1. 49-72 of 2744 « 1 2 3 4 5 6 ... 113 114 115 »
    1. Identifying reports of randomized controlled trials (RCTs) via a hybrid machine learning and crowdsourcing approach.

      Identifying reports of randomized controlled trials (RCTs) via a hybrid machine learning and crowdsourcing approach.

      Identifying reports of randomized controlled trials (RCTs) via a hybrid machine learning and crowdsourcing approach.

      J Am Med Inform Assoc. 2017 May 25;:

      Authors: Wallace BC, Noel-Storr A, Marshall IJ, Cohen AM, Smalheiser NR, Thomas J

      Abstract Objectives: Identifying all published reports of randomized controlled trials (RCTs) is an important aim, but it requires extensive manual effort to separate RCTs from non-RCTs, even using current machine learning (ML) approaches.

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    2. Integrating natural language processing expertise with patient safety event review committees to improve the analysis of medication events.

      Integrating natural language processing expertise with patient safety event review committees to improve the analysis of medication events.

      Integrating natural language processing expertise with patient safety event review committees to improve the analysis of medication events.

      Int J Med Inform. 2017 May 11;:

      Authors: Fong A, Harriott N, Walters DM, Foley H, Morrissey R, Ratwani RR

      Abstract OBJECTIVES: Many healthcare providers have implemented patient safety event reporting systems to better understand and improve patient safety.

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      Mentions: NLP
    3. Upper gastrointestinal complications following ablation therapy for atrial fibrillation.

      Upper gastrointestinal complications following ablation therapy for atrial fibrillation.

      Upper gastrointestinal complications following ablation therapy for atrial fibrillation.

      Neurogastroenterol Motil. 2017 May 19;:

      Authors: Park SY, Camilleri M, Packer D, Monahan K

      Abstract BACKGROUND: Following ablation therapy for cardiac arrhythmias, patients may develop upper gastrointestinal (UGI) symptoms. The vagus nerve is close to the atria and may be affected by ablating energy.

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    4. Semantic Technologies for Re-Use of Clinical Routine Data.

      Semantic Technologies for Re-Use of Clinical Routine Data.

      Semantic Technologies for Re-Use of Clinical Routine Data.

      Stud Health Technol Inform. 2017;236:24-31

      Authors: Kreuzthaler M, Martínez-Costa C, Kaiser P, Schulz S

      Abstract Routine patient data in electronic patient records are only partly structured, and an even smaller segment is coded, mainly for administrative purposes. Large parts are only available as free text.

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    5. Concept-Based Retrieval from Critical Incident Reports.

      Concept-Based Retrieval from Critical Incident Reports.

      Concept-Based Retrieval from Critical Incident Reports.

      Stud Health Technol Inform. 2017;236:1-7

      Authors: Denecke K

      Abstract BACKGROUND: Critical incident reporting systems (CIRS) are used as a means to collect anonymously entered information of incidents that occurred for example in a hospital. Analyzing this information helps to identify among others problems in the workflow, in the infrastructure or in processes.

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    6. Enhancing Comparative Effectiveness Research With Automated Pediatric Pneumonia Detection in a Multi-Institutional Clinical Repository: A Phis+ Pilot Study.

      Enhancing Comparative Effectiveness Research With Automated Pediatric Pneumonia Detection in a Multi-Institutional Clinical Repository: A Phis+ Pilot Study.

      Enhancing Comparative Effectiveness Research With Automated Pediatric Pneumonia Detection in a Multi-Institutional Clinical Repository: A PHIS+ Pilot Study.

      J Med Internet Res. 2017 May 15;19(5):e162

      Authors: Meystre S, Gouripeddi R, Tieder J, Simmons J, Srivastava R, Shah S

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      Mentions: NLP Shah
    7. Ordinal Convolutional Neural Networks for Predicting RDoC Positive Valence Psychiatric Symptom Severity Scores.

      Ordinal Convolutional Neural Networks for Predicting RDoC Positive Valence Psychiatric Symptom Severity Scores.

      Ordinal Convolutional Neural Networks for Predicting RDoC Positive Valence Psychiatric Symptom Severity Scores.

      J Biomed Inform. 2017 May 12;:

      Authors: Rios A, Kavuluru R

      Abstract BACKGROUND: The CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing (NLP) provided a set of 1000 neuropsychiatric notes to participants as part of a competition to predict psychiatric symptom severity scores.

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      Mentions: CNN
    8. HealthRecSys: A semantic content-based recommender system to complement health videos.

      HealthRecSys: A semantic content-based recommender system to complement health videos.

      HealthRecSys: A semantic content-based recommender system to complement health videos.

