1. 49-72 of 2664 « 1 2 3 4 5 6 ... 109 110 111 »
    1. Longitudinal changes in linguistic complexity among professional football players.

      Longitudinal changes in linguistic complexity among professional football players.

      Longitudinal changes in linguistic complexity among professional football players.

      Brain Lang. 2017 Mar 16;169:57-63

      Authors: Berisha V, Wang S, LaCross A, Liss J, Garcia-Filion P

      Abstract Reductions in spoken language complexity have been associated with the onset of various neurological disorders. The objective of this study is to analyze whether similar trends are found in professional football players who are at risk for chronic traumatic encephalopathy.

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    2. From big data to diagnosis and prognosis: gene expression signatures in liver hepatocellular carcinoma.

      From big data to diagnosis and prognosis: gene expression signatures in liver hepatocellular carcinoma.

      From big data to diagnosis and prognosis: gene expression signatures in liver hepatocellular carcinoma.

      PeerJ. 2017;5:e3089

      Authors: Yang H, Zhang X, Cai XY, Wen DY, Ye ZH, Liang L, Zhang L, Wang HL, Chen G, Feng ZB

      Abstract BACKGROUND: Liver hepatocellular carcinoma accounts for the overwhelming majority of primary liver cancers and its belated diagnosis and poor prognosis call for novel biomarkers to be discovered, which, in the era of big data, innovative bioinformatics and computational techniques can prove to be highly helpful in. METHODS: Big data aggregated from The Cancer Genome Atlas and Natural Language Processing were ...

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    3. Validation of the Total Visual Acuity Extraction Algorithm (TOVA) for Automated Extraction of Visual Acuity Data From Free Text, Unstructured Clinical Records.

      Validation of the Total Visual Acuity Extraction Algorithm (TOVA) for Automated Extraction of Visual Acuity Data From Free Text, Unstructured Clinical Records.

      Validation of the Total Visual Acuity Extraction Algorithm (TOVA) for Automated Extraction of Visual Acuity Data From Free Text, Unstructured Clinical Records.

      Transl Vis Sci Technol. 2017 Mar;6(2):2

      Authors: Baughman DM, Su GL, Tsui I, Lee CS, Lee AY

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    4. Hospital Readmission and Social Risk Factors Identified from Physician Notes.

      Hospital Readmission and Social Risk Factors Identified from Physician Notes.

      Hospital Readmission and Social Risk Factors Identified from Physician Notes.

      Health Serv Res. 2017 Mar 13;:

      Authors: Navathe AS, Zhong F, Lei VJ, Chang FY, Sordo M, Topaz M, Navathe SB, Rocha RA, Zhou L

      Abstract OBJECTIVE: To evaluate the prevalence of seven social factors using physician notes as compared to claims and structured electronic health records (EHRs) data and the resulting association with 30-day readmissions.

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      Mentions: Massachusetts EHR
    5. Using Probabilistic Record Linkage of Structured and Unstructured Data to Identify Duplicate Cases in Spontaneous Adverse Event Reporting Systems.

      Using Probabilistic Record Linkage of Structured and Unstructured Data to Identify Duplicate Cases in Spontaneous Adverse Event Reporting Systems.

      Using Probabilistic Record Linkage of Structured and Unstructured Data to Identify Duplicate Cases in Spontaneous Adverse Event Reporting Systems.

      Drug Saf. 2017 Mar 14;:

      Authors: Kreimeyer K, Menschik D, Winiecki S, Paul W, Barash F, Woo EJ, Alimchandani M, Arya D, Zinderman C, Forshee R, Botsis T

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    6. A Passage Retrieval Method based on Probabilistic Information Retrieval Model and UMLS Concepts in Biomedical Question Answering.

      A Passage Retrieval Method based on Probabilistic Information Retrieval Model and UMLS Concepts in Biomedical Question Answering.

      A Passage Retrieval Method based on Probabilistic Information Retrieval Model and UMLS Concepts in Biomedical Question Answering.

      J Biomed Inform. 2017 Mar 07;:

      Authors: Sarrouti M, Ouatik SE

      Abstract BACKGROUND AND OBJECTIVE: Passage retrieval, the identification of top-ranked passages that may contain the answer for a given biomedical question, is a crucial component for any biomedical question answering (QA) system.

