Change search
Refine search result
1 - 33 of 33
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Oldest first
  • Newest first
Select
The maximal number of hits you can export is 250. When you want to export more records please use the 'Create feeds' function.
  • 1. Abrahamsson, Emil
    et al.
    Forni, Timothy
    Skeppstedt, Maria
    Kvist, Maria
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Medical text simplification using synonym replacement: adapting assessment of word difficulty to a compounding language2014In: Proceedings of the 3rdWorkshop on Predicting andImproving Text Readability for Target Reader Populations(PITR) / [ed] Sandra Williams, Stroudsburg: Association for Computational Linguistics , 2014, 57-65 p.Conference paper (Refereed)
  • 2. Ahltorp, Magnus
    et al.
    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.
    Kvist, Maria
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Using text prediction for facilitating input and improving readability of clinical text2013In: MedInfo 2013: Proceedings of the 14th World Congress on Medical and Health Informatics / [ed] Christoph Ulrich Lehmann, Elske Ammenwerth, Christian Nøhr, IOS Press, 2013, 1149-1149 p.Conference paper (Refereed)
    Abstract [en]

    Text prediction has the potential for facilitating and speeding up the documentation work within health care, making it possible for health personnel to allocate less time to documentation and more time to patient care. It also offers a way to produce clinical text with fewer misspellings and abbreviations, increasing readability. We have explored how text prediction can be used for input of clinical text, and how the specific challenges of text prediction in this domain can be addressed. A text prediction prototype was constructed using data from a medical journal and from medical terminologies. This prototype achieved keystroke savings of 26% when evaluated on texts mimicking authentic clinical text. The results are encouraging, indicating that there are feasible methods for text prediction in the clinical domain.

  • 3. Chapman, Wendy W.
    et al.
    Hilert, Dieter
    Velupillai, Sumithra
    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.
    Chapman, Brian
    Conway, Michael
    Tharp, Melissa
    Mowery, Danielle L.
    Deleger, Louise
    Extending the NegEx Lexicon for Multiple Languages2013In: Proceedings of the 14th World Congress on Medical and Health Informatics / [ed] Christoph Ulrich Lehmann, Elske Ammenwerth, Christian Nøhr, IOS Press, 2013, Vol. 192, 677-681 p.Conference paper (Refereed)
    Abstract [en]

    We translated an existing English negation lexicon (NegEx) to Swedish, French, and German and compared the lexicon on corpora from each language. We observed Zipf’s law for all languages, i.e., a few phrases occur a large number of times, and a large number of phrases occur fewer times. Negation triggers “no” and “not” were common for all languages; however, other triggers varied considerably. The lexicon is available in OWL and RDF format and can be extended to other languages. We discuss the challenges in translating negation triggers to other languages and issues in representing multilingual lexical knowledge.

  • 4.
    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.

  • 5.
    Ehrentraut, Claudia
    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.
    Sparrelid, Elda
    Dalianis, Hercules
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Detecting Healthcare-Associated Infections in Electronic Health Records: Evaluation of Machine Learning and Preprocessing Techniques2014In: Proceedings of the 6th International Symposium on Semantic Mining in Biomedicine (SMBM 2014), University of Aveiro , 2014, 3-10 p.Conference paper (Refereed)
    Abstract [en]

    Healthcare-associated infections (HAI) are in- fections that patients acquire in the course of medical treatment. Being a severe pub- lic health problem, detecting and monitoring HAI in healthcare documentation is an impor- tant topic to address. Research on automated systems has increased over the past years, but performance is yet to be enhanced. The dataset in this study consists of 214 records obtained from a Point-Prevalence Survey. The records are manually classified into HAI and NoHAI records. Nine different preprocess- ing steps are carried out on the data. Two learning algorithms, Random Forest (RF) and Support Vector Machines (SVM), are applied to the data. The aim is to determine which of the two algorithms is more applicable to the task and if preprocessing methods will affect the performance. RF obtains the best performance results, yielding an F1 -score of 85% and AUC of 0.85 when lemmatisation is used as a preprocessing technique. Irrespec- tive of which preprocessing method is used, RF yields higher recall values than SVM, with a statistically significant difference for all but one preprocessing method. Regarding each classifier separately, the choice of preprocess- ing method led to no statistically significant improvement in performance results.

