1. 73-96 of 2664 « 1 2 3 4 5 6 7 ... 109 110 111 »
    1. Tashkeela: Novel corpus of Arabic vocalized texts, data for auto-diacritization systems.

      Tashkeela: Novel corpus of Arabic vocalized texts, data for auto-diacritization systems.

      Tashkeela: Novel corpus of Arabic vocalized texts, data for auto-diacritization systems.

      Data Brief. 2017 Apr;11:147-151

      Authors: Zerrouki T, Balla A

      Abstract Arabic diacritics are often missed in Arabic scripts. This feature is a handicap for new learner to read ŮŽArabic, text to speech conversion systems, reading and semantic analysis of Arabic texts. The automatic diacritization systems are the best solution to handle this issue.

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    2. Home Health Care: Nurse-Physician Communication, Patient Severity, and Hospital Readmission.

      Home Health Care: Nurse-Physician Communication, Patient Severity, and Hospital Readmission.

      Home Health Care: Nurse-Physician Communication, Patient Severity, and Hospital Readmission.

      Health Serv Res. 2017 Feb 19;:

      Authors: Pesko MF, Gerber LM, Peng TR, Press MJ

      Abstract OBJECTIVE: To evaluate whether communication failures between home health care nurses and physicians during an episode of home care after hospital discharge are associated with hospital readmission, stratified by patients at high and low risk of readmission.

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      Mentions: New York Medicare
    3. Radiology Reports With Hyperlinks Improve Target Lesion Selection and Measurement Concordance in Cancer Trials.

      Radiology Reports With Hyperlinks Improve Target Lesion Selection and Measurement Concordance in Cancer Trials.

      Radiology Reports With Hyperlinks Improve Target Lesion Selection and Measurement Concordance in Cancer Trials.

      AJR Am J Roentgenol. 2017 Feb;208(2):W31-W37

      Authors: Machado LB, Apolo AB, Steinberg SM, Folio LR

      Abstract OBJECTIVE: Radiology reports often lack the measurements of target lesions that are needed for oncology clinical trials. When available, the measurements in the radiology reports often do not match those in the records used to calculate therapeutic response.

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      Mentions: Wilcoxon
    4. The Role of Report Comparison, Analysis, and Discrepancy Categorization in Resident Education.

      The Role of Report Comparison, Analysis, and Discrepancy Categorization in Resident Education.

      The Role of Report Comparison, Analysis, and Discrepancy Categorization in Resident Education.

      AJR Am J Roentgenol. 2016 Dec;207(6):1223-1231

      Authors: Harari AA, Conti MB, Bokhari SA, Staib LH, Taylor CR

      Abstract OBJECTIVE: The purpose of this study was to show the value of automated radiology report comparison and analysis in resident education by providing qualitative and quantitative feedback on the discrepancies between preliminary and finalized reports.

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    5. Assessing Quality of Care and Elder Abuse in Nursing Homes via Google Reviews.

      Assessing Quality of Care and Elder Abuse in Nursing Homes via Google Reviews.

      Assessing Quality of Care and Elder Abuse in Nursing Homes via Google Reviews.

      Online J Public Health Inform. 2016;8(3):e201

      Authors: Mowery J, Andrei A, Le E, Jian J, Ward M

      Abstract BACKGROUND: It is challenging to assess the quality of care and detect elder abuse in nursing homes, since patients may be incapable of reporting quality issues or abuse themselves, and resources for sending inspectors are limited.

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      Mentions: Google
    6. Twitter Influenza Surveillance: Quantifying Seasonal Misdiagnosis Patterns and their Impact on Surveillance Estimates.

      Twitter Influenza Surveillance: Quantifying Seasonal Misdiagnosis Patterns and their Impact on Surveillance Estimates.

      Twitter Influenza Surveillance: Quantifying Seasonal Misdiagnosis Patterns and their Impact on Surveillance Estimates.

      Online J Public Health Inform. 2016;8(3):e198

      Authors: Mowery J

      Abstract BACKGROUND: Influenza (flu) surveillance using Twitter data can potentially save lives and increase efficiency by providing governments and healthcare organizations with greater situational awareness.

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    7. Stacked Learning to Search for Scene Labeling.

      Stacked Learning to Search for Scene Labeling.

      Stacked Learning to Search for Scene Labeling.

