1. 73-96 of 2696 « 1 2 3 4 5 6 7 ... 111 112 113 »
    1. Computerized Scoring Algorithms for the Autobiographical Memory Test.

      Computerized Scoring Algorithms for the Autobiographical Memory Test.

      Computerized Scoring Algorithms for the Autobiographical Memory Test.

      Psychol Assess. 2017 Apr 03;:

      Authors: Takano K, Gutenbrunner C, Martens K, Salmon K, Raes F

      Abstract Reduced specificity of autobiographical memories is a hallmark of depressive cognition. Autobiographical memory (AM) specificity is typically measured by the Autobiographical Memory Test (AMT), in which respondents are asked to describe personal memories in response to emotional cue words.

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    2. Creation of a simple natural language processing tool to support an imaging utilization quality dashboard.

      Creation of a simple natural language processing tool to support an imaging utilization quality dashboard.

      Creation of a simple natural language processing tool to support an imaging utilization quality dashboard.

      Int J Med Inform. 2017 May;101:93-99

      Authors: Swartz J, Koziatek C, Theobald J, Smith S, Iturrate E

      Abstract BACKGROUND: Testing for venous thromboembolism (VTE) is associated with cost and risk to patients (e.g. radiation).

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      Mentions: Smith NLP
    3. Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts.

      Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts.

      Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts.

      J Am Med Inform Assoc. 2017 Feb 22;:

      Authors: Cocos A, Fiks AG, Masino AJ

      Abstract Objective : Social media is an important pharmacovigilance data source for adverse drug reaction (ADR) identification. Human review of social media data is infeasible due to data quantity, thus natural language processing techniques are necessary.

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    4. Automated classification of eligibility criteria in clinical trials to facilitate patient-trial matching for specific patient populations.

      Automated classification of eligibility criteria in clinical trials to facilitate patient-trial matching for specific patient populations.

      Automated classification of eligibility criteria in clinical trials to facilitate patient-trial matching for specific patient populations.

      J Am Med Inform Assoc. 2017 Feb 19;:

      Authors: Zhang K, Demner-Fushman D

      Abstract Objective : To develop automated classification methods for eligibility criteria in ClinicalTrials.gov to facilitate patient-trial matching for specific populations such as persons living with HIV or pregnant women.

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    5. Visualizing the structure of RNA-seq expression data using grade of membership models.

      Visualizing the structure of RNA-seq expression data using grade of membership models.

      Visualizing the structure of RNA-seq expression data using grade of membership models.

      PLoS Genet. 2017 Mar;13(3):e1006599

      Authors: Dey KK, Hsiao CJ, Stephens M

      Abstract Grade of membership models, also known as "admixture models", "topic models" or "Latent Dirichlet Allocation", are a generalization of cluster models that allow each sample to have membership in multiple clusters.

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      Mentions: Hsiao
    6. A Natural Language Processing Framework for Assessing Hospital Readmissions for Patients with COPD.

      A Natural Language Processing Framework for Assessing Hospital Readmissions for Patients with COPD.

      A Natural Language Processing Framework for Assessing Hospital Readmissions for Patients with COPD.

      IEEE J Biomed Health Inform. 2017 Mar 17;:

      Authors: Agarwal A, Baechle C, Behara R, Zhu X

      Abstract With the passage of recent federal legislation many medical institutions are now responsible for reaching target hospital readmission rates.

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      Mentions: Bayes
    7. Characterisation of mental health conditions in social media using Informed Deep Learning.

      Characterisation of mental health conditions in social media using Informed Deep Learning.

      Characterisation of mental health conditions in social media using Informed Deep Learning.

      Sci Rep. 2017 Mar 22;7:45141

      Authors: Gkotsis G, Oellrich A, Velupillai S, Liakata M, Hubbard TJ, Dobson RJ, Dutta R

      Abstract The number of people affected by mental illness is on the increase and with it the burden on health and social care use, as well as the loss of both productivity and quality-adjusted life-years.

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    8. 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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    9. 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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    10. 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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    11. 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
    12. 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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    13. 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
    14. 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
    15. 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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    16. 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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    17. 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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    18. 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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    19. 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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    20. 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
    73-96 of 2696 « 1 2 3 4 5 6 7 ... 111 112 113 »
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    1. Default:

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