  • 6.
    Grigonyte, Gintare
    et al.
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Kvist, Maria
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Wirén, Mats
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Velupillai, Sumithra
    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.
    Swedification patterns of Latin and Greek affixes in clinical text2016In: Nordic Journal of Linguistics, ISSN 0332-5865, E-ISSN 1502-4717, Vol. 39, no 1, 5-37 p.Article in journal (Refereed)
    Abstract [en]

    Swedish medical language is rich with Latin and Greek terminology which has undergone a Swedification since the 1980s. However, many original expressions are still used by clinical professionals. The goal of this study is to obtain precise quantitative measures of how the foreign terminology is manifested in Swedish clinical text. To this end, we explore the use of Latin and Greek affixes in Swedish medical texts in three genres: clinical text, scientific medical text and online medical information for laypersons. More specifically, we use frequency lists derived from tokenised Swedish medical corpora in the three domains, and extract word pairs belonging to types that display both the original and Swedified spellings. We describe six distinct patterns explaining the variation in the usage of Latin and Greek affixes in clinical text. The results show that to a large extent affixes in clinical text are Swedified and that prefixes are used more conservatively than suffixes.

  • 7.
    Grigonyté, Gintaré
    et al.
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Kvist, Maria
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Karolinska Institutet, Sweden.
    Velupillai, Sumithra
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Wirén, Mats
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Improving Readability of Swedish Electronic Health Records through Lexical Simplification: First Results2014In: Proceedings of the 3rd Workshop on Predicting and Improving Text Readability for Target Reader Populations (PITR), Stroudsburg, USA: Association for Computational Linguistics, 2014, 74-83 p.Conference paper (Refereed)
    Abstract [en]

    This paper describes part of an ongoing effort to improve the readability of Swedish electronic health records (EHRs). An EHR contains systematic documentation of a single patient’s medical history across time, entered by healthcare professionals with the purpose of enabling safe and informed care. Linguistically, medical records exemplify a highly specialised domain, which can be superficially characterised as having telegraphic sentences involving displaced or missing words, abundant abbreviations, spelling variations including misspellings, and terminology. We report results on lexical simplification of Swedish EHRs, by which we mean detecting the unknown, out-ofdictionary words and trying to resolve them either as compounded known words, abbreviations or misspellings.

  • 8.
    Grigonyté, Gintaré
    et al.
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Kvist, Maria
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Karolinska Institute, Sweden.
    Velupillai, Sumithra
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Wirén, Mats
    Stockholm University, Faculty of Humanities, Department of Linguistics, Computational Linguistics.
    Spelling Variation of Latin and Greek words in Swedish Medical Text2014Conference paper (Refereed)
  • 9.
    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, 333-349 p.Article 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.

  • 10.
    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.

  • 11.
    Henriksson, Aron
    et al.
    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.
    Kvist, Maria
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Karolinska Institute, Sweden.
    Duneld, Martin
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Conway, Mike
    Corpus-Driven Terminology Development: Populating Swedish SNOMED CT with Synonyms Extracted from Electronic Health Records2013In: Proceedings of the 2013 Workshop on Biomedical Natural Language Processing (BioNLP 2013), Association for Computational Linguistics, 2013, 36-44 p.Conference paper (Refereed)
    Abstract [en]

    The various ways in which one can refer to the same clinical concept needs to be accounted for in a semantic resource such as SNOMED CT. Developing terminological resources manually is, however, prohibitively expensive and likely to result in low coverage, especially given the high variability of language use in clinical text. To support this process, distributional methods can be employed in conjunction with a large corpus of electronic health records to extract synonym candidates for clinical terms. In this paper, we exemplify the potential of our proposed method using the Swedish version of SNOMED CT, which currently lacks synonyms. A medical expert inspects two thousand term pairs generated by two semantic spaces -- one of which models multiword terms in addition to single words -- for one hundred preferred terms of the semantic types disorder and finding.

