Ändra sökning
Avgränsa sökresultatet
1 - 14 av 14
RefereraExporteraLänk till träfflistan
Permanent länk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Träffar per sida
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sortering
  • Standard (Relevans)
  • Författare A-Ö
  • Författare Ö-A
  • Titel A-Ö
  • Titel Ö-A
  • Publikationstyp A-Ö
  • Publikationstyp Ö-A
  • Äldst först
  • Nyast först
  • Skapad (Äldst först)
  • Skapad (Nyast först)
  • Senast uppdaterad (Äldst först)
  • Senast uppdaterad (Nyast först)
  • Standard (Relevans)
  • Författare A-Ö
  • Författare Ö-A
  • Titel A-Ö
  • Titel Ö-A
  • Publikationstyp A-Ö
  • Publikationstyp Ö-A
  • Äldst först
  • Nyast först
  • Skapad (Äldst först)
  • Skapad (Nyast först)
  • Senast uppdaterad (Äldst först)
  • Senast uppdaterad (Nyast först)
Markera
Maxantalet träffar du kan exportera från sökgränssnittet är 250. Vid större uttag använd dig av utsökningar.
  • 1. AAl Abdulsalam, Abdulrahman
    et al.
    Velupillai, Sumithra
    Meystre, Stephane
    UtahBMI at SemEval-2016 Task 12: Extracting Temporal Information from Clinical Text2016Ingår i: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), Association for Computational Linguistics , 2016, 1256-1262 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    The 2016 Clinical TempEval continued the 2015 shared task on temporal information extraction with a new evaluation test set. Our team, UtahBMI, participated in all subtasks using machine learning approaches with ClearTK (LIBLINEAR), CRF++ and CRFsuite packages. Our experiments show that CRF-based classifiers yield, in general, higher recall for multi-word spans, while SVM-based classifiers are better at predicting correct attributes of TIMEX3. In addition, we show that an ensemble-based approach for TIMEX3 could yield improved results. Our team achieved competitive results in each subtask with an F1 75.4% for TIMEX3, F1 89.2% for EVENT, F1 84.4% for event relations with document time (DocTimeRel), and F1 51.1% for narrative container (CONTAINS) relations.

  • 2.
    Dalianis, Hercules
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Data- och systemvetenskap, DSV.
    Nilsson, Gunnar
    Velupillai, Sumithra
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Data- och systemvetenskap, DSV.
    Is de-identification of electronic health records possible?: Or can we use health record corpora for research?2009Ingår i: Virtual healthcare interaction: Papers from AAAI fall symposium ; [November 5 - 7, 2009, at the Westin Arlington Gateway in Arlington, Virginia USA], AAAI Press, 2009, 2-3 s.Konferensbidrag (Refereegranskat)
  • 3. Gkotsis, George
    et al.
    Oellrich, Anika
    Hubbard, Tim
    Dobson, Richard
    Liakata, Maria
    Velupillai, Sumithra
    KTH, Skolan för datavetenskap och kommunikation (CSC), Teoretisk datalogi, TCS.
    Dutta, Rina
    The language of mental health problems in social media2016Ingår i: Proceedings of the Third Workshop on Computational Lingusitics and Clinical Psychology, Association for Computational Linguistics , 2016, 63-73 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    Online social media, such as Reddit, has become an important resource to share personal experiences and communicate with others. Among other personal information, some social media users communicate about mental health problems they are experiencing, with the intention of getting advice, support or empathy from other users. Here, we investigate the language of Reddit posts specific to mental health, to define linguistic characteristics that could be helpful for further applications. The latter include attempting to identify posts that need urgent attention due to their nature, e.g. when someone announces their intentions of ending their life by suicide or harming others. Our results show that there are a variety of linguistic features that are discriminative across mental health user communities and that can be further exploited in subsequent classification tasks. Furthermore, while negative sentiment is almost uniformly expressed across the entire data set, we demonstrate that there are also condition-specific vocabularies used in social media to communicate about particular disorders. Source code and related materials are available from: https: //github.com/gkotsis/ reddit-mental-health.

  • 4. Gkotsis, George
    et al.
    Oellrich, Anika
    Velupillai, Sumithra
    KTH, Skolan för datavetenskap och kommunikation (CSC), Teoretisk datalogi, TCS.
    Liakata, Maria
    Hubbard, Tim J. P.
    Dobson, Richard J. B.
    Dutta, Rina
    Characterisation of mental health conditions in social media using Informed Deep Learning2017Ingår i: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The number of people affected by mental illness is on the increase and with it the burden on health and social care use, as well as the loss of both productivity and quality-adjusted life-years. Natural language processing of electronic health records is increasingly used to study mental health conditions and risk behaviours on a large scale. However, narrative notes written by clinicians do not capture first-hand the patients' own experiences, and only record cross-sectional, professional impressions at the point of care. Social media platforms have become a source of 'in the moment' daily exchange, with topics including well- being and mental health. In this study, we analysed posts from the social media platform Reddit and developed classifiers to recognise and classify posts related to mental illness according to 11 disorder themes. Using a neural network and deep learning approach, we could automatically recognise mental illness-related posts in our balenced dataset with an accuracy of 91.08% and select the correct theme with a weighted average accuracy of 71.37%. We believe that these results are a first step in developing methods to characterise large amounts of user-generated content that could support content curation and targeted interventions.