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      Mentions: Stanford Umls
    7. Bayesian molecular design with a chemical language model.

      Bayesian molecular design with a chemical language model.

      Bayesian molecular design with a chemical language model.

      J Comput Aided Mol Des. 2017 Mar 09;:

      Authors: Ikebata H, Hongo K, Isomura T, Maezono R, Yoshida R

      Abstract The aim of computational molecular design is the identification of promising hypothetical molecules with a predefined set of desired properties. We address the issue of accelerating the material discovery with state-of-the-art machine learning techniques.

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      Mentions: Monte Carlo Ascii Mol
    8. Clinical Word Sense Disambiguation with Interactive Search and Classification.

      Clinical Word Sense Disambiguation with Interactive Search and Classification.

      Clinical Word Sense Disambiguation with Interactive Search and Classification.

      AMIA Annu Symp Proc. 2016;2016:2062-2071

      Authors: Wang Y, Zheng K, Xu H, Mei Q

      Abstract Resolving word ambiguity in clinical text is critical for many natural language processing applications. Effective word sense disambiguation (WSD) systems rely on training a machine learning based classifier with abundant clinical text that is accurately annotated, the creation of which can be costly and time-consuming.

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    9. Ensembles of NLP Tools for Data Element Extraction from Clinical Notes.

      Ensembles of NLP Tools for Data Element Extraction from Clinical Notes.

      Ensembles of NLP Tools for Data Element Extraction from Clinical Notes.

      AMIA Annu Symp Proc. 2016;2016:1880-1889

      Authors: Kuo TT, Rao P, Maehara C, Doan S, Chaparro JD, Day ME, Farcas C, Ohno-Machado L, Hsu CN

      Abstract Natural Language Processing (NLP) is essential for concept extraction from narrative text in electronic health records (EHR).

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    10. Using Natural Language Processing and Network Analysis to Develop a Conceptual Framework for Medication Therapy Management Research.

      Using Natural Language Processing and Network Analysis to Develop a Conceptual Framework for Medication Therapy Management Research.

      Using Natural Language Processing and Network Analysis to Develop a Conceptual Framework for Medication Therapy Management Research.

      AMIA Annu Symp Proc. 2016;2016:984-993

      Authors: Ogallo W, Kanter AS

      Abstract This paper describes a theory derivation process used to develop a conceptual framework for medication therapy management (MTM) research. The MTM service model and chronic care model were selected as parent theories.

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    11. Understanding patient satisfaction with received healthcare services: A natural language processing approach.

      Understanding patient satisfaction with received healthcare services: A natural language processing approach.

      Understanding patient satisfaction with received healthcare services: A natural language processing approach.

      AMIA Annu Symp Proc. 2016;2016:524-533

      Authors: Doing-Harris K, Mowery DL, Daniels C, Chapman WW, Conway M

      Abstract Important information is encoded in free-text patient comments. We determine the most common topics in patient comments, design automatic topic classifiers, identify comments ' sentiment, and find new topics in negative comments.

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    12. Automated Detection of Privacy Sensitive Conditions in C-CDAs: Security Labeling Services at the Department of Veterans Affairs.

      Automated Detection of Privacy Sensitive Conditions in C-CDAs: Security Labeling Services at the Department of Veterans Affairs.

      Automated Detection of Privacy Sensitive Conditions in C-CDAs: Security Labeling Services at the Department of Veterans Affairs.

      AMIA Annu Symp Proc. 2016;2016:332-341

      Authors: Bouhaddou O, Davis M, Donahue M, Mallia A, Griffin S, Teal J, Nebeker J

      Abstract Care coordination across healthcare organizations depends upon health information exchange. Various policies and laws govern permissible exchange, particularly when the information includes privacy sensitive conditions.

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    13. Unsupervised Ensemble Ranking of Terms in Electronic Health Record Notes Based on Their Importance to Patients.

      Unsupervised Ensemble Ranking of Terms in Electronic Health Record Notes Based on Their Importance to Patients.

      Unsupervised Ensemble Ranking of Terms in Electronic Health Record Notes Based on Their Importance to Patients.