  • 12.
    Isenius, Niklas
    et al.
    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.
    Kvist, Maria
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Initial Results in the Development of SCAN: a Swedish Clinical Abbreviation Normalizer2012In: CLEFeHealth 2012: The CLEF 2012 Workshop on Cross-Language Evaluation of Methods, Applications, and Resources for eHealth Document Analysis / [ed] Hanna Suominen, Canberra, Australia: NICTA, National ICT Australia and The Australian National University , 2012Conference paper (Refereed)
    Abstract [en]

    Abbreviations are common in clinical documentation, as this type of text is written under time-pressure and serves mostly for internal communication. This study attempts to apply and extend existing rule-based algorithms that have been developed for English and Swedish abbreviation detection, in order to create an abbreviation detection algorithm for Swedish clinical texts that can identify and suggest definitions for abbreviations and acronyms. This can be used as a pre-processing step for further information extraction and text mining models, as well as for readability solutions.

    Through a literature review, a number of heuristics were defined for automatic abbreviation detection. These were used in the construction of the Swedish Clinical Abbreviation Normalizer (SCAN). The heuristics were: a) freely available external resources: a dictionary of general Swedish, a dictionary of medical terms and a dictionary of known Swedish medical abbreviations, b) maximum word lengths (from three to eight characters), and c) heuristics for handling common patterns such as hyphenation. For each token in the text, the algorithm checks whether it is a known word in one of the lexicons, and whether it fulfills the criteria for word length and the created heuristics. The final algorithm was evaluated on a set of 300 Swedish clinical notes from an emergency department at the Karolinska University Hospital, Stockholm. These notes were annotated for abbreviations, a total of 2,050 tokens. This set was annotated by a physician accustomed to reading and writing medical records.

    The algorithm was tested in different variants, where the word lists were modified, heuristics adapted to characteristics found in the texts, and different combinations of word lengths. The best performing version of the algorithm achieved an F-Measure score of 79%, with 76% recall and 81% precision, which is a considerable improvement over the baseline where each token was only matched against the word lists (51% F-measure, 87% recall, 36% precision). Not surprisingly, precision results are higher when the maximum word length is set to the lowest (three), and recall results higher when it is set to the highest (eight).

    Algorithms for rule-based systems, mainly developed for English, can be successfully adapted for abbreviation detection in Swedish medical records. System performance relies heavily on the quality of the external resources, as well as on the created heuristics. In order to improve results, part-of-speech information and/or local context is needed for disambiguation. In the case of Swedish, compounding also needs to be handled.

  • 13.
    Kvist, Maria
    et al.
    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.
    SCAN: a Swedish Clinical Abbreviation Normalizer: further Development and Adaptation to Radiology2014In: Information Access Evaluation. Multilinguality, Multimodality, and Interaction: 5th International Conference of the CLEF Initiative, CLEF 2014, Sheffield, UK, September 15-18, 2014. Proceedings, Cham: Springer, 2014, 62-73 p.Conference paper (Refereed)
  • 14.
    Kvist, Maria
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Karolinska Institutet, Sweden.
    Velupillai, Sumithra
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Professional Language in Swedish Radiology Reports – Characterization for Patient-Adapted Text Simplification2013In: Scandinavian Conference on Health Informatics 2013 / [ed] Gustav Bellika et al., Linköping: Linköping University Electronic Press, 2013, 55-59 p.Conference paper (Refereed)
    Abstract [en]

    In health care, there is a need for patient adaption of clinical text, so that patients can understand their own health records. As a base for construction of automated text simplification tools, characterization of the clinical language is needed. We describe a corpus of 0.43 mill. radiology reports from a University Hospital, characterize it quantitatively and per-form a qualitative content analysis. The results show that a limited set of words and phrases are recurrent in the reports and can be used for exchange to more easy-to-read vocabu-lary. Semantic categories such as body parts, findings, proce-dures, and administrative information can be used in the sim-plification process. This study investigates the potentials and the pitfalls for text simplification of medical Swedish into general Swedish for laymen.

