1. 25-48 of 2696 « 1 2 3 4 5 ... 111 112 113 »
    1. It Doesn't Hurt to Ask: Question-Asking Increases Liking.

      It Doesn't Hurt to Ask: Question-Asking Increases Liking.

      It Doesn't Hurt to Ask: Question-Asking Increases Liking.

      J Pers Soc Psychol. 2017 Apr 27;:

      Authors: Huang K, Yeomans M, Brooks AW, Minson J, Gino F

      Abstract Conversation is a fundamental human experience that is necessary to pursue intrapersonal and interpersonal goals across myriad contexts, relationships, and modes of communication. In the current research, we isolate the role of an understudied conversational behavior: question-asking.

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    2. Application of Natural Language Processing and Network Analysis Techniques to Post-market Reports for the Evaluation of Dose-related Anti-Thymocyte Globulin Safety Patterns.

      Application of Natural Language Processing and Network Analysis Techniques to Post-market Reports for the Evaluation of Dose-related Anti-Thymocyte Globulin Safety Patterns.

      Application of Natural Language Processing and Network Analysis Techniques to Post-market Reports for the Evaluation of Dose-related Anti-Thymocyte Globulin Safety Patterns.

      Appl Clin Inform. 2017 Apr 26;8(2):396-411

      Authors: Botsis T, Foster M, Arya N, Kreimeyer K, Pandey A, Arya D

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    3. Probability Statements Extraction with Constrained Conditional Random Fields.

      Probability Statements Extraction with Constrained Conditional Random Fields.

      Probability Statements Extraction with Constrained Conditional Random Fields.

      Stud Health Technol Inform. 2016;228:527-31

      Authors: Deleris LA, Jochim C

      Abstract This paper investigates how to extract probability statements from academic medical papers. In previous work we have explored traditional classification methods which led to numerous false negatives.

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    4. Suggesting Missing Relations in Biomedical Ontologies Based on Lexical Regularities.

      Suggesting Missing Relations in Biomedical Ontologies Based on Lexical Regularities.

      Suggesting Missing Relations in Biomedical Ontologies Based on Lexical Regularities.

      Stud Health Technol Inform. 2016;228:384-8

      Authors: Quesada-Martínez M, Fernández-Breis JT, Karlsson D

      Abstract The number of biomedical ontologies has increased significantly in recent years. Many of such ontologies are the result of efforts of communities of domain experts and ontology engineers.

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    5. HLA class I binding prediction via convolutional neural networks.

      HLA class I binding prediction via convolutional neural networks.

      HLA class I binding prediction via convolutional neural networks.

      Bioinformatics. 2017 Apr 21;:

      Authors: Vang YS, Xie X

      Abstract Motivation: Many biological processes are governed by protein-ligand interactions. One such example is the recognition of self and nonself cells by the immune system. This immune response process is regulated by the major histocompatibility complex (MHC) protein which is encoded by the human leukocyte antigen (HLA) complex.

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      Mentions: HLA
    6. Clinical records anonymisation and text extraction (CRATE): an open-source software system.

      Clinical records anonymisation and text extraction (CRATE): an open-source software system.

      Clinical records anonymisation and text extraction (CRATE): an open-source software system.

      BMC Med Inform Decis Mak. 2017 Apr 26;17(1):50

      Authors: Cardinal RN

      Abstract BACKGROUND: Electronic medical records contain information of value for research, but contain identifiable and often highly sensitive confidential information.

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    7. A qRT-PCR and Gene Functional Enrichment Study Focused on Downregulation of miR-141-3p in Hepatocellular Carcinoma and Its Clinicopathological Significance.

      A qRT-PCR and Gene Functional Enrichment Study Focused on Downregulation of miR-141-3p in Hepatocellular Carcinoma and Its Clinicopathological Significance.

      A qRT-PCR and Gene Functional Enrichment Study Focused on Downregulation of miR-141-3p in Hepatocellular Carcinoma and Its Clinicopathological Significance.

      Technol Cancer Res Treat. 2017 Jan 01;:1533034617705056

      Authors: Liu CZ, Ye ZH, Ma J, He RQ, Liang HW, Peng ZG, Chen G

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      Mentions: Liu
    8. Taming Big Data: An Information Extraction Strategy for Large Clinical Text Corpora.

      Taming Big Data: An Information Extraction Strategy for Large Clinical Text Corpora.

      Taming Big Data: An Information Extraction Strategy for Large Clinical Text Corpora.

      Stud Health Technol Inform. 2015;213:175-8

      Authors: Gundlapalli AV, Divita G, Carter ME, Redd A, Samore MH, Gupta K, Trautner B

      Abstract Concepts of interest for clinical and research purposes are not uniformly distributed in clinical text available in electronic medical records.

