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    1. 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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    2. 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
    3. 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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    4. 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
    5. 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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    6. 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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    7. 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
    8. 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
    9. 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
    10. 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
    11. 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
    12. 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
    13. 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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    14. 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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    15. 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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    16. 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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    17. 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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    18. 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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    19. 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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    20. Childhood respiratory illness presentation and service utilisation in primary care: a six-year cohort study in Wellington, New Zealand, using natural language processing (NLP) software.

      Childhood respiratory illness presentation and service utilisation in primary care: a six-year cohort study in Wellington, New Zealand, using natural language processing (NLP) software.

      Childhood respiratory illness presentation and service utilisation in primary care: a six-year cohort study in Wellington, New Zealand, using natural language processing (NLP) software.

      BMJ Open. 2017 Aug 01;7(7):e017146

      Authors: Dowell A, Darlow B, Macrae J, Stubbe M, Turner N, McBain L

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    21. a novel in silico feature learning pipeline for mining prognostic and associative factors from text-based electronic medical records.

      a novel in silico feature learning pipeline for mining prognostic and associative factors from text-based electronic medical records.

      TEPAPA: a novel in silico feature learning pipeline for mining prognostic and associative factors from text-based electronic medical records.

      Sci Rep. 2017 Jul 31;7(1):6918

      Authors: Lin FP, Pokorny A, Teng C, Epstein RJ

      Abstract Vast amounts of clinically relevant text-based variables lie undiscovered and unexploited in electronic medical records (EMR).

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      Mentions: NLP ROC
    22. Triaging Patient Complaints: Monte Carlo Cross-Validation of Six Machine Learning Classifiers.

      Triaging Patient Complaints: Monte Carlo Cross-Validation of Six Machine Learning Classifiers.

      Triaging Patient Complaints: Monte Carlo Cross-Validation of Six Machine Learning Classifiers.

      JMIR Med Inform. 2017 Jul 31;5(3):e19

      Authors: Elmessiry A, Cooper WO, Catron TF, Karrass J, Zhang Z, Singh MP

      Abstract BACKGROUND: Unsolicited patient complaints can be a useful service recovery tool for health care organizations. Some patient complaints contain information that may necessitate further action on the part of the health care organization and/or the health care professional.

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    23. "What else are you worried about?" - Integrating textual responses into quantitative social science research.

      "What else are you worried about?" - Integrating textual responses into quantitative social science research.

      "What else are you worried about?" - Integrating textual responses into quantitative social science research.

      PLoS One. 2017;12(7):e0182156

      Authors: Rohrer JM, Brümmer M, Schmukle SC, Goebel J, Wagner GG

      Abstract Open-ended questions have routinely been included in large-scale survey and panel studies, yet there is some perplexity about how to actually incorporate the answers to such questions into quantitative social science research.

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    1-24 of 2777 1 2 3 4 ... 114 115 116 »
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      Discourse, Entailment, Machine Translation, NER, Parsing, Segmentation, Semantic, Sentiment, Summarization, WSD