      BMC Med Inform Decis Mak. 2017 May 15;17(1):63

      Authors: Sanchez Bocanegra CL, Sevillano Ramos JL, Rizo C, Civit A, Fernandez-Luque L

      Abstract BACKGROUND: The Internet, and its popularity, continues to grow at an unprecedented pace.

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    9. Automated surveillance of healthcare-associated infections: state of the art.

      Automated surveillance of healthcare-associated infections: state of the art.

      Automated surveillance of healthcare-associated infections: state of the art.

      Curr Opin Infect Dis. 2017 May 12;:

      Authors: Sips ME, Bonten MJM, van Mourik MSM

      Abstract PURPOSE OF REVIEW: This review describes recent advances in the field of automated surveillance of healthcare-associated infections (HAIs), with a focus on data sources and the development of semiautomated or fully automated algorithms.

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      Mentions: HAI
    10. Annotating Logical Forms for EHR Questions.

      Annotating Logical Forms for EHR Questions.

      Annotating Logical Forms for EHR Questions.

      LREC Int Conf Lang Resour Eval. 2016 May;2016:3772-3778

      Authors: Roberts K, Demner-Fushman D

      Abstract This paper discusses the creation of a semantically annotated corpus of questions about patient data in electronic health records (EHRs). The goal is to provide the training data necessary for semantic parsers to automatically convert EHR questions into a structured query.

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      Mentions: NLP EHR Roberts K
    11. A method for cohort selection of cardiovascular disease records from an electronic health record system.

      A method for cohort selection of cardiovascular disease records from an electronic health record system.

      A method for cohort selection of cardiovascular disease records from an electronic health record system.

      Int J Med Inform. 2017 Jun;102:138-149

      Authors: Abrahão MTF, Nobre MRC, Gutierrez MA

      Abstract INTRODUCTION: An electronic healthcare record (EHR) system, when used by healthcare providers, improves the quality of care for patients and helps to lower costs.

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      Mentions: São Paulo EHR
    12. Effective Information Extraction Framework for Heterogeneous Clinical Reports Using Online Machine Learning and Controlled Vocabularies.

      Effective Information Extraction Framework for Heterogeneous Clinical Reports Using Online Machine Learning and Controlled Vocabularies.

      Effective Information Extraction Framework for Heterogeneous Clinical Reports Using Online Machine Learning and Controlled Vocabularies.

      JMIR Med Inform. 2017 May 09;5(2):e12

      Authors: Zheng S, Lu JJ, Ghasemzadeh N, Hayek SS, Quyyumi AA, Wang F

      Abstract BACKGROUND: Extracting structured data from narrated medical reports is challenged by the complexity of heterogeneous structures and vocabularies and often requires significant manual effort.

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    13. The UAB Informatics Institute and 2016 Cegs N-GRID De-Identification Shared Task Challenge.

      The UAB Informatics Institute and 2016 Cegs N-GRID De-Identification Shared Task Challenge.

      The UAB Informatics Institute and 2016 CEGS N-GRID De-Identification Shared Task Challenge.

      J Biomed Inform. 2017 May 03;:

      Authors: Bui DDA, Wyatt M, Cimino JJ

      Abstract Clinical narratives (the text notes found in patients' medical records) are important information sources for secondary use in research. However, in order to protect patient privacy, they must be de-identified prior to use.

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      Mentions: Birmingham Hipaa UAB
    14. Focal Cystic Pancreatic Lesion Follow-up Recommendations After Publication of ACR White Paper on Managing Incidental Findings.

      Focal Cystic Pancreatic Lesion Follow-up Recommendations After Publication of ACR White Paper on Managing Incidental Findings.

      Focal Cystic Pancreatic Lesion Follow-up Recommendations After Publication of ACR White Paper on Managing Incidental Findings.

      J Am Coll Radiol. 2017 May 02;:

      Authors: Bobbin MD, Ip IK, Sahni VA, Shinagare AB, Khorasani R

      Abstract PURPOSE: To describe the variation in radiologists' follow-up recommendations for focal cystic pancreatic lesions (FCPL) after publication of the 2010 ACR incidental findings White Paper and determine adherence to guidance of the ACR Incidental Findings Committee. METHODS: Institutional Review Board approval was obtained for this retrospective, HIPAA-compliant observational study. Patients with FCPL were identified from abdominal CT and MRI reports generated in 2013 using natural ...

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    15. Provider-specific quality measurement for ERCP using natural language processing.

      Provider-specific quality measurement for ERCP using natural language processing.

      Provider-specific quality measurement for ERCP using natural language processing.

      Gastrointest Endosc. 2017 May 02;:

      Authors: Imler TD, Sherman S, Imperiale TF, Xu H, Ouyang F, Beesley C, Hilton C, Coté GA

      Abstract BACKGROUND: Natural language processing (NLP) is an information retrieval technique that has been shown to accurately identify quality measures for colonoscopy.

