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 *

310 related articles for article (PubMed ID: 34879829)

  • 1. Identifying and evaluating clinical subtypes of Alzheimer's disease in care electronic health records using unsupervised machine learning.
    Alexander N; Alexander DC; Barkhof F; Denaxas S
    BMC Med Inform Decis Mak; 2021 Dec; 21(1):343. PubMed ID: 34879829
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Using Unsupervised Learning to Identify Clinical Subtypes of Alzheimer's Disease in Electronic Health Records.
    Alexander N; Alexander DC; Barkhof F; Denaxas S
    Stud Health Technol Inform; 2020 Jun; 270():499-503. PubMed ID: 32570434
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas.
    Crider K; Williams J; Qi YP; Gutman J; Yeung L; Mai C; Finkelstain J; Mehta S; Pons-Duran C; Menéndez C; Moraleda C; Rogers L; Daniels K; Green P
    Cochrane Database Syst Rev; 2022 Feb; 2(2022):. PubMed ID: 36321557
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Identifying clinically important COPD sub-types using data-driven approaches in primary care population based electronic health records.
    Pikoula M; Quint JK; Nissen F; Hemingway H; Smeeth L; Denaxas S
    BMC Med Inform Decis Mak; 2019 Apr; 19(1):86. PubMed ID: 30999919
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Identifying subtypes of type 2 diabetes mellitus with machine learning: development, internal validation, prognostic validation and medication burden in linked electronic health records in 420 448 individuals.
    Mizani MA; Dashtban A; Pasea L; Zeng Q; Khunti K; Valabhji J; Mamza JB; Gao H; Morris T; Banerjee A
    BMJ Open Diabetes Res Care; 2024 Jun; 12(3):. PubMed ID: 38834334
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Identifying subtypes of heart failure from three electronic health record sources with machine learning: an external, prognostic, and genetic validation study.
    Banerjee A; Dashtban A; Chen S; Pasea L; Thygesen JH; Fatemifar G; Tyl B; Dyszynski T; Asselbergs FW; Lund LH; Lumbers T; Denaxas S; Hemingway H
    Lancet Digit Health; 2023 Jun; 5(6):e370-e379. PubMed ID: 37236697
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. Data-driven identification of endophenotypes of Alzheimer's disease progression: implications for clinical trials and therapeutic interventions.
    Geifman N; Kennedy RE; Schneider LS; Buchan I; Brinton RD
    Alzheimers Res Ther; 2018 Jan; 10(1):4. PubMed ID: 29370871
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Robust Discovery of Mild Cognitive Impairment Subtypes and Their Risk of Alzheimer's Disease Conversion Using Unsupervised Machine Learning and Gaussian Mixture Modeling.
    Nezhadmoghadam F; Martinez-Torteya A; Treviño V; Martínez E; Santos A; Tamez-Peña J; Alzheimer's Disease Neuroimaging Initiative
    Curr Alzheimer Res; 2021; 18(7):595-606. PubMed ID: 34488612
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Exploration of critical care data by using unsupervised machine learning.
    Hyun S; Kaewprag P; Cooper C; Hixon B; Moffatt-Bruce S
    Comput Methods Programs Biomed; 2020 Oct; 194():105507. PubMed ID: 32403049
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Data-driven identification of ageing-related diseases from electronic health records.
    Kuan V; Fraser HC; Hingorani M; Denaxas S; Gonzalez-Izquierdo A; Direk K; Nitsch D; Mathur R; Parisinos CA; Lumbers RT; Sofat R; Wong ICK; Casas JP; Thornton JM; Hemingway H; Partridge L; Hingorani AD
    Sci Rep; 2021 Feb; 11(1):2938. PubMed ID: 33536532
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Identifying subtypes of chronic kidney disease with machine learning: development, internal validation and prognostic validation using linked electronic health records in 350,067 individuals.
    Dashtban A; Mizani MA; Pasea L; Denaxas S; Corbett R; Mamza JB; Gao H; Morris T; Hemingway H; Banerjee A
    EBioMedicine; 2023 Mar; 89():104489. PubMed ID: 36857859
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Comparison of machine learning clustering algorithms for detecting heterogeneity of treatment effect in acute respiratory distress syndrome: A secondary analysis of three randomised controlled trials.
    Sinha P; Spicer A; Delucchi KL; McAuley DF; Calfee CS; Churpek MM
    EBioMedicine; 2021 Dec; 74():103697. PubMed ID: 34861492
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Unsupervised Machine Learning to Identify Separable Clinical Alzheimer's Disease Sub-Populations.
    Prakash J; Wang V; Quinn RE; Mitchell CS
    Brain Sci; 2021 Jul; 11(8):. PubMed ID: 34439596
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A clustering approach for detecting implausible observation values in electronic health records data.
    Estiri H; Klann JG; Murphy SN
    BMC Med Inform Decis Mak; 2019 Jul; 19(1):142. PubMed ID: 31337390
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Challenges of Clustering Multimodal Clinical Data: Review of Applications in Asthma Subtyping.
    Horne E; Tibble H; Sheikh A; Tsanas A
    JMIR Med Inform; 2020 May; 8(5):e16452. PubMed ID: 32463370
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Subtyping of children with developmental dyslexia via bootstrap aggregated clustering and the gap statistic: comparison with the double-deficit hypothesis.
    King WM; Giess SA; Lombardino LJ
    Int J Lang Commun Disord; 2007; 42(1):77-95. PubMed ID: 17365087
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Longitudinal K-means approaches to clustering and analyzing EHR opioid use trajectories for clinical subtypes.
    Mullin S; Zola J; Lee R; Hu J; MacKenzie B; Brickman A; Anaya G; Sinha S; Li A; Elkin PL
    J Biomed Inform; 2021 Oct; 122():103889. PubMed ID: 34411708
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Feature selection for unsupervised machine learning of accelerometer data physical activity clusters - A systematic review.
    Jones PJ; Catt M; Davies MJ; Edwardson CL; Mirkes EM; Khunti K; Yates T; Rowlands AV
    Gait Posture; 2021 Oct; 90():120-128. PubMed ID: 34438293
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Unsupervised Learning for Automated Detection of Coronary Artery Disease Subgroups.
    Flores AM; Schuler A; Eberhard AV; Olin JW; Cooke JP; Leeper NJ; Shah NH; Ross EG
    J Am Heart Assoc; 2021 Dec; 10(23):e021976. PubMed ID: 34845917
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 16.