1. 25-48 of 2861 « 1 2 3 4 5 ... 118 119 120 »
    1. Integrating Natural Language Processing and Machine Learning Algorithms to Categorize Oncologic Response in Radiology Reports.

      Integrating Natural Language Processing and Machine Learning Algorithms to Categorize Oncologic Response in Radiology Reports.

      J Digit Imaging. 2017 Oct 27;:

      Authors: Chen PH, Zafar H, Galperin-Aizenberg M, Cook T

      Abstract A significant volume of medical data remains unstructured. Natural language processing (NLP) and machine learning (ML) techniques have shown to successfully extract insights from radiology reports.

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      Mentions: Bayes SVM NLP
    2. Comparing clinician descriptions of frailty and geriatric syndromes using electronic health records: a retrospective cohort study.

      Comparing clinician descriptions of frailty and geriatric syndromes using electronic health records: a retrospective cohort study.

      BMC Geriatr. 2017 Oct 25;17(1):248

      Authors: Anzaldi LJ, Davison A, Boyd CM, Leff B, Kharrazi H

      Abstract BACKGROUND: Geriatric syndromes, including frailty, are common in older adults and associated with adverse outcomes.

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    3. An annotated corpus with nanomedicine and pharmacokinetic parameters.

      An annotated corpus with nanomedicine and pharmacokinetic parameters.

      Int J Nanomedicine. 2017;12:7519-7527

      Authors: Lewinski NA, Jimenez I, McInnes BT

      Abstract A vast amount of data on nanomedicines is being generated and published, and natural language processing (NLP) approaches can automate the extraction of unstructured text-based data. Annotated corpora are a key resource for NLP and information extraction methods which employ machine learning.

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    4. Best Paper Selection.

      Best Paper Selection.

      Yearb Med Inform. 2017 Aug;26(1):233-234

      Authors:

      Abstract Althoff, T, Clark K, Leskovec, J. Large-scale Analysis of Counseling Conversations: An Application of Natural Language Processing to Mental Health. Trans Assoc Comput Linguist 2016(4):463-76 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5361062/ Kilicoglu, H, Demner-Fushman, D. Bio-SCoRes: A Smorgasbord Architecture for Coreference Resolution in Biomedical Text. PLoS One.

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    5. Making Sense of Big Textual Data for Health Care: Findings from the Section on Clinical Natural Language Processing.

      Making Sense of Big Textual Data for Health Care: Findings from the Section on Clinical Natural Language Processing.

      Yearb Med Inform. 2017 Aug;26(1):228-233

      Authors: Névéol A, Zweigenbaum P

      Abstract Objectives: To summarize recent research and present a selection of the best papers published in 2016 in the field of clinical Natural Language Processing (NLP). Method: A survey of the literature was performed by the two section editors of the IMIA Yearbook NLP section.

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      Mentions: NLP
    6. Capturing the Patient's Perspective: a Review of Advances in Natural Language Processing of Health-Related Text.

      Capturing the Patient's Perspective: a Review of Advances in Natural Language Processing of Health-Related Text.

      Yearb Med Inform. 2017 Aug;26(1):214-227

      Authors: Gonzalez-Hernandez G, Sarker A, O'Connor K, Savova G

      Abstract Background: Natural Language Processing (NLP) methods are increasingly being utilized to mine knowledge from unstructured health-related texts.

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    7. Comparing deep neural network and other machine learning algorithms for stroke prediction in a large-scale population-based electronic medical claims database.

      Comparing deep neural network and other machine learning algorithms for stroke prediction in a large-scale population-based electronic medical claims database.

      Conf Proc IEEE Eng Med Biol Soc. 2017 Jul;2017:3110-3113

      Authors: Chen-Ying Hung, Wei-Chen Chen, Po-Tsun Lai, Ching-Heng Lin, Chi-Chun Lee

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      Mentions: EMC DNN
    8. Improving Terminology Mapping in Clinical Text with Context-Sensitive Spelling Correction.