  • 15. Lövestam, Elin
    et al.
    Velupillai, Sumithra
    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.
    Abbreviations in Swedish Clinical Text - use by three professions2014In: Studies in Health Technology and Informatics, ISSN 1879-8365, Vol. 205, 720-724 p.Article in journal (Refereed)
  • 16.
    Skeppstedt, Maria
    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.
    Dalianis, Hercules
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Rule-based Entity Recognition and Coverage of SNOMED CT in Swedish Clinical Text2012In: LREC 2012 8th ELRA Conference on Language Resources and Evaluation: Proceedings, European Language Resources Association (ELRA) , 2012, 1250-1257 p.Conference paper (Refereed)
    Abstract [en]

    Named entity recognition of the clinical entities disorders, findings and body structures is needed for information extraction from unstructured text in health records. Clinical notes from a Swedish emergency unit were annotated and used for evaluating a rule- and terminology-based entity recognition system. This system used different preprocessing techniques for matching terms to SNOMED CT, and, one by one, four other terminologies were added. For the class body structure, the results improved with preprocessing, whereas only small improvements were shown for the classes disorder and finding. The best average results were achieved when all terminologies were used together. The entity body structure was recognised with a precision of 0.74 and a recall of 0.80, whereas lower results were achieved for disorder (precision: 0.75, recall: 0.55) and for finding (precision: 0.57, recall: 0.30). The proportion of entities containing abbreviations were higher for false negatives than for correctly recognised entities, and no entities containing more than two tokens were recognised by the system. Low recall for disorders and findings shows both that additional methods are needed for entity recognition and that there are many expressions in clinical text that are not included in SNOMED CT.

  • 17.
    Skeppstedt, Maria
    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, Sweden; Karolinska Institutet, Sweden.
    Nilsson, Gunnar H.
    Dalianis, Hercules
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Automatic recognition of disorders, findings, pharmaceuticals and body structures from clinical text: An annotation and machine learning study2014In: Journal of Biomedical Informatics, ISSN 1532-0464, E-ISSN 1532-0480, Vol. 49, 148-158 p.Article in journal (Refereed)
    Abstract [en]

    Automatic recognition of clinical entities in the narrative text of health records is useful for constructing applications for documentation of patient care, as well as for secondary usage in the form of medical knowledge extraction. There are a number of named entity recognition studies on English clinical text, but less work has been carried out on clinical text in other languages. This study was performed on Swedish health records, and focused on four entities that are highly relevant for constructing a patient overview and for medical hypothesis generation, namely the entities: Disorder, Finding, Pharmaceutical Drug and Body Structure. The study had two aims: to explore how well named entity recognition methods previously applied to English clinical text perform on similar texts written in Swedish; and to evaluate whether it is meaningful to divide the more general category Medical Problem, which has been used in a number of previous studies, into the two more granular entities, Disorder and Finding. Clinical notes from a Swedish internal medicine emergency unit were annotated for the four selected entity categories, and the inter-annotator agreement between two pairs of annotators was measured, resulting in an average F-score of 0.79 for Disorder, 0.66 for Finding, 0.90 for Pharmaceutical Drug and 0.80 for Body Structure. A subset of the developed corpus was thereafter used for finding suitable features for training a conditional random fields model. Finally, a new model was trained on this subset, using the best features and settings, and its ability to generalise to held-out data was evaluated. This final model obtained an F-score of 0.81 for Disorder, 0.69 for Finding, 0.88 for Pharmaceutical Drug, 0.85 for Body Structure and 0.78 for the combined category Disorder + Finding. The obtained results, which are in line with or slightly lower than those for similar studies on English clinical text, many of them conducted using a larger training data set, show that the approaches used for English are also suitable for Swedish clinical text. However, a small proportion of the errors made by the model are less likely to occur in English text, showing that results might be improved by further tailoring the system to clinical Swedish. The entity recognition results for the individual entities Disorder and Finding show that it is meaningful to separate the general category Medical Problem into these two more granular entity types, e.g. for knowledge mining of co-morbidity relations and disorder-finding relations.

