These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

446 related articles for article (PubMed ID: 31857264)

  • 1. Natural Language Processing Combined with ICD-9-CM Codes as a Novel Method to Study the Epidemiology of Allergic Drug Reactions.
    Banerji A; Lai KH; Li Y; Saff RR; Camargo CA; Blumenthal KG; Zhou L
    J Allergy Clin Immunol Pract; 2020 Mar; 8(3):1032-1038.e1. PubMed ID: 31857264
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Utility of ICD-9-CM Codes for Identification of Allergic Drug Reactions.
    Saff RR; Camargo CA; Clark S; Rudders SA; Long AA; Banerji A
    J Allergy Clin Immunol Pract; 2016; 4(1):114-9.e1. PubMed ID: 26372539
    [TBL] [Abstract][Full Text] [Related]  

  • 3. The use of natural language processing to identify vaccine-related anaphylaxis at five health care systems in the Vaccine Safety Datalink.
    Yu W; Zheng C; Xie F; Chen W; Mercado C; Sy LS; Qian L; Glenn S; Tseng HF; Lee G; Duffy J; McNeil MM; Daley MF; Crane B; McLean HQ; Jackson LA; Jacobsen SJ
    Pharmacoepidemiol Drug Saf; 2020 Feb; 29(2):182-188. PubMed ID: 31797475
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Using natural language processing to identify opioid use disorder in electronic health record data.
    Singleton J; Li C; Akpunonu PD; Abner EL; Kucharska-Newton AM
    Int J Med Inform; 2023 Feb; 170():104963. PubMed ID: 36521420
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Identification of Inpatient Allergic Drug Reactions Using ICD-9-CM Codes.
    Saff RR; Li Y; Santhanakrishnan N; Camargo CA; Blumenthal KG; Zhou L; Banerji A
    J Allergy Clin Immunol Pract; 2019 Jan; 7(1):259-264.e1. PubMed ID: 30075337
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. Developing and validating natural language processing algorithms for radiology reports compared to ICD-10 codes for identifying venous thromboembolism in hospitalized medical patients.
    Verma AA; Masoom H; Pou-Prom C; Shin S; Guerzhoy M; Fralick M; Mamdani M; Razak F
    Thromb Res; 2022 Jan; 209():51-58. PubMed ID: 34871982
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Natural language processing of electronic health records is superior to billing codes to identify symptom burden in hemodialysis patients.
    Chan L; Beers K; Yau AA; Chauhan K; Duffy Á; Chaudhary K; Debnath N; Saha A; Pattharanitima P; Cho J; Kotanko P; Federman A; Coca SG; Van Vleck T; Nadkarni GN
    Kidney Int; 2020 Feb; 97(2):383-392. PubMed ID: 31883805
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Natural language processing of clinical notes for identification of critical limb ischemia.
    Afzal N; Mallipeddi VP; Sohn S; Liu H; Chaudhry R; Scott CG; Kullo IJ; Arruda-Olson AM
    Int J Med Inform; 2018 Mar; 111():83-89. PubMed ID: 29425639
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Enhancing ICD-Code-Based Case Definition for Heart Failure Using Electronic Medical Record Data.
    Xu Y; Lee S; Martin E; D'souza AG; Doktorchik CTA; Jiang J; Lee S; Eastwood CA; Fine N; Hemmelgarn B; Todd K; Quan H
    J Card Fail; 2020 Jul; 26(7):610-617. PubMed ID: 32304875
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Ascertaining Framingham heart failure phenotype from inpatient electronic health record data using natural language processing: a multicentre Atherosclerosis Risk in Communities (ARIC) validation study.
    Moore CR; Jain S; Haas S; Yadav H; Whitsel E; Rosamand W; Heiss G; Kucharska-Newton AM
    BMJ Open; 2021 Jun; 11(6):e047356. PubMed ID: 34127492
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Ascertainment of Delirium Status Using Natural Language Processing From Electronic Health Records.
    Fu S; Lopes GS; Pagali SR; Thorsteinsdottir B; LeBrasseur NK; Wen A; Liu H; Rocca WA; Olson JE; St Sauver J; Sohn S
    J Gerontol A Biol Sci Med Sci; 2022 Mar; 77(3):524-530. PubMed ID: 35239951
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A comparison of natural language processing to ICD-10 codes for identification and characterization of pulmonary embolism.
    Johnson SA; Signor EA; Lappe KL; Shi J; Jenkins SL; Wikstrom SW; Kroencke RD; Hallowell D; Jones AE; Witt DM
    Thromb Res; 2021 Jul; 203():190-195. PubMed ID: 34044246
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Validation of Case Finding Algorithms for Hepatocellular Cancer From Administrative Data and Electronic Health Records Using Natural Language Processing.
    Sada Y; Hou J; Richardson P; El-Serag H; Davila J
    Med Care; 2016 Feb; 54(2):e9-14. PubMed ID: 23929403
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Defining a Patient Population With Cirrhosis: An Automated Algorithm With Natural Language Processing.
    Chang EK; Yu CY; Clarke R; Hackbarth A; Sanders T; Esrailian E; Hommes DW; Runyon BA
    J Clin Gastroenterol; 2016; 50(10):889-894. PubMed ID: 27348317
    [TBL] [Abstract][Full Text] [Related]  

  • 16. The use of natural language processing to identify Tdap-related local reactions at five health care systems in the Vaccine Safety Datalink.
    Zheng C; Yu W; Xie F; Chen W; Mercado C; Sy LS; Qian L; Glenn S; Lee G; Tseng HF; Duffy J; Jackson LA; Daley MF; Crane B; McLean HQ; Jacobsen SJ
    Int J Med Inform; 2019 Jul; 127():27-34. PubMed ID: 31128829
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Positive Predictive Value of
    Ivey LC; Rodriguez FH; Shi H; Chong C; Chen J; Raskind-Hood CL; Downing KF; Farr SL; Book WM
    J Am Heart Assoc; 2023 Aug; 12(16):e030821. PubMed ID: 37548168
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Cerebrovascular disease case identification in inpatient electronic medical record data using natural language processing.
    Pan J; Zhang Z; Peters SR; Vatanpour S; Walker RL; Lee S; Martin EA; Quan H
    Brain Inform; 2023 Sep; 10(1):22. PubMed ID: 37658963
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Development and Validation of a Natural Language Processing Tool to Identify Patients Treated for Pneumonia across VA Emergency Departments.
    Jones BE; South BR; Shao Y; Lu CC; Leng J; Sauer BC; Gundlapalli AV; Samore MH; Zeng Q
    Appl Clin Inform; 2018 Jan; 9(1):122-128. PubMed ID: 29466818
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Rule-based and machine learning algorithms identify patients with systemic sclerosis accurately in the electronic health record.
    Jamian L; Wheless L; Crofford LJ; Barnado A
    Arthritis Res Ther; 2019 Dec; 21(1):305. PubMed ID: 31888720
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 23.