BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

653 related articles for article (PubMed ID: 27185194)

  • 1. Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records.
    Miotto R; Li L; Kidd BA; Dudley JT
    Sci Rep; 2016 May; 6():26094. PubMed ID: 27185194
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Deep representation learning of electronic health records to unlock patient stratification at scale.
    Landi I; Glicksberg BS; Lee HC; Cherng S; Landi G; Danieletto M; Dudley JT; Furlanello C; Miotto R
    NPJ Digit Med; 2020; 3():96. PubMed ID: 32699826
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Representation learning for clinical time series prediction tasks in electronic health records.
    Ruan T; Lei L; Zhou Y; Zhai J; Zhang L; He P; Gao J
    BMC Med Inform Decis Mak; 2019 Dec; 19(Suppl 8):259. PubMed ID: 31842854
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review.
    Si Y; Du J; Li Z; Jiang X; Miller T; Wang F; Jim Zheng W; Roberts K
    J Biomed Inform; 2021 Mar; 115():103671. PubMed ID: 33387683
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Optimizing Autoencoders for Learning Deep Representations From Health Data.
    Zhou C; Jia Y; Motani M
    IEEE J Biomed Health Inform; 2019 Jan; 23(1):103-111. PubMed ID: 30028714
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Predictive modeling of structured electronic health records for adverse drug event detection.
    Zhao J; Henriksson A; Asker L; Boström H
    BMC Med Inform Decis Mak; 2015; 15 Suppl 4(Suppl 4):S1. PubMed ID: 26606038
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Prediction task guided representation learning of medical codes in EHR.
    Cui L; Xie X; Shen Z
    J Biomed Inform; 2018 Aug; 84():1-10. PubMed ID: 29928997
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Combining structured and unstructured data for predictive models: a deep learning approach.
    Zhang D; Yin C; Zeng J; Yuan X; Zhang P
    BMC Med Inform Decis Mak; 2020 Oct; 20(1):280. PubMed ID: 33121479
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A deep learning-based, unsupervised method to impute missing values in electronic health records for improved patient management.
    Xu D; Hu PJ; Huang TS; Fang X; Hsu CC
    J Biomed Inform; 2020 Nov; 111():103576. PubMed ID: 33010424
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Treatment effect prediction with adversarial deep learning using electronic health records.
    Chu J; Dong W; Wang J; He K; Huang Z
    BMC Med Inform Decis Mak; 2020 Dec; 20(Suppl 4):139. PubMed ID: 33317502
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Semi-supervised learning of the electronic health record for phenotype stratification.
    Beaulieu-Jones BK; Greene CS;
    J Biomed Inform; 2016 Dec; 64():168-178. PubMed ID: 27744022
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A machine learning-based framework to identify type 2 diabetes through electronic health records.
    Zheng T; Xie W; Xu L; He X; Zhang Y; You M; Yang G; Chen Y
    Int J Med Inform; 2017 Jan; 97():120-127. PubMed ID: 27919371
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Modeling Healthcare Quality via Compact Representations of Electronic Health Records.
    Stojanovic J; Gligorijevic D; Radosavljevic V; Djuric N; Grbovic M; Obradovic Z
    IEEE/ACM Trans Comput Biol Bioinform; 2017; 14(3):545-554. PubMed ID: 27429443
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Applying interpretable deep learning models to identify chronic cough patients using EHR data.
    Luo X; Gandhi P; Zhang Z; Shao W; Han Z; Chandrasekaran V; Turzhitsky V; Bali V; Roberts AR; Metzger M; Baker J; La Rosa C; Weaver J; Dexter P; Huang K
    Comput Methods Programs Biomed; 2021 Oct; 210():106395. PubMed ID: 34525412
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. Incorporating temporal EHR data in predictive models for risk stratification of renal function deterioration.
    Singh A; Nadkarni G; Gottesman O; Ellis SB; Bottinger EP; Guttag JV
    J Biomed Inform; 2015 Feb; 53():220-8. PubMed ID: 25460205
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis.
    Shickel B; Tighe PJ; Bihorac A; Rashidi P
    IEEE J Biomed Health Inform; 2018 Sep; 22(5):1589-1604. PubMed ID: 29989977
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Ensembles of randomized trees using diverse distributed representations of clinical events.
    Henriksson A; Zhao J; Dalianis H; Boström H
    BMC Med Inform Decis Mak; 2016 Jul; 16 Suppl 2(Suppl 2):69. PubMed ID: 27459846
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Unsupervised machine learning for the discovery of latent disease clusters and patient subgroups using electronic health records.
    Wang Y; Zhao Y; Therneau TM; Atkinson EJ; Tafti AP; Zhang N; Amin S; Limper AH; Khosla S; Liu H
    J Biomed Inform; 2020 Feb; 102():103364. PubMed ID: 31891765
    [TBL] [Abstract][Full Text] [Related]  

  • 20. DeepMPM: a mortality risk prediction model using longitudinal EHR data.
    Yang F; Zhang J; Chen W; Lai Y; Wang Y; Zou Q
    BMC Bioinformatics; 2022 Oct; 23(1):423. PubMed ID: 36241976
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 33.