      Improving Terminology Mapping in Clinical Text with Context-Sensitive Spelling Correction.

      Stud Health Technol Inform. 2017;235:241-245

      Authors: Dziadek J, Henriksson A, Duneld M

      Abstract 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.

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    9. The Human Behaviour-Change Project: harnessing the power of artificial intelligence and machine learning for evidence synthesis and interpretation.

      The Human Behaviour-Change Project: harnessing the power of artificial intelligence and machine learning for evidence synthesis and interpretation.

      Implement Sci. 2017 Oct 18;12(1):121

      Authors: Michie S, Thomas J, Johnston M, Aonghusa PM, Shawe-Taylor J, Kelly MP, Deleris LA, Finnerty AN, Marques MM, Norris E, O'Mara-Eves A, West R

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      Mentions: BCI
    10. De-Identification of Medical Narrative Data.

      De-Identification of Medical Narrative Data.

      Stud Health Technol Inform. 2017;244:23-27

      Authors: Foufi V, Gaudet-Blavignac C, Chevrier R, Lovis C

      Abstract Maintaining data security and privacy in an era of cybersecurity is a challenge. The enormous and rapidly growing amount of health-related data available today raises numerous questions about data collection, storage, analysis, comparability and interoperability but also about data protection.

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    11. Mining comorbidity patterns using retrospective analysis of big collection of outpatient records.

      Mining comorbidity patterns using retrospective analysis of big collection of outpatient records.

      Health Inf Sci Syst. 2017 Dec;5(1):3

      Authors: Boytcheva S, Angelova G, Angelov Z, Tcharaktchiev D

      Abstract BACKGROUND: Studying comorbidities of disorders is important for detection and prevention. For discovering frequent patterns of diseases we can use retrospective analysis of population data, by filtering events with common properties and similar significance.

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      Mentions: Bulgaria
    12. Querying EHRs with a Semantic and Entity-Oriented Query Language.

      Querying EHRs with a Semantic and Entity-Oriented Query Language.

      Stud Health Technol Inform. 2017;235:121-125

      Authors: Lelong R, Soualmia L, Dahamna B, Griffon N, Darmoni SJ

      Abstract While the digitization of medical documents has greatly expanded during the past decade, health information retrieval has become a great challenge to address many issues in medical research.

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    13. Interspecies gene function prediction using semantic similarity.

      Interspecies gene function prediction using semantic similarity.

      Interspecies gene function prediction using semantic similarity.

      BMC Syst Biol. 2016 Dec 23;10(Suppl 4):121

      Authors: Yu G, Luo W, Fu G, Wang J

      Abstract BACKGROUND: Gene Ontology (GO) is a collaborative project that maintains and develops controlled vocabulary (or terms) to describe the molecular function, biological roles and cellular location of gene products in a hierarchical ontology. GO also provides GO annotations that associate genes with GO terms.

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      Mentions: Gene Ontology Mouse
    14. Assessing Behavioral Stages From Social Media Data.

      Assessing Behavioral Stages From Social Media Data.

      CSCW Conf Comput Support Coop Work. 2017 Feb-Mar;2017:1320-1333

      Authors: Liu J, Weitzman ER, Chunara R

      Abstract Important work rooted in psychological theory posits that health behavior change occurs through a series of discrete stages. Our work builds on the field of social computing by identifying how social media data can be used to resolve behavior stages at high resolution (e.g.

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      Mentions: Liu
    15. De-identification of medical records using conditional random fields and long short-term memory networks.

      De-identification of medical records using conditional random fields and long short-term memory networks.

      J Biomed Inform. 2017 Oct 12;:

      Authors: Jiang Z, Zhao C, He B, Guan Y, Jiang J

      Abstract The CEGS N-GRID 2016 Shared Task 1 in Clinical Natural Language Processing focuses on the de-identification of psychiatric evaluation records. This paper describes two participating systems of our team, based on conditional random fields (CRFs) and long short-term memory networks (LSTMs).

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    16. A method for named entity normalization in biomedical articles: application to diseases and plants.