  • 18.
    Svee, Eric-Oluf
    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 Institute, Sweden.
    Velupillai, Sumithra
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Capturing and Representing Values for Requirements of Personal Health Records2013In: PoEM Short Papers: Short Paper Proceedings of the 6th IFIP WG 8.1 Working Conference on the Practice of Enterprise Modeling (PoEM 2013) / [ed] Janis Grabis, Marite Kirikova, Jelena Zdravkovic, Janis Stirna, 2013, 166-175 p.Conference paper (Refereed)
    Abstract [en]

    Patients’ access to their medical records in the form of Personal Health Records (PHRs) is a central part of the ongoing shift in health policy, where patient empowerment is in focus. A survey was conducted to gauge the stakeholder requirements of patients in regards to functionality requests in PHRs. Models from goal-oriented requirements engineering were created to express the values and preferences held by patients in regards to PHRs from this survey. The present study concludes that patient values can be extracted from survey data, allowing the incorporation of values in the common workflow of requirements engineering without extensive reworking.

  • 19.
    Tanushi, Hideyuki
    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.
    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. Karolinska University Hospital.
    Skeppstedt, 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.
    Negation Scope Delimitation in Clinical Text Using Three Approaches: NegEx, PyConTextNLP and SynNeg2013In: Proceedings of the 19th Nordic Conference of Computational Linguistics (NODALIDA 2013) / [ed] Stephan Oepen, Kristin Hagen, Janne Bondi Johannessen, Linköping: Linköping University Electronic Press , 2013, 387-474 p.Conference paper (Refereed)
    Abstract [en]

    Negation detection is a key component in clinical information extraction systems, as health record text contains reasonings in which the physician excludes different diagnoses by negating them. Many systems for negation detection rely on negation cues (e.g. not), but only few studies have investigated if the syntactic structure of the sentences can be used for determining the scope of these cues. We have in this paper compared three different systems for negation detection in Swedish clinical text (NegEx, PyConTextNLP and SynNeg), which have different approaches for determining the scope of negation cues. NegEx uses the distance between the cue and the disease, PyConTextNLP relies on a list of conjunctions limiting the scope of a cue, and in SynNeg the boundaries of the sentence units, provided by a syntactic parser, limit the scope of the cues. The three systems produced similar results, detecting negation with an F-score of around 80%, but using a parser had advantages when handling longer, complex sentences or short sentences with contradictory statements.

  • 20.
    Tanushi, Hideyuki
    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.
    Sparrelid, Elda
    Detection of Healthcare-Associated Urinary Tract Infection in Swedish Electronic Health Records2014In: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 207, 330-339 p.Article in journal (Refereed)
  • 21. 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).

  • 22.
    ul Muntaha, Sidrat
    et al.
    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.
    Kvist, 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.
    Entity Recognition of Pharmaceutical Drugs in Swedish Clinical Text2012In: Proceedings of the Conference, 2012, 77-78 p.Conference paper (Refereed)
    Abstract [en]

    An entity recognition system for expressions of pharmaceutical drugs, based on vocabulary lists from FASS, the Medical Subject Headings and SNOMED~CT, achieved a precision of 94\% and a recall of 74\% when evaluated on assessment texts from Swedish emergency unit health records.