      IEEE Trans Image Process. 2017 Feb 13;:

      Authors: Cheng F, He X, Zhang H

      Abstract Search-based structured prediction methods have shown promising successes in both computer vision and natural language processing recently. However, most existing search-based approaches lead to a complex multi-stage learning process, which is ill-suited for scene labeling problems with a high-dimensional output space.

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    8. Autism spectrum disorder detection from semi-structured and unstructured medical data.

      Autism spectrum disorder detection from semi-structured and unstructured medical data.

      Autism spectrum disorder detection from semi-structured and unstructured medical data.

      EURASIP J Bioinform Syst Biol. 2017 Dec;2017:3

      Authors: Yuan J, Holtz C, Smith T, Luo J

      Abstract Autism spectrum disorder (ASD) is a developmental disorder that significantly impairs patients' ability to perform normal social interaction and communication. Moreover, the diagnosis procedure of ASD is highly time-consuming, labor-intensive, and requires extensive expertise.

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      Mentions: Smith
    9. Mining peripheral arterial disease cases from narrative clinical notes using natural language processing.

      Mining peripheral arterial disease cases from narrative clinical notes using natural language processing.

      Mining peripheral arterial disease cases from narrative clinical notes using natural language processing.

      J Vasc Surg. 2017 Feb 08;:

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

      Abstract OBJECTIVE: Lower extremity peripheral arterial disease (PAD) is highly prevalent and affects millions of individuals worldwide.

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    10. Extraction of Left Ventricular Ejection Fraction Information from Various Types of Clinical Reports.

      Extraction of Left Ventricular Ejection Fraction Information from Various Types of Clinical Reports.

      Extraction of Left Ventricular Ejection Fraction Information from Various Types of Clinical Reports.

      J Biomed Inform. 2017 Feb 02;:

      Authors: Kim Y, Garvin JH, Goldstein MK, Hwang TS, Redd A, Bolton D, Heidenreich PA, Meystre SM

      Abstract Efforts to improve the treatment of congestive heart failure, a common and serious medical condition, include the use of quality measures to assess guideline-concordant care.

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    11. Identifying the missing proteins in human proteome by biological language model.

      Identifying the missing proteins in human proteome by biological language model.

      Identifying the missing proteins in human proteome by biological language model.

      BMC Syst Biol. 2016 Dec 23;10(Suppl 4):113

      Authors: Dong Q, Wang K, Liu X

      Abstract BACKGROUND: With the rapid development of high-throughput sequencing technology, the proteomics research becomes a trendy field in the post genomics era. It is necessary to identify all the native-encoding protein sequences for further function and pathway analysis.

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      Mentions: Liu
    12. A Character Level Based and Word Level Based Approach for Chinese-Vietnamese Machine Translation.

      A Character Level Based and Word Level Based Approach for Chinese-Vietnamese Machine Translation.

      A Character Level Based and Word Level Based Approach for Chinese-Vietnamese Machine Translation.

      Comput Intell Neurosci. 2016;2016:9821608

      Authors: Tran P, Dinh D, Nguyen HT

      Abstract Chinese and Vietnamese have the same isolated language; that is, the words are not delimited by spaces. In machine translation, word segmentation is often done first when translating from Chinese or Vietnamese into different languages (typically English) and vice versa.

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      Mentions: Nguyen
    13. Automatic Construction and Global Optimization of a Multisentiment Lexicon.

      Automatic Construction and Global Optimization of a Multisentiment Lexicon.

      Automatic Construction and Global Optimization of a Multisentiment Lexicon.

      Comput Intell Neurosci. 2016;2016:2093406

      Authors: Yang X, Zhang Z, Zhang Z, Mo Y, Li L, Yu L, Zhu P

      Abstract Manual annotation of sentiment lexicons costs too much labor and time, and it is also difficult to get accurate quantification of emotional intensity. Besides, the excessive emphasis on one specific field has greatly limited the applicability of domain sentiment lexicons (Wang et al., 2010).

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      Mentions: Li L
    14. MetaMap Lite: an evaluation of a new Java implementation of MetaMap.

      MetaMap Lite: an evaluation of a new Java implementation of MetaMap.

      MetaMap Lite: an evaluation of a new Java implementation of MetaMap.

      J Am Med Inform Assoc. 2017 Jan 27;:

      Authors: Demner-Fushman D, Rogers WJ, Aronson AR

      Abstract MetaMap is a widely used named entity recognition tool that identifies concepts from the Unified Medical Language System Metathesaurus in text. This study presents MetaMap Lite, an implementation of some of the basic MetaMap functions in Java.

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    15. Automated Data Aggregation for Time-Series Analysis: Study Case on Anaesthesia Data Warehouse.

      Automated Data Aggregation for Time-Series Analysis: Study Case on Anaesthesia Data Warehouse.

      Automated Data Aggregation for Time-Series Analysis: Study Case on Anaesthesia Data Warehouse.

      Stud Health Technol Inform. 2016;221:102-6

      Authors: Lamer A, Jeanne M, Ficheur G, Marcilly R

      Abstract Data stored in operational databases are not reusable directly. Aggregation modules are necessary to facilitate secondary use. They decrease volume of data while increasing the number of available information.

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    16. Elderly Surgical Patients: Automated Computation of Healthcare Quality Indicators by Data Reuse of EHR.

      Elderly Surgical Patients: Automated Computation of Healthcare Quality Indicators by Data Reuse of EHR.

      Elderly Surgical Patients: Automated Computation of Healthcare Quality Indicators by Data Reuse of EHR.

      Stud Health Technol Inform. 2016;221:92-6

      Authors: Ficheur G, Schaffar A, Caron A, Balcaen T, Beuscart JB, Chazard E

      Abstract UNLABELLED: The objective of the work is to implement and evaluate the automated computation of 9 healthcare quality indicators, by data reuse of electronic health records, in the field of elderly surgical patients.

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    17. Implementation of a Decision Support System for Interpretation of Laboratory Tests for Patients.

      Implementation of a Decision Support System for Interpretation of Laboratory Tests for Patients.

      Implementation of a Decision Support System for Interpretation of Laboratory Tests for Patients.

      Stud Health Technol Inform. 2016;221:79-83

      Authors: Semenov I, Kopanitsa G

      Abstract The paper presents the results of the development and implementation of an expert system that automatically generates doctors' letters based on the results of laboratory tests. Medical knowledge is expressed using a first order predictate logic based language.

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    18. Ontological Foundations for Tracking Data Quality through the Internet of Things.

      Ontological Foundations for Tracking Data Quality through the Internet of Things.

      Ontological Foundations for Tracking Data Quality through the Internet of Things.

      Stud Health Technol Inform. 2016;221:74-8

      Authors: Ceusters W, Bona J

      Abstract Amongst the positive outcomes expected from the Internet of Things for Health are longitudinal patient records that are more complete and less erroneous by complementing manual data entry with automatic data feeds from sensors. Unfortunately, devices are fallible too.

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    19. An Integrated Children Disease Prediction Tool within a Special Social Network.

      An Integrated Children Disease Prediction Tool within a Special Social Network.

      An Integrated Children Disease Prediction Tool within a Special Social Network.

      Stud Health Technol Inform. 2016;221:69-73

      Authors: Apostolova Trpkovska M, Yildirim Yayilgan S, Besimi A

      Abstract This paper proposes a social network with an integrated children disease prediction system developed by the use of the specially designed Children General Disease Ontology (CGDO).

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    20. Ontology-Oriented Programming for Biomedical Informatics.

      Ontology-Oriented Programming for Biomedical Informatics.

      Ontology-Oriented Programming for Biomedical Informatics.

      Stud Health Technol Inform. 2016;221:64-8

      Authors: Lamy JB

      Abstract Ontologies are now widely used in the biomedical domain. However, it is difficult to manipulate ontologies in a computer program and, consequently, it is not easy to integrate ontologies with databases or websites.

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    21. Remote Monitoring of Cardiac Implantable Devices: Ontology Driven Classification of the Alerts.

      Remote Monitoring of Cardiac Implantable Devices: Ontology Driven Classification of the Alerts.

      Remote Monitoring of Cardiac Implantable Devices: Ontology Driven Classification of the Alerts.

      Stud Health Technol Inform. 2016;221:59-63

      Authors: Rosier A, Mabo P, Temal L, Van Hille P, Dameron O, Deleger L, Grouin C, Zweigenbaum P, Jacques J, Chazard E, Laporte L, Henry C, Burgun A

      Read Full Article
    73-96 of 2664 « 1 2 3 4 5 6 7 ... 109 110 111 »
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      Discourse, Entailment, Machine Translation, NER, Parsing, Segmentation, Semantic, Sentiment, Summarization, WSD