1. 25-48 of 2805 « 1 2 3 4 5 ... 115 116 117 »
    1. Informatics Support for Basic Research in Biomedicine.

      Informatics Support for Basic Research in Biomedicine.

      Informatics Support for Basic Research in Biomedicine.

      ILAR J. 2017 Jul 01;58(1):80-89

      Authors: Rindflesch TC, Blake CL, Fiszman M, Kilicoglu H, Rosemblat G, Schneider J, Zeiss CJ

      Abstract Informatics methodologies exploit computer-assisted techniques to help biomedical researchers manage large amounts of information. In this paper, we focus on the biomedical research literature (MEDLINE).

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    2. Semantic Role Labeling of Clinical Text: Comparing Syntactic Parsers and Features.

      Semantic Role Labeling of Clinical Text: Comparing Syntactic Parsers and Features.

      AMIA Annu Symp Proc. 2016;2016:1283-1292

      Authors: Zhang Y, Jiang M, Wang J, Xu H

      Abstract Semantic role labeling (SRL), which extracts shallow semantic relation representation from different surface textual forms of free text sentences, is important for understanding clinical narratives.

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    3. Epidemiology from Tweets: Estimating Misuse of Prescription Opioids in the USA from Social Media.

      Epidemiology from Tweets: Estimating Misuse of Prescription Opioids in the USA from Social Media.

      Epidemiology from Tweets: Estimating Misuse of Prescription Opioids in the USA from Social Media.

      J Med Toxicol. 2017 Aug 22;:

      Authors: Chary M, Genes N, Giraud-Carrier C, Hanson C, Nelson LS, Manini AF

      Abstract BACKGROUND: The misuse of prescription opioids (MUPO) is a leading public health concern. Social media are playing an expanded role in public health research, but there are few methods for estimating established epidemiological metrics from social media.

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      Mentions: Social Media USA
    4. Word2Vec inversion and traditional text classifiers for phenotyping lupus.

      Word2Vec inversion and traditional text classifiers for phenotyping lupus.

      Word2Vec inversion and traditional text classifiers for phenotyping lupus.

      BMC Med Inform Decis Mak. 2017 Aug 22;17(1):126

      Authors: Turner CA, Jacobs AD, Marques CK, Oates JC, Kamen DL, Anderson PE, Obeid JS

      Abstract BACKGROUND: Identifying patients with certain clinical criteria based on manual chart review of doctors' notes is a daunting task given the massive amounts of text notes in the electronic health records (EHR).

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    5. Serious Choices: A Protocol for an Environmental Scan of Patient Decision Aids for Seriously Ill People at Risk of Death Facing Choices about Life-Sustaining Treatments.

      Serious Choices: A Protocol for an Environmental Scan of Patient Decision Aids for Seriously Ill People at Risk of Death Facing Choices about Life-Sustaining Treatments.

      Serious Choices: A Protocol for an Environmental Scan of Patient Decision Aids for Seriously Ill People at Risk of Death Facing Choices about Life-Sustaining Treatments.

      Patient. 2017 Aug 20;:

      Authors: Saunders CH, Elwyn G, Kirkland K, Durand MA

      Abstract BACKGROUND: Seriously ill people at high risk of death face difficult decisions, especially concerning the extent of medical intervention.

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    6. Classification of Use Status for Dietary Supplements in Clinical Notes.

      Classification of Use Status for Dietary Supplements in Clinical Notes.

      Classification of Use Status for Dietary Supplements in Clinical Notes.

      Proceedings (IEEE Int Conf Bioinformatics Biomed). 2016 Dec;2016:1054-1061

      Authors: Fan Y, He L, Zhang R

      Abstract Clinical notes contain rich information about dietary supplements, which are critical for detecting signals of dietary supplement side effects and interactions between drugs and supplements. One of the important factors of supplement documentation is usage status, such as started and discontinuation.

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      Mentions: John
    7. Using Pathfinder Networks to Discover Alignment between Expert and Consumer Conceptual Knowledge from Online Vaccine Content.

      Using Pathfinder Networks to Discover Alignment between Expert and Consumer Conceptual Knowledge from Online Vaccine Content.

      Using Pathfinder Networks to Discover Alignment between Expert and Consumer Conceptual Knowledge from Online Vaccine Content.

      J Biomed Inform. 2017 Aug 17;:

      Authors: Amith M, Cunningham R, Savas LS, Boom J, Schvaneveldt R, Tao C, Cohen T

      Abstract This study demonstrates the use of distributed vector representations and Pathfinder Network Scaling (PFNETS) to represent online vaccine content created by health experts and by laypeople.

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    8. Do you vape? Leveraging electronic health records to assess clinician documentation of electronic nicotine delivery system use among adolescents and adults.

      Do you vape? Leveraging electronic health records to assess clinician documentation of electronic nicotine delivery system use among adolescents and adults.

      Do you vape? Leveraging electronic health records to assess clinician documentation of electronic nicotine delivery system use among adolescents and adults.