  • 23.
    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.
    Kvist, Maria
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Factuality Levels of Diagnoses in Swedish Clinical Text2011In: User Centred Networked Health Care - Proceedings of MIE 2011 / [ed] Anne Moen, Stig Kjær Andersen, Jos Aarts, Petter Hurlen, 2011, 559-563 p.Conference paper (Refereed)
    Abstract [en]

    Different levels of knowledge certainty, or factuality levels, are expressed in clinical health record documentation. This information is currently not fully exploited, as the subtleties expressed in natural language cannot easily be machine analyzed. Extracting relevant information from knowledge-intensive resources such as electronic health records can be used for improving health care in general by e.g. building automated information access systems. We present an annotation model of six factuality levels linked to diagnoses in Swedish clinical assessments from an emergency ward. Our main findings are that overall agreement is fairly high (0.7/0.58 F-measure, 0.73/0.6 Cohen's κ, Intra/Inter). These distinctions are important for knowledge models, since only approx. 50% of the diagnoses are affirmed with certainty. Moreover, our results indicate that there are patterns inherent in the diagnosis expressions themselves conveying factuality levels, showing that certainty is not only dependent on context cues.

  • 24.
    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, 1-3 p.Article in journal (Refereed)
  • 25.
    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)
  • 26.
    Velupillai, Sumithra
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Ibrahim, Omran
    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 Institute, Sweden.
    Functions for personal health records in Sweden – patient perspectives2013In: Scandinavian Conference on Health Informatics 2013: Copenhagen, Denmark, August 20, 2013 / [ed] Gustav Bellika et al., Linköping: Linköping University Press , 2013, 95-95 p.Conference paper (Refereed)
    Abstract [en]

    As part of the ongoing shift in health policy, with focus on patient empowerment, the Swedish government prioritizes the patients’ access to their medical records. Different models for personal health records (PHR) are suggested.

    Studies have shown difficulties for patients when navigating and understanding the information in their records. Electronic health record systems are physician-oriented and do not include patient-oriented functions. One problem with medical records is that they contain a lot of data which is usually kept as unstructured text in narrative form; this information overload needs to be structured and presented in a manner that patients understand. Furthermore, in order for the PHR to be a supporting tool for patients, there is a need to identify which key functions should be implemented to support patients. Usage of PHR is highly dependent on the information offered and that functions available meet patient needs. In Sweden, little research has been conducted regarding PHR functions  referred by patients. This study addresses the research question “Which PHR functions are preferred by patients living in Sweden?”.

  • 27.
    Velupillai, Sumithra
    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.
    Fine-grained Certainty Level Annotations Used for Coarser-grained E-health Scenarios: Certainty Classication of Diagnostic Statements in Swedish Clinical Text2012In: Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part II / [ed] Alexander Gelbukh, Berlin/Heidelberg: Springer Berlin/Heidelberg, 2012, 450-461 p.Conference paper (Refereed)
    Abstract [en]

    An important task in information access methods is distinguishingfactual information from speculative or negated information.Fine-grained certainty levels of diagnostic statements in Swedish clinicaltext are annotated in a corpus from a medical university hospital.The annotation model has two polarities (positive and negative) andthree certainty levels. However, there are many e-health scenarios wheresuch ne-grained certainty levels are not practical for information extraction.Instead, more coarse-grained groups are needed. We presentthree scenarios: adverse event surveillance, decision support alerts andautomatic summaries and collapse the ne-grained certainty level classi-cations into coarser-grained groups. We build automatic classiers foreach scenario and analyze the results quantitatively. Annotation discrepanciesare analyzed qualitatively through manual corpus analysis. Ourmain ndings are that it is feasible to use a corpus of ne-grained certaintylevel annotations to build classiers for coarser-grained real-worldscenarios: 0.89, 0.91 and 0.8 F-score (overall average).

