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  • 1.
    Dalianis, Hercules
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Hassel, Martin
    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.
    Skeppstedt, Maria
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Stockholm EPR Corpus: A Clinical Database Used to Improve Health Care2012In: Proceedings of SLCT 2012: The Fourth Swedish Language Technology Conference, 2012, p. 17-18Conference paper (Other academic)
    Abstract [en]

    The care of patients is well documented in health records. Despite being a valuable source of information that could be mined by computers and used to improve health care, health records are not readily available for research. Moreover, the narrative parts of the records are noisy and need to be interpreted by domain experts. In this abstract we describe our experiences of gaining access to a database of electronic health records for research. We also highlight some important issues in this domain and describe a number of possible applications, including comorbidity networks, detection of hospital-acquired infections and adverse drug reactions, as well as diagnosis coding support.

  • 2.
    Dziadek, Juliusz
    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.
    Duneld, Martin
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Improving Terminology Mapping in Clinical Text with Context-Sensitive Spelling Correction2017In: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 235, p. 241-245Article in journal (Refereed)
    Abstract [en]

    The mapping of unstructured clinical text to an ontology facilitates meaningful secondary use of health records but is non-trivial due to lexical variation and the abundance of misspellings in hurriedly produced notes. Here, we apply several spelling correction methods to Swedish medical text and evaluate their impact on SNOMED CT mapping; first in a controlled evaluation using medical literature text with induced errors, followed by a partial evaluation on clinical notes. It is shown that the best-performing method is context-sensitive, taking into account trigram frequencies and utilizing a corpus-based dictionary.

  • 3.
    Henriksson, Aron
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Hassel, Martin
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Optimizing the Dimensionality of Clinical Term Spaces for Improved Diagnosis Coding Support2013In: Proceedings of the 4th International Louhi Workshop on Health Document Text Mining and Information Analysis (Louhi 2013) / [ed] Hanna Suominen, NICTA , 2013Conference paper (Refereed)
    Abstract [en]

    In natural language processing, dimensionality reduction is a common technique to reduce complexity that simultaneously addresses the sparseness property of language. It is also used as a means to capture some latent structure in text, such as the underlying semantics. Dimensionality reduction is an important property of the word space model, not least in random indexing, where the dimensionality is a predefined model parameter. In this paper, we demonstrate the importance of dimensionality optimization and discuss correlations between dimensionality and the size of the vocabulary. This is of particular importance in the clinical domain, where the level of noise in the text leads to a large vocabulary; it may also mitigate the effect of exploding vocabulary sizes when modeling multiword terms as single tokens. A system that automatically assigns diagnosis codes to patient record entries is shown to improve by up to 18 percentage points by manually optimizing the dimensionality.

  • 4.
    Henriksson, Aron
    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.
    Dalianis, Hercules
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Duneld, Martin
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Identifying adverse drug event information in clinical notes with distributional semantic representations of context2015In: Journal of Biomedical Informatics, ISSN 1532-0464, E-ISSN 1532-0480, Vol. 57, p. 333-349Article in journal (Refereed)
    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.

  • 5.
    Henriksson, Aron
    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 University Hospital.
    Hassel, Martin
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Dalianis, Hercules
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Exploration of Adverse Drug Reactions in Semantic Vector Space Models of Clinical Text2012In:  , 2012Conference paper (Refereed)
    Abstract [en]

    A novel method for identifying potential side-effects to medications through large-scale analysis of clinical data is here introduced and evaluated. By calculating distributional similarities for medication-symptom pairs based on co-occurrence information in a large clinical corpus, many known adverse drug reactions are successfully identified. These preliminary results suggest that semantic vector space models of clinical text could also be used to generate hypotheses about potentially unknown adverse drug reactions. In the best model, 50% of the terms in a list of twenty are considered to be conceivable side-effects. Among the medication-symptom pairs, however, diagnostic indications and terms related to the medication in other ways also appear. These relations need to be distinguished in a more refined method for detecting adverse drug reactions.

  • 6.
    Henriksson, Aron
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Moen, Hans
    Skeppstedt, Maria
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Daudaravičius, Vidas
    Duneld, Martin
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Synonym extraction and abbreviation expansion with ensembles of semantic spaces2014In: Journal of Biomedical Semantics, ISSN 2041-1480, E-ISSN 2041-1480, Vol. 5, no 6Article in journal (Refereed)
    Abstract [en]

    Background: Terminologies that account for variation in language use by linking synonyms and abbreviations to their corresponding concept are important enablers of high-quality information extraction from medical texts. Due to the use of specialized sub-languages in the medical domain, manual construction of semantic resources that accurately reflect language use is both costly and challenging, often resulting in low coverage. Although models of distributional semantics applied to large corpora provide a potential means of supporting development of such resources, their ability to isolate synonymy from other semantic relations is limited. Their application in the clinical domain has also only recently begun to be explored. Combining distributional models and applying them to different types of corpora may lead to enhanced performance on the tasks of automatically extracting synonyms and abbreviation-expansion pairs. Results: A combination of two distributional models – Random Indexing and Random Permutation – employed in conjunction with a single corpus outperforms using either of the models in isolation. Furthermore, combining semantic spaces induced from different types of corpora – a corpus of clinical text and a corpus of medical journal articles – further improves results, outperforming a combination of semantic spaces induced from a single source, as well as a single semantic space induced from the conjoint corpus. A combination strategy that simply sums the cosine similarity scores of candidate terms is generally the most profitable out of the ones explored. Finally, applying simple post-processing filtering rules yields substantial performance gains on the tasks of extracting abbreviation-expansion pairs, but not synonyms. The best results, measured as recall in a list of ten candidate terms, for the three tasks are: 0.39 for abbreviations to long forms, 0.33 for long forms to abbreviations, and 0.47 for synonyms. Conclusions: This study demonstrates that ensembles of semantic spaces can yield improved performance on the tasks of automatically extracting synonyms and abbreviation-expansion pairs. This notion, which merits further exploration, allows different distributional models – with different model parameters – and different types of corpora to be combined, potentially allowing enhanced performance to be obtained on a wide range of natural language processing tasks.