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    9. Automated annotation and classification of BI-RADS assessment from radiology reports.

      Automated annotation and classification of BI-RADS assessment from radiology reports.

      Automated annotation and classification of BI-RADS assessment from radiology reports.

      J Biomed Inform. 2017 Apr 17;:

      Authors: Castro SM, Tseytlin E, Medvedeva O, Mitchell K, Visweswaran S, Bekhuis T, Jacobson RS

      Abstract The Breast Imaging Reporting and Data System (BI-RADS) was developed to reduce variation in the descriptions of findings. Manual analysis of breast radiology report data is challenging but is necessary for clinical and healthcare quality assurance activities.

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      Mentions: Naïve Bayes SVM NLP
    10. Personalized Guideline-Based Treatment Recommendations Using Natural Language Processing Techniques.

      Personalized Guideline-Based Treatment Recommendations Using Natural Language Processing Techniques.

      Personalized Guideline-Based Treatment Recommendations Using Natural Language Processing Techniques.

      Stud Health Technol Inform. 2017;235:271-275

      Authors: Becker M, Böckmann B

      Abstract Clinical guidelines and clinical pathways are accepted and proven instruments for quality assurance and process optimization. Today, electronic representation of clinical guidelines exists as unstructured text, but is not well-integrated with patient-specific information from electronic health records.

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    11. Automated Classification of Semi-Structured Pathology Reports into ICD-O Using SVM in Portuguese.

      Automated Classification of Semi-Structured Pathology Reports into ICD-O Using SVM in Portuguese.

      Automated Classification of Semi-Structured Pathology Reports into ICD-O Using SVM in Portuguese.

      Stud Health Technol Inform. 2017;235:256-260

      Authors: Oleynik M, Patrão DFC, Finger M

      Abstract Pathology reports are a main source of information regarding cancer diagnosis and are commonly written following semi-structured templates that include tumour localisation and behaviour.

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    12. Acronym Disambiguation in Spanish Electronic Health Narratives Using Machine Learning Techniques.

      Acronym Disambiguation in Spanish Electronic Health Narratives Using Machine Learning Techniques.

      Acronym Disambiguation in Spanish Electronic Health Narratives Using Machine Learning Techniques.

      Stud Health Technol Inform. 2017;235:251-255

      Authors: Rubio-López I, Costumero R, Ambit H, Gonzalo-Martín C, Menasalvas E, Rodríguez González A

      Abstract Electronic Health Records (EHRs) are now being massively used in hospitals what has motivated current developments of new methods to process clinical narratives (unstructured data) making it possible to perform context-based searches.

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    13. Medical Text Classification Using Convolutional Neural Networks.

      Medical Text Classification Using Convolutional Neural Networks.

      Medical Text Classification Using Convolutional Neural Networks.

      Stud Health Technol Inform. 2017;235:246-250

      Authors: Hughes M, Li I, Kotoulas S, Suzumura T

      Abstract We present an approach to automatically classify clinical text at a sentence level. We are using deep convolutional neural networks to represent complex features. We train the network on a dataset providing a broad categorization of health information.

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    14. Prevalence Estimation of Protected Health Information in Swedish Clinical Text.

      Prevalence Estimation of Protected Health Information in Swedish Clinical Text.

      Prevalence Estimation of Protected Health Information in Swedish Clinical Text.

      Stud Health Technol Inform. 2017;235:216-220

      Authors: Henriksson A, Kvist M, Dalianis H

      Abstract Obscuring protected health information (PHI) in the clinical text of health records facilitates the secondary use of healthcare data in a privacy-preserving manner.

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    15. Developing a Manually Annotated Corpus of Clinical Letters for Breast Cancer Patients on Routine Follow-Up.

      Developing a Manually Annotated Corpus of Clinical Letters for Breast Cancer Patients on Routine Follow-Up.

      Developing a Manually Annotated Corpus of Clinical Letters for Breast Cancer Patients on Routine Follow-Up.

      Stud Health Technol Inform. 2017;235:196-200

      Authors: Pitson G, Banks P, Cavedon L, Verspoor K

      Abstract This paper introduces the annotation schema and annotation process for a corpus of clinical letters describing the disease course and treatment of oestrogen receptor positive breast cancer patients, after completion of primary surgery and radiotherapy treatment.

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    16. Development and Evaluation of a Case-Based Retrieval Service.

      Development and Evaluation of a Case-Based Retrieval Service.

      Development and Evaluation of a Case-Based Retrieval Service.

