 0.677 and the Clinical Entity Finder extended with NegEx had an average F-score of 0.667.

1 - 6 of 6
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