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      Mentions: NLP
    16. Converting Text to Structured Models of Healthcare Services.

      Converting Text to Structured Models of Healthcare Services.

      Converting Text to Structured Models of Healthcare Services.

      Stud Health Technol Inform. 2016;226:123-6

      Authors: Despotou G, Matragkas N, Arvanitis TN

      Abstract The paper presents a concise method for transforming textual representations of healthcare services, to a structured, semantically unambiguous modelling language. The method is designed based on literature, as well as trial and error by the authors, using text descriptions of healthcare services.

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    17. An Innovative Method for Monitoring Food Quality and the Healthfulness of Consumers' Grocery Purchases.

      An Innovative Method for Monitoring Food Quality and the Healthfulness of Consumers' Grocery Purchases.

      An Innovative Method for Monitoring Food Quality and the Healthfulness of Consumers' Grocery Purchases.

      Nutrients. 2017 May 05;9(5):

      Authors: Tran LT, Brewster PJ, Chidambaram V, Hurdle JF

      Abstract This study presents a method laying the groundwork for systematically monitoring food quality and the healthfulness of consumers' point-of-sale grocery purchases.

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      Mentions: America
    18. Olelo: a web application for intuitive exploration of biomedical literature.

      Olelo: a web application for intuitive exploration of biomedical literature.

      Olelo: a web application for intuitive exploration of biomedical literature.

      Nucleic Acids Res. 2017 May 03;:

      Authors: Kraus M, Niedermeier J, Jankrift M, Tietböhl S, Stachewicz T, Folkerts H, Uflacker M, Neves M

      Abstract Researchers usually query the large biomedical literature in PubMed via keywords, logical operators and filters, none of which is very intuitive. Question answering systems are an alternative to keyword searches.

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    19. A Machine Learning Approach to Evaluating Illness-Induced Religious Struggle.

      A Machine Learning Approach to Evaluating Illness-Induced Religious Struggle.

      A Machine Learning Approach to Evaluating Illness-Induced Religious Struggle.

      Biomed Inform Insights. 2017;9:1178222616686067

      Authors: Glauser J, Connolly B, Nash P, Grossoehme DH

      Abstract Religious or spiritual struggles are clinically important to health care chaplains because they are related to poorer health outcomes, involving both mental and physical health problems. Identifying persons experiencing religious struggle poses a challenge for chaplains.

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    20. TwiMed: Twitter and PubMed Comparable Corpus of Drugs, Diseases, Symptoms, and Their Relations.

      TwiMed: Twitter and PubMed Comparable Corpus of Drugs, Diseases, Symptoms, and Their Relations.

      TwiMed: Twitter and PubMed Comparable Corpus of Drugs, Diseases, Symptoms, and Their Relations.

      JMIR Public Health Surveill. 2017 May 03;3(2):e24

      Authors: Alvaro N, Miyao Y, Collier N

      Abstract BACKGROUND: Work on pharmacovigilance systems using texts from PubMed and Twitter typically target at different elements and use different annotation guidelines resulting in a scenario where there is no comparable set of documents from both Twitter and PubMed annotated in the same manner.

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      Mentions: Miyao Y NLP
    21. Canary: An NLP Platform for Clinicians and Researchers.

      Canary: An NLP Platform for Clinicians and Researchers.

      Canary: An NLP Platform for Clinicians and Researchers.

      Appl Clin Inform. 2017 May 03;8(2):447-453

      Authors: Malmasi S, Sandor NL, Hosomura N, Goldberg M, Skentzos S, Turchin A

      Abstract Information Extraction methods can help discover critical knowledge buried in the vast repositories of unstructured clinical data. However, these methods are underutilized in clinical research, potentially due to the absence of free software geared towards clinicians with little technical expertise.

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      Mentions: NLP
    22. Symptom severity prediction from neuropsychiatric clinical records: Overview of 2016 Cegs N-GRID Shared Tasks Track 2.

      Symptom severity prediction from neuropsychiatric clinical records: Overview of 2016 Cegs N-GRID Shared Tasks Track 2.

      Symptom severity prediction from neuropsychiatric clinical records: Overview of 2016 CEGS N-GRID Shared Tasks Track 2.

      J Biomed Inform. 2017 Apr 25;:

      Authors: Filannino M, Stubbs A, Uzuner Ö

      Abstract The second track of the CEGS N-GRID 2016 natural language processing shared tasks focused on predicting symptom severity from neuropsychiatric clinical records.

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    49-72 of 2744 « 1 2 3 4 5 6 ... 113 114 115 »
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      Discourse, Entailment, Machine Translation, NER, Parsing, Segmentation, Semantic, Sentiment, Summarization, WSD