  • 5. Gkotsis, George
    et al.
    Velupillai, Sumithra
    KTH, Skolan för datavetenskap och kommunikation (CSC), Teoretisk datalogi, TCS.
    Oellrich, Anika
    Dean, Harry
    Liakata, Maria
    Dutta, Rina
    Don’t Let Notes Be Misunderstood: A Negation Detection Method for Assessing Risk of Suicide in Mental Health Records2016Ingår i: Proceedings of the Third Workshop on Computational Lingusitics and Clinical Psychology, Association for Computational Linguistics , 2016, 95-105 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    Mental Health Records (MHRs) contain freetext documentation about patients’ suicide and suicidality. In this paper, we address the problem of determining whether grammatic variants (inflections) of the word “suicide” are af- firmed or negated. To achieve this, we populate and annotate a dataset with over 6,000 sentences originating from a large repository of MHRs. The resulting dataset has high InterAnnotator Agreement (κ 0.93). Furthermore, we develop and propose a negation detection method that leverages syntactic features of text1 . Using parse trees, we build a set of basic rules that rely on minimum domain knowledge and render the problem as binary classification (affirmed vs. negated). Since the overall goal is to identify patients who are expected to be at high risk of suicide, we focus on the evaluation of positive (affirmed) cases as determined by our classifier. Our negation detection approach yields a recall (sensitivity) value of 94.6% for the positive cases and an overall accuracy value of 91.9%. We believe that our approach can be integrated with other clinical Natural Language Processing tools in order to further advance information extraction capabilities.

  • 6.
    Grigonyte, Gintare
    et al.
    Stockholms universitet, Humanistiska fakulteten, Institutionen för lingvistik, Avdelningen för datorlingvistik.
    Kvist, Maria
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Wirén, Mats
    Stockholms universitet, Humanistiska fakulteten, Institutionen för lingvistik, Avdelningen för datorlingvistik.
    Velupillai, Sumithra
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Henriksson, Aron
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Swedification patterns of Latin and Greek affixes in clinical text2016Ingår i: Nordic Journal of Linguistics, ISSN 0332-5865, E-ISSN 1502-4717, Vol. 39, nr 1, 5-37 s.Artikel i tidskrift (Refereegranskat)
    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.
    Stockholms universitet, Humanistiska fakulteten, Institutionen för lingvistik, Avdelningen för datorlingvistik.
    Kvist, Maria
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap. Karolinska Institutet, Sweden.
    Velupillai, Sumithra
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Wirén, Mats
    Stockholms universitet, Humanistiska fakulteten, Institutionen för lingvistik, Avdelningen för datorlingvistik.
    Improving Readability of Swedish Electronic Health Records through Lexical Simplification: First Results2014Ingår i: 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 s.Konferensbidrag (Refereegranskat)
    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.
    Stockholms universitet, Humanistiska fakulteten, Institutionen för lingvistik, Avdelningen för datorlingvistik.
    Kvist, Maria
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap. Karolinska Institute, Sweden.
    Velupillai, Sumithra
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
    Wirén, Mats
    Stockholms universitet, Humanistiska fakulteten, Institutionen för lingvistik, Avdelningen för datorlingvistik.
    Spelling Variation of Latin and Greek words in Swedish Medical Text2014Konferensbidrag (Refereegranskat)
  • 9. Kalyanam, Janani
    et al.
    Velupillai, Sumithra
    KTH, Skolan för datavetenskap och kommunikation (CSC), Numerisk Analys och Datalogi, NADA. KTH, Skolan för informations- och kommunikationsteknik (ICT), Data- och systemvetenskap, DSV.
    Conway, Mike
    Lanckriet, Gert
    From Event Detection to Storytelling on Microblogs2016Ingår i: PROCEEDINGS OF THE 2016 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING ASONAM 2016, IEEE, 2016, 437-442 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    The problem of detecting events from content published on microblogs has garnered much interest in recent times. In this paper, we address the questions of what happens after the outbreak of an event in terms of how the event gradually progresses and attains each of its milestones, and how it eventually dissipates. We propose a model based approach to capture the gradual unfolding of an event over time. This enables the model to automatically produce entire timeline trajectories of events from the time of their outbreak to their disappearance. We apply our model on the Twitter messages collected about Ebola during the 2014 outbreak and obtain the progression timelines of several events that occurred during the outbreak. We also compare our model to several existing topic modeling and event detection baselines in literature to demonstrate its efficiency.