      J Biomed Inform. 2017 Mar 03;:

      Authors: Chen J, Yu H

      Abstract BACKGROUND: Allowing patients to access their own electronic health record (EHR) notes through online patient portals has the potential to improve patient-centered care. However, EHR notes contain abundant medical jargon that can be difficult for patients to comprehend.

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      Mentions: NLP EHR
    14. Prediction of advertisement preference by fusing EEG response and sentiment analysis.

      Prediction of advertisement preference by fusing EEG response and sentiment analysis.

      Prediction of advertisement preference by fusing EEG response and sentiment analysis.

      Neural Netw. 2017 Feb 16;:

      Authors: Gauba H, Kumar P, Roy PP, Singh P, Dogra DP, Raman B

      Abstract This paper presents a novel approach to predict rating of video-advertisements based on a multimodal framework combining physiological analysis of the user and global sentiment-rating available on the internet.

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      Mentions: EEG
    15. Detecting negation and scope in Chinese clinical notes using character and word embedding.

      Detecting negation and scope in Chinese clinical notes using character and word embedding.

      Detecting negation and scope in Chinese clinical notes using character and word embedding.

      Comput Methods Programs Biomed. 2017 Mar;140:53-59

      Authors: Kang T, Zhang S, Xu N, Wen D, Zhang X, Lei J

      Abstract BACKGROUND AND OBJECTIVES: Researchers have developed effective methods to index free-text clinical notes into structured database, in which negation detection is a critical but challenging step.

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    16. Big Data Analyses in Health and Opportunities for Research in Radiology.

      Big Data Analyses in Health and Opportunities for Research in Radiology.

      Big Data Analyses in Health and Opportunities for Research in Radiology.

      Semin Musculoskelet Radiol. 2017 Feb;21(1):32-36

      Authors: Aphinyanaphongs Y

      Abstract This article reviews examples of big data analyses in health care with a focus on radiology. We review the defining characteristics of big data, the use of natural language processing, traditional and novel data sources, and large clinical data repositories available for research.

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    17. Understanding Depressive Symptoms and Psychosocial Stressors on Twitter: A Corpus-Based Study.

      Understanding Depressive Symptoms and Psychosocial Stressors on Twitter: A Corpus-Based Study.

      Understanding Depressive Symptoms and Psychosocial Stressors on Twitter: A Corpus-Based Study.

      J Med Internet Res. 2017 Feb 28;19(2):e48

      Authors: Mowery D, Smith H, Cheney T, Stoddard G, Coppersmith G, Bryan C, Conway M

      Abstract BACKGROUND: With a lifetime prevalence of 16.2%, major depressive disorder is the fifth biggest contributor to the disease burden in the United States.

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      Mentions: DSM
    18. Early recognition of multiple sclerosis using natural language processing of the electronic health record.

      Early recognition of multiple sclerosis using natural language processing of the electronic health record.

      Early recognition of multiple sclerosis using natural language processing of the electronic health record.

      BMC Med Inform Decis Mak. 2017 Feb 28;17(1):24

      Authors: Chase HS, Mitrani LR, Lu GG, Fulgieri DJ

      Abstract BACKGROUND: Diagnostic accuracy might be improved by algorithms that searched patients' clinical notes in the electronic health record (EHR) for signs and symptoms of diseases such as multiple sclerosis (MS).

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    19. The effects of deep network topology on mortality prediction.

      The effects of deep network topology on mortality prediction.

      The effects of deep network topology on mortality prediction.

      Conf Proc IEEE Eng Med Biol Soc. 2016 Aug;2016:2602-2605

      Authors: Du H, Ghassemi MM, Feng M, Hao Du, Ghassemi MM, Mengling Feng, Feng M, Du H, Ghassemi MM

      Abstract Deep learning has achieved remarkable results in the areas of computer vision, speech recognition, natural language processing and most recently, even playing Go.

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      Mentions: EMR
    49-72 of 2664 « 1 2 3 4 5 6 ... 109 110 111 »
  1. Categories

    1. Default:

      Discourse, Entailment, Machine Translation, NER, Parsing, Segmentation, Semantic, Sentiment, Summarization, WSD