      Prev Med. 2017 Aug 16;:

      Authors: Young-Wolff KC, Klebaner D, Folck B, Carter-Harris L, Salloum RG, Prochaska JJ, Fogelberg R, Tan ASL

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      Mentions: EHR ASL
    9. Automatic data source identification for clinical trial eligibility criteria resolution.

      Automatic data source identification for clinical trial eligibility criteria resolution.

      AMIA Annu Symp Proc. 2016;2016:1149-1158

      Authors: Shivade C, Hebert C, Regan K, Fosler-Lussier E, Lai AM

      Abstract Clinical trial coordinators refer to both structured and unstructured sources of data when evaluating a subject for eligibility. While some eligibility criteria can be resolved using structured data, some require manual review of clinical notes.

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    10. Prediction of Emergency Department Hospital Admission Based on Natural Language Processing and Neural Networks.

      Prediction of Emergency Department Hospital Admission Based on Natural Language Processing and Neural Networks.

      Prediction of Emergency Department Hospital Admission Based on Natural Language Processing and Neural Networks.

      Methods Inf Med. 2017 Aug 16;56(5):

      Authors: Zhang X, Kim J, Patzer RE, Pitts SR, Patzer A, Schrager JD

      Abstract OBJECTIVE: To describe and compare logistic regression and neural network modeling strategies to predict hospital admission or transfer following initial presentation to Emergency Department (ED) triage with and without the addition of natural language processing elements. METHODS: Using data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), a cross-sectional probability sample of United States EDs from 2012 and 2013 survey years, we developed ...

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    11. The use of natural language processing on pediatric diagnostic radiology reports in the electronic health record to identify deep venous thrombosis in children.

      The use of natural language processing on pediatric diagnostic radiology reports in the electronic health record to identify deep venous thrombosis in children.

      The use of natural language processing on pediatric diagnostic radiology reports in the electronic health record to identify deep venous thrombosis in children.

      J Thromb Thrombolysis. 2017 Aug 16;:

      Authors: Gálvez JA, Pappas JM, Ahumada L, Martin JN, Simpao AF, Rehman MA, Witmer C

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      Mentions: San Mateo NLP
    12. A Clinical Decision Support System for Monitoring Post-Colonoscopy Patient Follow-Up and Scheduling.

      A Clinical Decision Support System for Monitoring Post-Colonoscopy Patient Follow-Up and Scheduling.

      A Clinical Decision Support System for Monitoring Post-Colonoscopy Patient Follow-Up and Scheduling.

      AMIA Jt Summits Transl Sci Proc. 2017;2017:295-301

      Authors: Wadia R, Shifman M, Levin FL, Marenco L, Brandt CA, Cheung KH, Taddei T, Krauthammer M

      Abstract This paper describes a natural language processing (NLP)-based clinical decision support (CDS) system that is geared towards colon cancer care coordinators as the end users.

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      Mentions: NLP
    13. Correlating Lab Test Results in Clinical Notes with Structured Lab Data: A Case Study in HbA1c and Glucose.

      Correlating Lab Test Results in Clinical Notes with Structured Lab Data: A Case Study in HbA1c and Glucose.

      Correlating Lab Test Results in Clinical Notes with Structured Lab Data: A Case Study in HbA1c and Glucose.

      AMIA Jt Summits Transl Sci Proc. 2017;2017:221-228

      Authors: Sijia L, Liwei W, Ihrke D, Chaudhary V, Tao C, Weng C, Liu H

      Abstract It is widely acknowledged that information extraction of unstructured clinical notes using natural language processing (NLP) and text mining is essential for secondary use of clinical data for clinical research and practice.

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      Mentions: Liu
    14. Ground Truth Creation for Complex Clinical NLP Tasks - an Iterative Vetting Approach and Lessons Learned.

      Ground Truth Creation for Complex Clinical NLP Tasks - an Iterative Vetting Approach and Lessons Learned.

      Ground Truth Creation for Complex Clinical NLP Tasks - an Iterative Vetting Approach and Lessons Learned.

      AMIA Jt Summits Transl Sci Proc. 2017;2017:203-212

      Authors: Liang JJ, Tsou CH, Devarakonda MV

      Abstract Natural language processing (NLP) holds the promise of effectively analyzing patient record data to reduce cognitive load on physicians and clinicians in patient care, clinical research, and hospital operations management.

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      Mentions: NLP EHR
    15. Surveillance of Peripheral Arterial Disease Cases Using Natural Language Processing of Clinical Notes.

      Surveillance of Peripheral Arterial Disease Cases Using Natural Language Processing of Clinical Notes.

      Surveillance of Peripheral Arterial Disease Cases Using Natural Language Processing of Clinical Notes.

      AMIA Jt Summits Transl Sci Proc. 2017;2017:28-36

      Authors: Afzal N, Sohn S, Scott CG, Liu H, Kullo IJ, Arruda-Olson AM

      Abstract Peripheral arterial disease (PAD) is a chronic disease that affects millions of people worldwide and yet remains underdiagnosed and undertreated. Early detection is important, because PAD is strongly associated with an increased risk of mortality and morbidity.