  • 10.
    Rosell, Magnus
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Numerisk Analys och Datalogi, NADA.
    Velupillai, Sumithra
    KTH, Skolan för datavetenskap och kommunikation (CSC), Numerisk Analys och Datalogi, NADA.
    Revealing Relations between Open and Closed Answers in Questionnaires through Text Clustering Evaluation2008Ingår i: Proceedings of the Sixth International Language Resources and Evaluation (LREC'08), 2008, 1-7 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    Open answers in questionnaires contain valuable information that is very time-consuming to analyze manually. We present a method forhypothesis generation from questionnaires based on text clustering. Text clustering is used interactively on the open answers, and the usercan explore the cluster contents. The exploration is guided by automatic evaluation of the clusters against a closed answer regarded as acategorization. This simplifies the process of selecting interesting clusters. The user formulates a hypothesis from the relation betweenthe cluster content and the closed answer categorization. We have applied our method on an open answer regarding occupation comparedto a closed answer on smoking habits. With no prior knowledge of smoking habits in different occupation groups we have generated thehypothesis that farmers smoke less than the average. The hypothesis is supported by several separate surveys. Closed answers are easyto analyze automatically but are restricted and may miss valuable aspects. Open answers, on the other hand, fully capture the dynamicsand diversity of possible outcomes. With our method the process of analyzing open answers becomes feasible.

  • 11.
    Rosell, Magnus
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Numerisk Analys och Datalogi, NADA.
    Velupillai, Sumithra
    KTH, Skolan för datavetenskap och kommunikation (CSC), Numerisk Analys och Datalogi, NADA.
    The Impact of Phrases in Document Clustering for Swedish2005Ingår i: Proceedings of the 15th NODALIDA conference, Joensuu 2005 / [ed] Werner, S., 2005, 173-179 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    We have investigated the impact of using phrases in the vector spacemodel for clustering documents in Swedish in different ways. The investigation is carried out on two textsets from different domains: one set of newspaper articles and one set of medical papers.The use of phrases do not improveresults relative the ordinary use ofwords. The results differ significantly between the text types. Thisindicates that one could benefit from different text representations for different domains although a fundamentally different approach probably would be needed.

  • 12. Samuelsson, Y.
    et al.
    Täckström, O.
    Velupillai, Sumithra
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Data- och systemvetenskap, DSV.
    Eklund, J.
    Fišel, M.
    Saers, M.
    Mixing and blending syntactic and semantic dependencies2008Ingår i: CoNLL - Proc. Twelfth Conf. Comput. Nat. Lang. Learn., 2008, 248-252 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    Our system for the CoNLL 2008 shared task uses a set of individual parsers, a set of stand-alone semantic role labellers, and a joint system for parsing and semantic role labelling, all blended together. The system achieved a macro averaged labelled F 1- score of 79.79 (WSJ 80.92, Brown 70.49) for the overall task. The labelled attachment score for syntactic dependencies was 86.63 (WSJ 87.36, Brown 80.77) and the labelled F 1-score for semantic dependencies was 72.94 (WSJ 74.47, Brown 60.18).

  • 13. Velupillai, Sumithra
    et al.
    Dalianis, Hercules
    Hassel, Martin
    Nilsson, Gunnar H.
    Developing a standard for de-identifying electronic patient records written in Swedish: Precision, recall and F-measure in a manual and computerized annotation trial2009Ingår i: International Journal of Medical Informatics, ISSN 1386-5056, E-ISSN 1872-8243, Vol. 78, nr 12, E19-E26 s.Artikel i tidskrift (Refereegranskat)
    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 deidentification 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.

  • 14.
    Velupillai, Sumithra
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Teoretisk datalogi, TCS.
    Mowery, Danielle
    Conway, Mike
    Hurdle, John
    Kious, Brent
    Vocabulary Development To Support Information Extraction of Substance Abuse from Psychiatry Notest2016Ingår i: Proceedings of BioNLP 2016, Association for Computational Linguistics , 2016, 92-101 s.Konferensbidrag (Refereegranskat)
    Abstract [en]

    Extracting information from mental health records can be useful for large-scale clinical studies (e.g., to predict medication adherence or to understand medication effects) in this clinical specialty largely underserved by the Natural Language Processing (NLP) community. Vocabularies that contain medical terms for specific clinical use-cases, such as signs, symptoms, histories, social risk factors, are valuable resources for the development of NLP systems that aid clinicians in extracting information from text. Substance abuse is an important variable for many clinical use-cases, but, to our knowledge, there are no publicly available vocabularies that cover these types of terms. In this study, we apply and combine three methods for generating vocabularies related to substance abuse. We propose a simple and systematic method to generate highly relevant vocabularies and evaluate these vocabularies with respect to size and content, as well as coverage and relevance when applied to authentic psychiatric notes.

1 - 14 av 14
RefereraExporteraLänk till träfflistan
Permanent länk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf