BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

194 related articles for article (PubMed ID: 35488252)

  • 1. Improving medical term embeddings using UMLS Metathesaurus.
    Chanda AK; Bai T; Yang Z; Vucetic S
    BMC Med Inform Decis Mak; 2022 Apr; 22(1):114. PubMed ID: 35488252
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A comparison of word embeddings for the biomedical natural language processing.
    Wang Y; Liu S; Afzal N; Rastegar-Mojarad M; Wang L; Shen F; Kingsbury P; Liu H
    J Biomed Inform; 2018 Nov; 87():12-20. PubMed ID: 30217670
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes.
    Ye J; Yao L; Shen J; Janarthanam R; Luo Y
    BMC Med Inform Decis Mak; 2020 Dec; 20(Suppl 11):295. PubMed ID: 33380338
    [TBL] [Abstract][Full Text] [Related]  

  • 4. The impact of learning Unified Medical Language System knowledge embeddings in relation extraction from biomedical texts.
    Weinzierl MA; Maldonado R; Harabagiu SM
    J Am Med Inform Assoc; 2020 Oct; 27(10):1556-1567. PubMed ID: 33029619
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach.
    Weng WH; Wagholikar KB; McCray AT; Szolovits P; Chueh HC
    BMC Med Inform Decis Mak; 2017 Dec; 17(1):155. PubMed ID: 29191207
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Evaluating semantic relations in neural word embeddings with biomedical and general domain knowledge bases.
    Chen Z; He Z; Liu X; Bian J
    BMC Med Inform Decis Mak; 2018 Jul; 18(Suppl 2):65. PubMed ID: 30066651
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Use of word and graph embedding to measure semantic relatedness between Unified Medical Language System concepts.
    Mao Y; Fung KW
    J Am Med Inform Assoc; 2020 Oct; 27(10):1538-1546. PubMed ID: 33029614
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A Natural Language Processing System That Links Medical Terms in Electronic Health Record Notes to Lay Definitions: System Development Using Physician Reviews.
    Chen J; Druhl E; Polepalli Ramesh B; Houston TK; Brandt CA; Zulman DM; Vimalananda VG; Malkani S; Yu H
    J Med Internet Res; 2018 Jan; 20(1):e26. PubMed ID: 29358159
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Use of "off-the-shelf" information extraction algorithms in clinical informatics: A feasibility study of MetaMap annotation of Italian medical notes.
    Chiaramello E; Pinciroli F; Bonalumi A; Caroli A; Tognola G
    J Biomed Inform; 2016 Oct; 63():22-32. PubMed ID: 27444186
    [TBL] [Abstract][Full Text] [Related]  

  • 10. The Impact of Specialized Corpora for Word Embeddings in Natural Langage Understanding.
    Neuraz A; Rance B; Garcelon N; Llanos LC; Burgun A; Rosset S
    Stud Health Technol Inform; 2020 Jun; 270():432-436. PubMed ID: 32570421
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Unified Medical Language System term occurrences in clinical notes: a large-scale corpus analysis.
    Wu ST; Liu H; Li D; Tao C; Musen MA; Chute CG; Shah NH
    J Am Med Inform Assoc; 2012 Jun; 19(e1):e149-56. PubMed ID: 22493050
    [TBL] [Abstract][Full Text] [Related]  

  • 12. UMLS mapping and Word embeddings for ICD code assignment using the MIMIC-III intensive care database.
    Schafer H; Friedrich CM
    Annu Int Conf IEEE Eng Med Biol Soc; 2019 Jul; 2019():6089-6092. PubMed ID: 31947234
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Using word embeddings to expand terminology of dietary supplements on clinical notes.
    Fan Y; Pakhomov S; McEwan R; Zhao W; Lindemann E; Zhang R
    JAMIA Open; 2019 Jul; 2(2):246-253. PubMed ID: 31825016
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Ontology-driven and weakly supervised rare disease identification from clinical notes.
    Dong H; Suárez-Paniagua V; Zhang H; Wang M; Casey A; Davidson E; Chen J; Alex B; Whiteley W; Wu H
    BMC Med Inform Decis Mak; 2023 May; 23(1):86. PubMed ID: 37147628
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Improving search over Electronic Health Records using UMLS-based query expansion through random walks.
    Martinez D; Otegi A; Soroa A; Agirre E
    J Biomed Inform; 2014 Oct; 51():100-6. PubMed ID: 24768598
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Effectively processing medical term queries on the UMLS Metathesaurus by layered dynamic programming.
    Ren K; Lai AM; Mukhopadhyay A; Machiraju R; Huang K; Xiang Y
    BMC Med Genomics; 2014; 7 Suppl 1(Suppl 1):S11. PubMed ID: 25079259
    [TBL] [Abstract][Full Text] [Related]  

  • 17. EHR phenotyping via jointly embedding medical concepts and words into a unified vector space.
    Bai T; Chanda AK; Egleston BL; Vucetic S
    BMC Med Inform Decis Mak; 2018 Dec; 18(Suppl 4):123. PubMed ID: 30537974
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Risk prediction using natural language processing of electronic mental health records in an inpatient forensic psychiatry setting.
    Le DV; Montgomery J; Kirkby KC; Scanlan J
    J Biomed Inform; 2018 Oct; 86():49-58. PubMed ID: 30118855
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Leveraging medical context to recommend semantically similar terms for chart reviews.
    Ye C; Malin BA; Fabbri D
    BMC Med Inform Decis Mak; 2021 Dec; 21(1):353. PubMed ID: 34922536
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Projection Word Embedding Model With Hybrid Sampling Training for Classifying ICD-10-CM Codes: Longitudinal Observational Study.
    Lin C; Lou YS; Tsai DJ; Lee CC; Hsu CJ; Wu DC; Wang MC; Fang WH
    JMIR Med Inform; 2019 Jul; 7(3):e14499. PubMed ID: 31339103
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.