BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

222 related articles for article (PubMed ID: 32548637)

  • 21. Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals.
    Teixeira PL; Wei WQ; Cronin RM; Mo H; VanHouten JP; Carroll RJ; LaRose E; Bastarache LA; Rosenbloom ST; Edwards TL; Roden DM; Lasko TA; Dart RA; Nikolai AM; Peissig PL; Denny JC
    J Am Med Inform Assoc; 2017 Jan; 24(1):162-171. PubMed ID: 27497800
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Building bridges across electronic health record systems through inferred phenotypic topics.
    Chen Y; Ghosh J; Bejan CA; Gunter CA; Gupta S; Kho A; Liebovitz D; Sun J; Denny J; Malin B
    J Biomed Inform; 2015 Jun; 55():82-93. PubMed ID: 25841328
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Semi-supervised ROC analysis for reliable and streamlined evaluation of phenotyping algorithms.
    Gao J; Bonzel CL; Hong C; Varghese P; Zakir K; Gronsbell J
    J Am Med Inform Assoc; 2024 Feb; 31(3):640-650. PubMed ID: 38128118
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Clinical phenotyping in selected national networks: demonstrating the need for high-throughput, portable, and computational methods.
    Richesson RL; Sun J; Pathak J; Kho AN; Denny JC
    Artif Intell Med; 2016 Jul; 71():57-61. PubMed ID: 27506131
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Electronic medical record phenotyping using the anchor and learn framework.
    Halpern Y; Horng S; Choi Y; Sontag D
    J Am Med Inform Assoc; 2016 Jul; 23(4):731-40. PubMed ID: 27107443
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Applying active learning to high-throughput phenotyping algorithms for electronic health records data.
    Chen Y; Carroll RJ; Hinz ER; Shah A; Eyler AE; Denny JC; Xu H
    J Am Med Inform Assoc; 2013 Dec; 20(e2):e253-9. PubMed ID: 23851443
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Selection of Clinical Text Features for Classifying Suicide Attempts.
    Buckland RS; Hogan JW; Chen ES
    AMIA Annu Symp Proc; 2020; 2020():273-282. PubMed ID: 33936399
    [TBL] [Abstract][Full Text] [Related]  

  • 28. A high-throughput phenotyping algorithm is portable from adult to pediatric populations.
    Geva A; Liu M; Panickan VA; Avillach P; Cai T; Mandl KD
    J Am Med Inform Assoc; 2021 Jun; 28(6):1265-1269. PubMed ID: 33594412
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Evaluating resources composing the PheMAP knowledge base to enhance high-throughput phenotyping.
    Wan NC; Yaqoob AA; Ong HH; Zhao J; Wei WQ
    J Am Med Inform Assoc; 2023 Feb; 30(3):456-465. PubMed ID: 36451277
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Estimating summary statistics for electronic health record laboratory data for use in high-throughput phenotyping algorithms.
    Albers DJ; Elhadad N; Claassen J; Perotte R; Goldstein A; Hripcsak G
    J Biomed Inform; 2018 Feb; 78():87-101. PubMed ID: 29369797
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Cardiology record multi-label classification using latent Dirichlet allocation.
    Pérez J; Pérez A; Casillas A; Gojenola K
    Comput Methods Programs Biomed; 2018 Oct; 164():111-119. PubMed ID: 30195419
    [TBL] [Abstract][Full Text] [Related]  

  • 32. High-throughput phenotyping with temporal sequences.
    Estiri H; Strasser ZH; Murphy SN
    J Am Med Inform Assoc; 2021 Mar; 28(4):772-781. PubMed ID: 33313899
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Trends and opportunities in computable clinical phenotyping: A scoping review.
    He T; Belouali A; Patricoski J; Lehmann H; Ball R; Anagnostou V; Kreimeyer K; Botsis T
    J Biomed Inform; 2023 Apr; 140():104335. PubMed ID: 36933631
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Temporal phenotyping of medically complex children via PARAFAC2 tensor factorization.
    Perros I; Papalexakis EE; Vuduc R; Searles E; Sun J
    J Biomed Inform; 2019 May; 93():103125. PubMed ID: 30743070
    [TBL] [Abstract][Full Text] [Related]  

  • 35. The use of electronic health records for psychiatric phenotyping and genomics.
    Smoller JW
    Am J Med Genet B Neuropsychiatr Genet; 2018 Oct; 177(7):601-612. PubMed ID: 28557243
    [TBL] [Abstract][Full Text] [Related]  

  • 36. A methodology of phenotyping ICU patients from EHR data: High-fidelity, personalized, and interpretable phenotypes estimation.
    Wang Y; Stroh JN; Hripcsak G; Low Wang CC; Bennett TD; Wrobel J; Der Nigoghossian C; Mueller SW; Claassen J; Albers DJ
    J Biomed Inform; 2023 Dec; 148():104547. PubMed ID: 37984547
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Incorporating natural language processing to improve classification of axial spondyloarthritis using electronic health records.
    Zhao SS; Hong C; Cai T; Xu C; Huang J; Ermann J; Goodson NJ; Solomon DH; Cai T; Liao KP
    Rheumatology (Oxford); 2020 May; 59(5):1059-1065. PubMed ID: 31535693
    [TBL] [Abstract][Full Text] [Related]  

  • 38. A semi-supervised adaptive Markov Gaussian embedding process (SAMGEP) for prediction of phenotype event times using the electronic health record.
    Ahuja Y; Wen J; Hong C; Xia Z; Huang S; Cai T
    Sci Rep; 2022 Oct; 12(1):17737. PubMed ID: 36273240
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Electronic Health Record Phenotypes for Precision Medicine: Perspectives and Caveats From Treatment of Breast Cancer at a Single Institution.
    Breitenstein MK; Liu H; Maxwell KN; Pathak J; Zhang R
    Clin Transl Sci; 2018 Jan; 11(1):85-92. PubMed ID: 29084368
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Ensembles of natural language processing systems for portable phenotyping solutions.
    Liu C; Ta CN; Rogers JR; Li Z; Lee J; Butler AM; Shang N; Kury FSP; Wang L; Shen F; Liu H; Ena L; Friedman C; Weng C
    J Biomed Inform; 2019 Dec; 100():103318. PubMed ID: 31655273
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 12.