1. 25-48 of 2744 « 1 2 3 4 5 ... 113 114 115 »
    1. Classifying Chinese Questions Related to Health Care Posted by Consumers Via the Internet.

      Classifying Chinese Questions Related to Health Care Posted by Consumers Via the Internet.

      Classifying Chinese Questions Related to Health Care Posted by Consumers Via the Internet.

      J Med Internet Res. 2017 Jun 20;19(6):e220

      Authors: Guo H, Na X, Hou L, Li J

      Abstract BACKGROUND: In question answering (QA) system development, question classification is crucial for identifying information needs and improving the accuracy of returned answers. Although the questions are domain-specific, they are asked by non-professionals, making the question classification task more challenging.

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    2. Natural Language Processing for Asthma Ascertainment in Different Practice Settings.

      Natural Language Processing for Asthma Ascertainment in Different Practice Settings.

      Natural Language Processing for Asthma Ascertainment in Different Practice Settings.

      J Allergy Clin Immunol Pract. 2017 Jun 17;:

      Authors: Wi CI, Sohn S, Ali M, Krusemark E, Ryu E, Liu H, Juhn YJ

      Abstract BACKGROUND: We developed and validated NLP-PAC, a natural language processing (NLP) algorithm based on predetermined asthma criteria (PAC) for asthma ascertainment using electronic health records at Mayo Clinic.

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      Mentions: Mayo Clinic NLP Liu
    3. Biomedical text mining for research rigor and integrity: tasks, challenges, directions.

      Biomedical text mining for research rigor and integrity: tasks, challenges, directions.

      Biomedical text mining for research rigor and integrity: tasks, challenges, directions.

      Brief Bioinform. 2017 Jun 13;:

      Authors: Kilicoglu H

      Abstract An estimated quarter of a trillion US dollars is invested in the biomedical research enterprise annually. There is growing alarm that a significant portion of this investment is wasted because of problems in reproducibility of research findings and in the rigor and integrity of research conduct and reporting.

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    4. What Are People Tweeting About Zika? An Exploratory Study Concerning Its Symptoms, Treatment, Transmission, and Prevention.

      What Are People Tweeting About Zika? An Exploratory Study Concerning Its Symptoms, Treatment, Transmission, and Prevention.

      What Are People Tweeting About Zika? An Exploratory Study Concerning Its Symptoms, Treatment, Transmission, and Prevention.

      JMIR Public Health Surveill. 2017 Jun 19;3(2):e38

      Authors: Miller M, Banerjee T, Muppalla R, Romine W, Sheth A

      Abstract BACKGROUND: In order to harness what people are tweeting about Zika, there needs to be a computational framework that leverages machine learning techniques to recognize relevant Zika tweets and, further, categorize these into disease-specific categories to address specific societal concerns related to the prevention, transmission, symptoms, and treatment of Zika virus. OBJECTIVE: The purpose of this study was to determine the relevancy ...

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    5. Data mining and pathway analysis of glucose-6-phosphate dehydrogenase with natural language processing.

      Data mining and pathway analysis of glucose-6-phosphate dehydrogenase with natural language processing.

      Data mining and pathway analysis of glucose-6-phosphate dehydrogenase with natural language processing.

      Mol Med Rep. 2017 Jun 15;:

      Authors: Chen L, Zhang C, Wang Y, Li Y, Han Q, Yang H, Zhu Y

      Abstract Human glucose-6-phosphate dehydrogenase (G6PD) is a crucial enzyme in the pentose phosphate pathway, and serves an important role in biosynthesis and the redox balance.

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      Mentions: Yang H Li Y
    6. DrugSemantics: a corpus for Named Entity Recognition in Spanish Summaries of Product Characteristics.

      DrugSemantics: a corpus for Named Entity Recognition in Spanish Summaries of Product Characteristics.

      DrugSemantics: a corpus for Named Entity Recognition in Spanish Summaries of Product Characteristics.

      J Biomed Inform. 2017 Jun 14;:

      Authors: Moreno I, Boldrini E, Moreda P, Teresa Romá-Ferri M

      Abstract For the healthcare sector, it is critical to exploit the vast amount of textual health-related information. Nevertheless, healthcare providers have difficulties to benefit from such quantity of data during pharmacotherapeutic care.

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    7. De-identification of psychiatric intake records: Overview of 2016 Cegs N-GRID Shared Tasks Track 1.

      De-identification of psychiatric intake records: Overview of 2016 Cegs N-GRID Shared Tasks Track 1.

      De-identification of psychiatric intake records: Overview of 2016 CEGS N-GRID Shared Tasks Track 1.

      J Biomed Inform. 2017 Jun 11;:

      Authors: Stubbs A, Filannino M, Uzuner Ö

      Abstract The 2016 CEGS N-GRID shared tasks for clinical records contained three tracks. Track 1 focused on de-identification of a new corpus of 1,000 psychiatric intake records.

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      Mentions: NLP
    8. How Do You #relax When You're #stressed? A Content Analysis and Infodemiology Study of Stress-Related Tweets.

      How Do You #relax When You're #stressed? A Content Analysis and Infodemiology Study of Stress-Related Tweets.