  • 28.
    Velupillai, Sumithra
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Mowery, Danielle L
    South, Brett R.
    Kvist, 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.
    Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis2015In: IMIA Yearbook of Medical Informatics, ISSN 0943-4747, Vol. 10, 183-193 p.Article in journal (Refereed)
  • 29.
    Velupillai, Sumithra
    et al.
    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.
    Kvist, Maria
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Karolinska Institutet, Sweden.
    Mowery, Danielle
    Chapman, Brian E.
    Dalianis, Hercules
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Chapman, Wendy W.
    Cue-based assertion classification for Swedish clinical text-Developing a lexicon for pyConTextSwe2014In: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 61, no 3, 137-144 p.Article in journal (Refereed)
    Abstract [en]

    Objective: The ability of a cue-based system to accurately assert whether a disorder is affirmed, negated, or uncertain is dependent, in part, on its cue lexicon. In this paper, we continue our study of porting an assertion system (pyConTextNLP) from English to Swedish (pyConTextSwe) by creating an optimized assertion lexicon for clinical Swedish. Methods and material: We integrated cues from four external lexicons, along with generated inflections and combinations. We used subsets of a clinical corpus in Swedish. We applied four assertion classes (definite existence, probable existence, probable negated existence and definite negated existence) and two binary classes (existence yes/no and uncertainty yes/no) to pyConTextSwe. We compared pyConTextSwe's performance with and without the added cues on a development set, and improved the lexicon further after an error analysis. On a separate evaluation set, we calculated the system's final performance. Results: Following integration steps, we added 454 cues to pyConTextSwe. The optimized lexicon developed after an error analysis resulted in statistically significant improvements on the development set (83%F-score, overall). The system's final F-scores on an evaluation set were 81% (overall). For the individual assertion classes, F-score results were 88% (definite existence), 81% (probable existence), 55% (probable negated existence), and 63% (definite negated existence). For the binary classifications existence yes/no and uncertainty yes/no, final system performance was 97%/87% and 78%/86% F-score, respectively. Conclusions: We have successfully ported pyConTextNLP to Swedish (pyConTextSwe). We have created an extensive and useful assertion lexicon for Swedish clinical text, which could form a valuable resource for similar studies, and which is publicly available.

  • 30.
    Velupillai, Sumithra
    et al.
    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.
    Kvist, Maria
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Karolinska University Hospital.
    Mowery, Danielle
    Chapman, Brian E.
    Dalianis, Hercules
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Chapman, Wendy W.
    Porting a Rule-based Assertion Classifier for Clinical Text from English to Swedish2013In: Proceedings of the 4th International Louhi Workshop on Health Document Text Mining and Information Analysis - Louhi 2013 / [ed] Hanna Suominen, NICTA, Australia's ICT Research Centre of Excellence , 2013Conference paper (Refereed)
    Abstract [en]

    An existing rule-based assertion classier is ported from En- glish to Swedish: pyConTextSwe. Evaluation on Swedish clinical texts shows that the English lexical resources are useful, but that there are assertion cues not obtainable in existing resources. Iterative error cor- rection of cue lexicons improves results for the ported classier. Overall nal results are 82% F-score on a development set and 74% on a test set.

  • 31.
    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.

  • 32.
    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.

  • 33.
    Zhao, Jing
    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. Karolinska Institute, Sweden.
    Asker, Lars
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Boström, Henrik
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Handling Temporality of Clinical Events for Drug Safety Surveillance2015In: AMIA Annual Symposium Proceedings, ISSN 1559-4076, Vol. 2015, 1371-1380 p.Article in journal (Refereed)
    Abstract [en]

    Using longitudinal data in electronic health records (EHRs) for post-marketing adverse drug event (ADE) detection allows for monitoring patients throughout their medical history. Machine learning methods have been shown to be efficient and effective in screening health records and detecting ADEs. How best to exploit historical data, as encoded by clinical events in EHRs is, however, not very well understood. In this study, three strategies for handling temporality of clinical events are proposed and evaluated using an EHR database from Stockholm, Sweden. The random forest learning algorithm is applied to predict fourteen ADEs using clinical events collected from different lengths of patient history. The results show that, in general, including longer patient history leads to improved predictive performance, and that assigning weights to events according to time distance from the ADE yields the biggest improvement.

1 - 33 of 33
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf