BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

1033 related articles for article (PubMed ID: 33058101)

  • 1. Designing an openEHR-Based Pipeline for Extracting and Standardizing Unstructured Clinical Data Using Natural Language Processing.
    Wulff A; Mast M; Hassler M; Montag S; Marschollek M; Jack T
    Methods Inf Med; 2020 Dec; 59(S 02):e64-e78. PubMed ID: 33058101
    [TBL] [Abstract][Full Text] [Related]  

  • 2. An interoperable clinical decision-support system for early detection of SIRS in pediatric intensive care using openEHR.
    Wulff A; Haarbrandt B; Tute E; Marschollek M; Beerbaum P; Jack T
    Artif Intell Med; 2018 Jul; 89():10-23. PubMed ID: 29753616
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Using openEHR Archetypes for Automated Extraction of Numerical Information from Clinical Narratives.
    Zubke M; Bott OJ; Marschollek M
    Stud Health Technol Inform; 2019 Sep; 267():156-163. PubMed ID: 31483268
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Structuring Legacy Pathology Reports by openEHR Archetypes to Enable Semantic Querying.
    Kropf S; Krücken P; Mueller W; Denecke K
    Methods Inf Med; 2017 May; 56(3):230-237. PubMed ID: 28244546
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Programming techniques for improving rule readability for rule-based information extraction natural language processing pipelines of unstructured and semi-structured medical texts.
    Ladas N; Borchert F; Franz S; Rehberg A; Strauch N; Sommer KK; Marschollek M; Gietzelt M
    Health Informatics J; 2023; 29(2):14604582231164696. PubMed ID: 37068028
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Extracting Medical Information From Free-Text and Unstructured Patient-Generated Health Data Using Natural Language Processing Methods: Feasibility Study With Real-world Data.
    Sezgin E; Hussain SA; Rust S; Huang Y
    JMIR Form Res; 2023 Mar; 7():e43014. PubMed ID: 36881467
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Using NLP in openEHR archetypes retrieval to promote interoperability: a feasibility study in China.
    Sun B; Zhang F; Li J; Yang Y; Diao X; Zhao W; Shu T
    BMC Med Inform Decis Mak; 2021 Jun; 21(1):199. PubMed ID: 34174874
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Identification of Preanesthetic History Elements by a Natural Language Processing Engine.
    Suh HS; Tully JL; Meineke MN; Waterman RS; Gabriel RA
    Anesth Analg; 2022 Dec; 135(6):1162-1171. PubMed ID: 35841317
    [TBL] [Abstract][Full Text] [Related]  

  • 9. openEHR Archetype Use and Reuse Within Multilingual Clinical Data Sets: Case Study.
    Leslie H
    J Med Internet Res; 2020 Nov; 22(11):e23361. PubMed ID: 33035176
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Application of openEHR archetypes to automate data quality rules for electronic health records: a case study.
    Tian Q; Han Z; Yu P; An J; Lu X; Duan H
    BMC Med Inform Decis Mak; 2021 Apr; 21(1):113. PubMed ID: 33812388
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Development of an openEHR Template for COVID-19 Based on Clinical Guidelines.
    Li M; Leslie H; Qi B; Nan S; Feng H; Cai H; Lu X; Duan H
    J Med Internet Res; 2020 Jun; 22(6):e20239. PubMed ID: 32496207
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Modeling EHR with the openEHR approach: an exploratory study in China.
    Min L; Tian Q; Lu X; Duan H
    BMC Med Inform Decis Mak; 2018 Aug; 18(1):75. PubMed ID: 30157838
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Archetype-based conversion of EHR content models: pilot experience with a regional EHR system.
    Chen R; Klein GO; Sundvall E; Karlsson D; Ahlfeldt H
    BMC Med Inform Decis Mak; 2009 Jul; 9():33. PubMed ID: 19570196
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Natural language processing for automatic evaluation of free-text answers - a feasibility study based on the European Diploma in Radiology examination.
    Stoehr F; Kämpgen B; Müller L; Zufiría LO; Junquero V; Merino C; Mildenberger P; Kloeckner R
    Insights Imaging; 2023 Sep; 14(1):150. PubMed ID: 37726485
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Knowledge Author: facilitating user-driven, domain content development to support clinical information extraction.
    Scuba W; Tharp M; Mowery D; Tseytlin E; Liu Y; Drews FA; Chapman WW
    J Biomed Semantics; 2016 Jun; 7(1):42. PubMed ID: 27338146
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Supervised methods to extract clinical events from cardiology reports in Italian.
    Viani N; Miller TA; Napolitano C; Priori SG; Savova GK; Bellazzi R; Sacchi L
    J Biomed Inform; 2019 Jul; 95():103219. PubMed ID: 31150777
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Transformation of standardized clinical models based on OWL technologies: from CEM to OpenEHR archetypes.
    Legaz-García Mdel C; Menárguez-Tortosa M; Fernández-Breis JT; Chute CG; Tao C
    J Am Med Inform Assoc; 2015 May; 22(3):536-44. PubMed ID: 25670753
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review.
    Kreimeyer K; Foster M; Pandey A; Arya N; Halford G; Jones SF; Forshee R; Walderhaug M; Botsis T
    J Biomed Inform; 2017 Sep; 73():14-29. PubMed ID: 28729030
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Facilitating clinical research through automation: Combining optical character recognition with natural language processing.
    Hom J; Nikowitz J; Ottesen R; Niland JC
    Clin Trials; 2022 Oct; 19(5):504-511. PubMed ID: 35608136
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Developing a scalable FHIR-based clinical data normalization pipeline for standardizing and integrating unstructured and structured electronic health record data.
    Hong N; Wen A; Shen F; Sohn S; Wang C; Liu H; Jiang G
    JAMIA Open; 2019 Dec; 2(4):570-579. PubMed ID: 32025655
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 52.