  • 7.
    Henriksson, Aron
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Moen, Hans
    Skeppstedt, Maria
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Eklund, Ann-Marie
    Daudaravičius, Vidas
    Hassel, Martin
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Synonym Extraction of Medical Terms from Clinical Text Using Combinations of Word Space Models2012In: Proceedings of the 5th International Symposium on Semantic Mining in Biomedicine (SMBM 2012), 2012, p. 10-17Conference paper (Refereed)
    Abstract [en]

    In information extraction, it is useful to know if two signifiers have the same or very similar semantic content. Maintaining such information in a controlled vocabulary is, however, costly. Here it is demonstrated how synonyms of medical terms can be extracted automatically from a large corpus of clinical text using distributional semantics. By combining Random Indexing and Random Permutation, different lexical semantic aspects are captured, effectively increasing our ability to identify synonymic relations between terms. 44% of 340 synonym pairs from MeSH are successfully extracted in a list of ten suggestions. The models can also be used to map abbreviations to their full-length forms; simple pattern-based filtering of the suggestions yields substantial improvements.

  • 8. Tengstrand, Lisa
    et al.
    Megyesi, Beata
    Henriksson, Aron
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Duneld, Martin
    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.
    EACL - Expansion of Abbreviations in CLinical text2014In: Proceedings of the 3rdWorkshop on Predicting and Improving Text Readability for Target Reader Population, Association for Computational Linguistics , 2014Conference paper (Refereed)
    Abstract [en]

    In the medical domain, especially in clinical texts, non-standard abbreviations are prevalent, which impairs readability for patients. To ease the understanding of the physicians’ notes, abbreviations need to be identified and expanded to their original forms. We present a distributional semantic approach to find candidates of the original form of the abbreviation, and combine this with Levenshtein distance to choose the correct candidate among the semantically related words. We apply the method to radiology reports and medical journal texts, and compare the results to general Swedish. The results show that the correct expansion of the abbreviation can be found in 40% of the cases, an improvement by 24 percentage points compared to the baseline (0.16), and an increase by 22 percentage points compared to using word space models alone (0.18).

  • 9.
    Velupillai, Sumithra
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Dalianis, Hercules
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Hassel, Martin
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Nilsson, Gunnar
    Developing a standard for de-identifying electronic patient records written in Swedish: precision, recall and F-measure in a manual and computerized annotation trial2009In: International Journal of Medical Informatics, ISSN 1386-5056, E-ISSN 1872-8243, Vol. 78, no 12, p. e19-e26Article in journal (Refereed)
    Abstract [en]

    Background

    Electronic patient records (EPRs) contain a large amount of information written in free text. This information is considered very valuable for research but is also very sensitive since the free text parts may contain information that could reveal the identity of a patient. Therefore, methods for de-identifying EPRs are needed. The work presented here aims to perform a manual and automatic Protected Health Information (PHI)-annotation trial for EPRs written in Swedish.

    Methods

    This study consists of two main parts: the initial creation of a manually PHI-annotated gold standard, and the porting and evaluation of an existing de-identification software written for American English to Swedish in a preliminary automatic de-identification trial. Results are measured with precision, recall and F-measure.

    Results

    This study reports fairly high Inter-Annotator Agreement (IAA) results on the manually created gold standard, especially for specific tags such as names. The average IAA over all tags was 0.65 F-measure (0.84 F-measure highest pairwise agreement). For name tags the average IAA was 0.80 F-measure (0.91 F-measure highest pairwise agreement). Porting a de-identification software written for American English to Swedish directly was unfortunately non-trivial, yielding poor results.

    Conclusion

    Developing gold standard sets as well as automatic systems for de-identification tasks in Swedish is feasible. However, discussions and definitions on identifiable information is needed, as well as further developments both on the tag sets and the annotation guidelines, in order to get a reliable gold standard. A completely new de-identification software needs to be developed.

  • 10.
    Velupillai, Sumithra
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Duneld, MartinStockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.Henriksson, AronStockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.Kvist, MariaStockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.Skeppstedt, MariaStockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.Dalianis, HerculesStockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Louhi 2014: Special issue on health text mining and information analysis2015Conference proceedings (editor) (Refereed)
  • 11.
    Velupillai, Sumithra
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Duneld, Martin
    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.
    Skeppstedt, Maria
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Dalianis, Hercules
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Louhi 2014: Special issue on health text mining and information analysis: introduction2015In: BMC Medical Informatics and Decision Making, ISSN 1472-6947, E-ISSN 1472-6947, Vol. 2, no SI, p. 1-3Article in journal (Refereed)
  • 12.
    Velupillai, Sumithra
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Hassel, Martin
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Dalianis, Hercules
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Finding the Parallel: Automatic Dictionary Construction and Identification of Parallel Text Pairs2010In: Using Corpora in Contrastive and Translation Studies / [ed] edited by Richard Xiao, Newcastle: Cambridge Scholars Publishing , 2010Chapter in book (Other academic)
1 - 12 of 12
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