      How Do You #relax When You're #stressed? A Content Analysis and Infodemiology Study of Stress-Related Tweets.

      JMIR Public Health Surveill. 2017 Jun 13;3(2):e35

      Authors: Doan S, Ritchart A, Perry N, Chaparro JD, Conway M

      Abstract BACKGROUND: Stress is a contributing factor to many major health problems in the United States, such as heart disease, depression, and autoimmune diseases.

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    9. Predicting Mental Conditions Based on "History of Present Illness" in Psychiatric Notes with Deep Neural Networks.

      Predicting Mental Conditions Based on "History of Present Illness" in Psychiatric Notes with Deep Neural Networks.

      Predicting Mental Conditions Based on "History of Present Illness" in Psychiatric Notes with Deep Neural Networks.

      J Biomed Inform. 2017 Jun 09;:

      Authors: Tran T, Kavuluru R

      Abstract BACKGROUND: Applications of natural language processing to mental health notes are not common given the sensitive nature of the associated narratives.

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      Mentions: NLP CNN RNN
    10. Unlocking echocardiogram measurements for heart disease research through natural language processing.

      Unlocking echocardiogram measurements for heart disease research through natural language processing.

      Unlocking echocardiogram measurements for heart disease research through natural language processing.

      BMC Cardiovasc Disord. 2017 Jun 12;17(1):151

      Authors: Patterson OV, Freiberg MS, Skanderson M, J Fodeh S, Brandt CA, DuVall SL

      Abstract BACKGROUND: In order to investigate the mechanisms of cardiovascular disease in HIV infected and uninfected patients, an analysis of echocardiogram reports is required for a large longitudinal multi-center study.

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    11. Exploring convolutional neural networks for drug-drug interaction extraction.

      Exploring convolutional neural networks for drug-drug interaction extraction.

      Exploring convolutional neural networks for drug-drug interaction extraction.

      Database (Oxford). 2017 Jan 01;2017:

      Authors: Suárez-Paniagua V, Segura-Bedmar I, Martínez P

      Abstract Drug-drug interaction (DDI), which is a specific type of adverse drug reaction, occurs when a drug influences the level or activity of another drug.

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      Mentions: Oxford CNN Martínez
    12. Counting trees in Random Forests: Predicting symptom severity in psychiatric intake reports.

      Counting trees in Random Forests: Predicting symptom severity in psychiatric intake reports.

      Counting trees in Random Forests: Predicting symptom severity in psychiatric intake reports.

      J Biomed Inform. 2017 Jun 07;:

      Authors: Scheurwegs E, Sushil M, Tulkens S, Daelemans W, Luyckx K

      Abstract The CEGS N-GRID 2016 Shared Task (Filannino, Stubbs, Uzuner (2017)) in Clinical Natural Language Processing introduces the assignment of a severity score to a psychiatric symptom, based on a psychiatric intake report.

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    13. A hybrid approach to automatic de-identification of psychiatric notes.

      A hybrid approach to automatic de-identification of psychiatric notes.

      A hybrid approach to automatic de-identification of psychiatric notes.

      J Biomed Inform. 2017 Jun 07;:

      Authors: Lee HJ, Wu Y, Zhang Y, Xu J, Xu H, Roberts K

      Abstract De-identification, or identifying and removing protected health information (PHI) from clinical data, is a critical step in making clinical data available for clinical applications and research.

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      Mentions: PHI
    14. Feasibility and Utility of Lexical Analysis for Occupational Health Text.

      Feasibility and Utility of Lexical Analysis for Occupational Health Text.

      Feasibility and Utility of Lexical Analysis for Occupational Health Text.

      J Occup Environ Med. 2017 Jun;59(6):578-587

      Authors: Harber P, Leroy G

      Abstract OBJECTIVE: Assess feasibility and potential utility of natural language processing (NLP) for storing and analyzing occupational health data. METHODS: Basic NLP lexical analysis methods were applied to 89,000 Mine Safety and Health Administration (MSHA) free text records.

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      Mentions: NLP Umls
    15. StandFood: Standardization of Foods Using a Semi-Automatic System for Classifying and Describing Foods According to FoodEx2.

      StandFood: Standardization of Foods Using a Semi-Automatic System for Classifying and Describing Foods According to FoodEx2.

      StandFood: Standardization of Foods Using a Semi-Automatic System for Classifying and Describing Foods According to FoodEx2.

      Nutrients. 2017 May 26;9(6):

      Authors: Eftimov T, Korošec P, Koroušić Seljak B

      Abstract The European Food Safety Authority has developed a standardized food classification and description system called FoodEx2.

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      Mentions: Slovenia
    16. Text Mining of the Electronic Health Record: An Information Extraction Approach for Automated Identification and Subphenotyping of HFpEF Patients for Clinical Trials.

      Text Mining of the Electronic Health Record: An Information Extraction Approach for Automated Identification and Subphenotyping of HFpEF Patients for Clinical Trials.

      Text Mining of the Electronic Health Record: An Information Extraction Approach for Automated Identification and Subphenotyping of HFpEF Patients for Clinical Trials.