      A method for named entity normalization in biomedical articles: application to diseases and plants.

      BMC Bioinformatics. 2017 Oct 13;18(1):451

      Authors: Cho H, Choi W, Lee H

      Abstract BACKGROUND: In biomedical articles, a named entity recognition (NER) technique that identifies entity names from texts is an important element for extracting biological knowledge from articles.

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    17. Segment convolutional neural networks (Seg-CNNs) for classifying relations in clinical notes.

      Segment convolutional neural networks (Seg-CNNs) for classifying relations in clinical notes.

      Segment convolutional neural networks (Seg-CNNs) for classifying relations in clinical notes.

      J Am Med Inform Assoc. 2017 Aug 31;:

      Authors: Luo Y, Cheng Y, Uzuner Ö, Szolovits P, Starren J

      Abstract We propose Segment Convolutional Neural Networks (Seg-CNNs) for classifying relations from clinical notes. Seg-CNNs use only word-embedding features without manual feature engineering.

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    18. A new synonym-substitution method to enrich the human phenotype ontology.

      A new synonym-substitution method to enrich the human phenotype ontology.

      A new synonym-substitution method to enrich the human phenotype ontology.

      BMC Bioinformatics. 2017 Oct 10;18(1):446

      Authors: Taboada M, Rodriguez H, Gudivada RC, Martinez D

      Abstract BACKGROUND: Named entity recognition is critical for biomedical text mining, where it is not unusual to find entities labeled by a wide range of different terms.

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    19. Automatable algorithms to identify nonmedical opioid use using electronic data: a systematic review.

      Automatable algorithms to identify nonmedical opioid use using electronic data: a systematic review.

      Automatable algorithms to identify nonmedical opioid use using electronic data: a systematic review.

      J Am Med Inform Assoc. 2017 Nov 01;24(6):1204-1210

      Authors: Canan C, Polinski JM, Alexander GC, Kowal MK, Brennan TA, Shrank WH

      Abstract Objective: Improved methods to identify nonmedical opioid use can help direct health care resources to individuals who need them.

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    20. NLPReViz: an interactive tool for natural language processing on clinical text.

      NLPReViz: an interactive tool for natural language processing on clinical text.

      NLPReViz: an interactive tool for natural language processing on clinical text.

      J Am Med Inform Assoc. 2017 Jul 22;:

      Authors: Trivedi G, Pham P, Chapman WW, Hwa R, Wiebe J, Hochheiser H

      Abstract The gap between domain experts and natural language processing expertise is a barrier to extracting understanding from clinical text. We describe a prototype tool for interactive review and revision of natural language processing models of binary concepts extracted from clinical notes.

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    21. Bullying in Virtual Learning Communities.

      Bullying in Virtual Learning Communities.

      Bullying in Virtual Learning Communities.

      Adv Exp Med Biol. 2017;989:211-216

      Authors: Nikiforos S, Tzanavaris S, Kermanidis KL

      Abstract Bullying through the internet has been investigated and analyzed mainly in the field of social media. In this paper, it is attempted to analyze bullying in the Virtual Learning Communities using Natural Language Processing (NLP) techniques, mainly in the context of sociocultural learning theories. Therefore four case studies took place.

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      Mentions: NLP
    22. Simplifying drug package leaflets written in Spanish by using word embedding.

      Simplifying drug package leaflets written in Spanish by using word embedding.

      Simplifying drug package leaflets written in Spanish by using word embedding.

      J Biomed Semantics. 2017 Sep 29;8(1):45

      Authors: Segura-Bedmar I, Martínez P

      Abstract BACKGROUND: Drug Package Leaflets (DPLs) provide information for patients on how to safely use medicines. Pharmaceutical companies are responsible for producing these documents.

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      Mentions: Martínez
    25-48 of 2861 « 1 2 3 4 5 ... 118 119 120 »
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      Discourse, Entailment, Machine Translation, NER, Parsing, Segmentation, Semantic, Sentiment, Summarization, WSD