      Stud Health Technol Inform. 2017;235:186-190

      Authors: Pasche E, Chinali M, Gobeill J, Ruch P

      Abstract Identifying similar patients might greatly facilitate the treatment of a given patient, enabling to observe the response and outcome to a particular treatment. Case-based retrieval services dealing with natural language processing are of major importance to deal with the significant amount of unstructured clinical data.

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    17. Evaluation of the Terminology Coverage in the French Corpus LiSSa.

      Evaluation of the Terminology Coverage in the French Corpus LiSSa.

      Evaluation of the Terminology Coverage in the French Corpus LiSSa.

      Stud Health Technol Inform. 2017;235:126-130

      Authors: Cabot C, Soualmia LF, Grosjean J, Griffon N, Darmoni SJ

      Abstract Extracting concepts from medical texts is a key to support many advanced applications in medical information retrieval. Entity recognition in French texts is moreover challenged by the availability of many resources originally developed for English texts.

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    18. Automated Transformation of openEHR Data Instances to OWL.

      Automated Transformation of openEHR Data Instances to OWL.

      Automated Transformation of openEHR Data Instances to OWL.

      Stud Health Technol Inform. 2016;223:63-70

      Authors: Haarbrandt B, Jack T, Marschollek M

      Abstract Standard-based integration and semantic enrichment of clinical data originating from electronic medical records has shown to be critical to enable secondary use.

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    19. Is the ISO Reference Terminology Model for Nursing Actions Enough to Describe Nursing Actions?

      Is the ISO Reference Terminology Model for Nursing Actions Enough to Describe Nursing Actions?

      Is the ISO Reference Terminology Model for Nursing Actions Enough to Describe Nursing Actions?

      Stud Health Technol Inform. 2016;225:1022-3

      Authors: Lee JY, Park HA

      Abstract The aim of this study is to test the applicability of the International Standards Organization (ISO) Reference terminology model (RTM) for nursing action to describe Detailed Clinical Models (DCMs) for nursing action. All verb and target terms were mapped to 'Action' and 'Target' category of RTM for nursing actions.

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    20. Challenges in adapting existing clinical natural language processing systems to multiple, diverse health care settings.

      Challenges in adapting existing clinical natural language processing systems to multiple, diverse health care settings.

      Challenges in adapting existing clinical natural language processing systems to multiple, diverse health care settings.

      J Am Med Inform Assoc. 2017 Apr 17;:

      Authors: Carrell DS, Schoen RE, Leffler DA, Morris M, Rose S, Baer A, Crockett SD, Gourevitch RA, Dean KM, Mehrotra A

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    21. Opportunities for developing therapies for rare genetic diseases: focus on gain-of-function and allostery.

      Opportunities for developing therapies for rare genetic diseases: focus on gain-of-function and allostery.

      Opportunities for developing therapies for rare genetic diseases: focus on gain-of-function and allostery.

      Orphanet J Rare Dis. 2017 Apr 17;12(1):61

      Authors: Chen B, Altman RB

      Abstract BACKGROUND: Advances in next generation sequencing technologies have revolutionized our ability to discover the causes of rare genetic diseases. However, developing treatments for these diseases remains challenging.

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      Mentions: FDA
    22. Using a Text-Mining Approach to Evaluate the Quality of Nursing Records.

      Using a Text-Mining Approach to Evaluate the Quality of Nursing Records.

      Using a Text-Mining Approach to Evaluate the Quality of Nursing Records.

      Stud Health Technol Inform. 2016;225:813-4

      Authors: Chang HM, Chiou SF, Liu HY, Yu HC

      Abstract Nursing records in Taiwan have been computerized, but their quality has rarely been discussed. Therefore, this study employed a text-mining approach and a cross-sectional retrospective research design to evaluate the quality of electronic nursing records at a medical center in Northern Taiwan.

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    23. Harmonising ICNP and Snomed Ct: A Model for Effective Collaboration.

      Harmonising ICNP and Snomed Ct: A Model for Effective Collaboration.

      Harmonising ICNP and SNOMED CT: A Model for Effective Collaboration.

      Stud Health Technol Inform. 2016;225:744-5

      Authors: Hardiker N

      Abstract The purpose of this panel was to demonstrate an approach to collaborative working within nursing and health informatics. The panel took as an example an initiative to harmonise between two large-scale terminologies, namely the International Classification for Nursing Practice (ICNP) and SNOMED Clinical Terms (SNOMED CT).

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    25-48 of 2696 « 1 2 3 4 5 ... 111 112 113 »
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      Discourse, Entailment, Machine Translation, NER, Parsing, Segmentation, Semantic, Sentiment, Summarization, WSD