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      Mentions: NLP PAD Liu H
    16. Large-Scale Machine Learning of Media Outlets for Understanding Public Reactions to Nation-Wide Viral Infection Outbreaks.

      Large-Scale Machine Learning of Media Outlets for Understanding Public Reactions to Nation-Wide Viral Infection Outbreaks.

      Large-Scale Machine Learning of Media Outlets for Understanding Public Reactions to Nation-Wide Viral Infection Outbreaks.

      Methods. 2017 Aug 13;:

      Authors: Choi S, Lee J, Kang MG, Min H, Chang YS, Yoon S

      Abstract From May to July 2015, there was a nation-wide outbreak of Middle East respiratory syndrome (MERS) in Korea. MERS is caused by MERS-CoV, an enveloped, positive-sense, single stranded RNA virus belonging to the family Coronaviridae.

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      Mentions: Korea
    17. Resource Classification for Medical Questions.

      Resource Classification for Medical Questions.

      AMIA Annu Symp Proc. 2016;2016:1040-1049

      Authors: Roberts K, Rodriguez L, Shooshan SE, Demner-Fushman D

      Abstract We present an approach for manually and automatically classifying the resource type of medical questions. Three types of resources are considered: patient-specific, general knowledge, and research. Using this approach, an automatic question answering system could select the best type of resource from which to consider answers.

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    18. Mapping Phenotypic Information in Heterogeneous Textual Sources to a Domain-Specific Terminological Resource.

      Mapping Phenotypic Information in Heterogeneous Textual Sources to a Domain-Specific Terminological Resource.

      Mapping Phenotypic Information in Heterogeneous Textual Sources to a Domain-Specific Terminological Resource.

      PLoS One. 2016;11(9):e0162287

      Authors: Alnazzawi N, Thompson P, Ananiadou S

      Abstract Biomedical literature articles and narrative content from Electronic Health Records (EHRs) both constitute rich sources of disease-phenotype information. Phenotype concepts may be mentioned in text in multiple ways, using phrases with a variety of structures.

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    19. Decoding naturalistic experiences from human brain activity via distributed representations of words.

      Decoding naturalistic experiences from human brain activity via distributed representations of words.

      Decoding naturalistic experiences from human brain activity via distributed representations of words.

      Neuroimage. 2017 Aug 08;:

      Authors: Nishida S, Nishimoto S

      Abstract Natural visual scenes induce rich perceptual experiences that are highly diverse from scene to scene and from person to person. Here, we propose a new framework for decoding such experiences using a distributed representation of words.

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    20. Automated Cancer Registry Notifications: Validation of a Medical Text Analytics System for Identifying Patients with Cancer from a State-Wide Pathology Repository.

      Automated Cancer Registry Notifications: Validation of a Medical Text Analytics System for Identifying Patients with Cancer from a State-Wide Pathology Repository.

      AMIA Annu Symp Proc. 2016;2016:964-973

      Authors: Nguyen AN, Moore J, O'Dwyer J, Philpot S

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    21. Reverse translation of adverse event reports paves the way for de-risking preclinical off-targets.

      Reverse translation of adverse event reports paves the way for de-risking preclinical off-targets.

      Reverse translation of adverse event reports paves the way for de-risking preclinical off-targets.

      Elife. 2017 Aug 08;6:

      Authors: Maciejewski M, Lounkine E, Whitebread S, Farmer P, DuMouchel W, Shoichet BK, Urban L

      Abstract The Food and Drug Administration Adverse Event Reporting System (FAERS) remains the primary source for post-marketing pharmacovigilance.

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    22. Combining Open-domain and Biomedical Knowledge for Topic Recognition in Consumer Health Questions.

      Combining Open-domain and Biomedical Knowledge for Topic Recognition in Consumer Health Questions.

      Combining Open-domain and Biomedical Knowledge for Topic Recognition in Consumer Health Questions.

      AMIA Annu Symp Proc. 2016;2016:914-923

      Authors: Mrabet Y, Kilicoglu H, Roberts K, Demner-Fushman D

      Abstract Determining the main topics in consumer health questions is a crucial step in their processing as it allows narrowing the search space to a specific semantic context. In this paper we propose a topic recognition approach based on biomedical and open-domain knowledge bases.

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    23. Identification of people with acquired hemophilia in a large electronic health record database.

      Identification of people with acquired hemophilia in a large electronic health record database.

      Identification of people with acquired hemophilia in a large electronic health record database.

      J Blood Med. 2017;8:89-97

      Authors: Wang M, Cyhaniuk A, Cooper DL, Iyer NN

      Abstract BACKGROUND: Electronic health records (EHRs) can provide insights into diagnoses, treatment patterns, and clinical outcomes. Acquired hemophilia (AH) is an ultrarare bleeding disorder characterized by factor VIII inhibiting autoantibodies. AIM: To identify patients with AH using an EHR database.

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    25-48 of 2805 « 1 2 3 4 5 ... 115 116 117 »
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      Discourse, Entailment, Machine Translation, NER, Parsing, Segmentation, Semantic, Sentiment, Summarization, WSD