      J Cardiovasc Transl Res. 2017 Jun 05;:

      Authors: Jonnalagadda SR, Adupa AK, Garg RP, Corona-Cox J, Shah SJ

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      Mentions: Shah
    17. Language from police body camera footage shows racial disparities in officer respect.

      Language from police body camera footage shows racial disparities in officer respect.

      Language from police body camera footage shows racial disparities in officer respect.

      Proc Natl Acad Sci U S A. 2017 Jun 05;:

      Authors: Voigt R, Camp NP, Prabhakaran V, Hamilton WL, Hetey RC, Griffiths CM, Jurgens D, Jurafsky D, Eberhardt JL

      Abstract Using footage from body-worn cameras, we analyze the respectfulness of police officer language toward white and black community members during routine traffic stops.

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    18. De-identification of Clinical Notes via Recurrent Neural Network and Conditional Random Field.

      De-identification of Clinical Notes via Recurrent Neural Network and Conditional Random Field.

      De-identification of Clinical Notes via Recurrent Neural Network and Conditional Random Field.

      J Biomed Inform. 2017 Jun 01;:

      Authors: Liu Z, Tang B, Wang X, Chen Q

      Abstract De-identification, identifying information from data, such as protected health information (PHI) present in clinical data, is a critical step to enable data to be shared or published.

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      Mentions: NLP PHI
    19. Automatic Recognition of Symptom Severity From Psychiatric Evaluation Records.

      Automatic Recognition of Symptom Severity From Psychiatric Evaluation Records.

      Automatic Recognition of Symptom Severity From Psychiatric Evaluation Records.

      J Biomed Inform. 2017 May 30;:

      Authors: Goodwin TR, Maldonado R, Harabagiu SM

      Abstract This paper presents a novel method for automatically recognizing symptom severity by using natural language processing of psychiatric evaluation records to extract features that are processed by machine learning techniques to assign a severity score to each record evaluated in the 2016 RDoC for Psychiatry Challenge from CEGS/N-GRID. The natural language processing techniques focused on (a) discerning the discourse information expressed in questions and answers; (b) identifying medical concepts that relate to mental disorders; and (c ...

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      Mentions: SVM Harabagiu SM
    20. Predictive Modeling for Classification of Positive Valence System Symptom Severity from Initial Psychiatric Evaluation Records.

      Predictive Modeling for Classification of Positive Valence System Symptom Severity from Initial Psychiatric Evaluation Records.

      Predictive Modeling for Classification of Positive Valence System Symptom Severity from Initial Psychiatric Evaluation Records.

      J Biomed Inform. 2017 May 29;:

      Authors: Posada JD, Barda AJ, Shi L, Xue D, Ruiz V, Kuan PH, Ryan ND, Rich Tsui F

      Abstract In response to the challenges set forth by the CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing, we describe a framework to automatically classify initial psychiatric evaluation records to one of four positive valence system severities: absent, mild, moderate, or severe. We used a dataset provided by the event organizers to develop a framework comprised of natural language ...

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      Mentions: Naïve Bayes NLP Shi
    21. The effects of natural language processing on cross-institutional portability of influenza case detection for disease surveillance.

      The effects of natural language processing on cross-institutional portability of influenza case detection for disease surveillance.

      The effects of natural language processing on cross-institutional portability of influenza case detection for disease surveillance.

      Appl Clin Inform. 2017 May 31;8(2):560-580

      Authors: Ferraro JP, Ye Y, Gesteland PH, Haug PJ, Tsui FR, Cooper GF, Van Bree R, Ginter T, Nowalk AJ, Wagner M

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    22. RysannMD: A Biomedical Semantic Annotator Balancing Speed and Accuracy.

      RysannMD: A Biomedical Semantic Annotator Balancing Speed and Accuracy.

      RysannMD: A Biomedical Semantic Annotator Balancing Speed and Accuracy.

      J Biomed Inform. 2017 May 25;:

      Authors: Cuzzola J, Jovanovic J, Bagheri E

      Abstract Recently, both researchers and practitioners have explored the possibility of semantically annotating large and continuously evolving collections of biomedical texts such as research papers, medical reports, and physician notes in order to enable their efficient and effective management and use in clinical practice or research laboratories. Such annotations can be automatically generated by biomedical semantic annotators - tools that are specifically designed for detecting and disambiguating biomedical concepts mentioned in text. The biomedical community has already presented several ...

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      Mentions: Coder
    23. Scoring best-worst data in unbalanced many-item designs, with applications to crowdsourcing semantic judgments.

      Scoring best-worst data in unbalanced many-item designs, with applications to crowdsourcing semantic judgments.

      Scoring best-worst data in unbalanced many-item designs, with applications to crowdsourcing semantic judgments.

      Behav Res Methods. 2017 May 27;:

      Authors: Hollis G

      Abstract Best-worst scaling is a judgment format in which participants are presented with a set of items and have to choose the superior and inferior items in the set.

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    25-48 of 2744 « 1 2 3 4 5 ... 113 114 115 »
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      Discourse, Entailment, Machine Translation, NER, Parsing, Segmentation, Semantic, Sentiment, Summarization, WSD