BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

294 related articles for article (PubMed ID: 32565422)

  • 1. Validation of a type 1 diabetes algorithm using electronic medical records and administrative healthcare data to study the population incidence and prevalence of type 1 diabetes in Ontario, Canada.
    Weisman A; Tu K; Young J; Kumar M; Austin PC; Jaakkimainen L; Lipscombe L; Aronson R; Booth GL
    BMJ Open Diabetes Res Care; 2020 Jun; 8(1):. PubMed ID: 32565422
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Prevalence and Incidence Trends of Attention Deficit/Hyperactivity Disorder in Children and Youth Aged 1-24 Years in Ontario, Canada: A Validation Study of Health Administrative Data Algorithms: Tendances de la prévalence et de l'incidence du trouble de déficit de l'attention/hyperactivité chez les enfants et les jeunes âgés de 1 à 24 ans, en Ontario, Canada: une étude de validation des algorithmes de données administratives de santé.
    Butt DA; Jaakkimainen L; Tu K
    Can J Psychiatry; 2024 May; 69(5):326-336. PubMed ID: 37960872
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Identification of Physician-Diagnosed Alzheimer's Disease and Related Dementias in Population-Based Administrative Data: A Validation Study Using Family Physicians' Electronic Medical Records.
    Jaakkimainen RL; Bronskill SE; Tierney MC; Herrmann N; Green D; Young J; Ivers N; Butt D; Widdifield J; Tu K
    J Alzheimers Dis; 2016 Aug; 54(1):337-49. PubMed ID: 27567819
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Development and validation of an administrative data algorithm to estimate the disease burden and epidemiology of multiple sclerosis in Ontario, Canada.
    Widdifield J; Ivers NM; Young J; Green D; Jaakkimainen L; Butt DA; O'Connor P; Hollands S; Tu K
    Mult Scler; 2015 Jul; 21(8):1045-54. PubMed ID: 25392338
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Development and validation of algorithms to classify type 1 and 2 diabetes according to age at diagnosis using electronic health records.
    Ke C; Stukel TA; Luk A; Shah BR; Jha P; Lau E; Ma RCW; So WY; Kong AP; Chow E; Chan JCN
    BMC Med Res Methodol; 2020 Feb; 20(1):35. PubMed ID: 32093635
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A validation study of administrative data algorithms to identify patients with Parkinsonism with prevalence and incidence trends.
    Butt DA; Tu K; Young J; Green D; Wang M; Ivers N; Jaakkimainen L; Lam R; Guttman M
    Neuroepidemiology; 2014; 43(1):28-37. PubMed ID: 25323155
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Development of algorithms to identify individuals with Neurofibromatosis type 1 within administrative data and electronic medical records in Ontario, Canada.
    Barnett C; Candido E; Chen B; Pequeno P; Parkin PC; Tu K
    Orphanet J Rare Dis; 2022 Aug; 17(1):321. PubMed ID: 36028856
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Diabetes in Ontario: determination of prevalence and incidence using a validated administrative data algorithm.
    Hux JE; Ivis F; Flintoft V; Bica A
    Diabetes Care; 2002 Mar; 25(3):512-6. PubMed ID: 11874939
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Identification of Early Onset Dementia in Population-Based Health Administrative Data: A Validation Study Using Primary Care Electronic Medical Records.
    Jaakkimainen L; Duchen R; Lix L; Al-Azazi S; Yu B; Butt D; Park SB; Widdifield J
    J Alzheimers Dis; 2022; 89(4):1463-1472. PubMed ID: 36057820
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Validation of canadian health administrative data algorithms for estimating trends in the incidence and prevalence of osteoarthritis.
    Widdifield J; Jaakkimainen RL; Gatley JM; Hawker GA; Lix LM; Bernatsky S; Ravi B; Wasserstein D; Yu B; Tu K
    Osteoarthr Cartil Open; 2020 Dec; 2(4):100115. PubMed ID: 36474895
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Validation of international algorithms to identify adults with inflammatory bowel disease in health administrative data from Ontario, Canada.
    Benchimol EI; Guttmann A; Mack DR; Nguyen GC; Marshall JK; Gregor JC; Wong J; Forster AJ; Manuel DG
    J Clin Epidemiol; 2014 Aug; 67(8):887-96. PubMed ID: 24774473
    [TBL] [Abstract][Full Text] [Related]  

  • 12. An administrative data validation study of the accuracy of algorithms for identifying rheumatoid arthritis: the influence of the reference standard on algorithm performance.
    Widdifield J; Bombardier C; Bernatsky S; Paterson JM; Green D; Young J; Ivers N; Butt DA; Jaakkimainen RL; Thorne JC; Tu K
    BMC Musculoskelet Disord; 2014 Jun; 15():216. PubMed ID: 24956925
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Identifying cases of congestive heart failure from administrative data: a validation study using primary care patient records.
    Schultz SE; Rothwell DM; Chen Z; Tu K
    Chronic Dis Inj Can; 2013 Jun; 33(3):160-6. PubMed ID: 23735455
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Development and Validation of a Machine Learning Model Using Administrative Health Data to Predict Onset of Type 2 Diabetes.
    Ravaut M; Harish V; Sadeghi H; Leung KK; Volkovs M; Kornas K; Watson T; Poutanen T; Rosella LC
    JAMA Netw Open; 2021 May; 4(5):e2111315. PubMed ID: 34032855
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Validation of a health administrative data algorithm for assessing the epidemiology of diabetes in Canadian children.
    Guttmann A; Nakhla M; Henderson M; To T; Daneman D; Cauch-Dudek K; Wang X; Lam K; Hux J
    Pediatr Diabetes; 2010 Mar; 11(2):122-8. PubMed ID: 19500278
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Identifying diabetes cases from administrative data: a population-based validation study.
    Lipscombe LL; Hwee J; Webster L; Shah BR; Booth GL; Tu K
    BMC Health Serv Res; 2018 May; 18(1):316. PubMed ID: 29720153
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Identifying musculoskeletal conditions in electronic medical records: a prevalence and validation study using the Deliver Primary Healthcare Information (DELPHI) database.
    Ryan BL; Maddocks HL; McKay S; Petrella R; Terry AL; Stewart M
    BMC Musculoskelet Disord; 2019 May; 20(1):187. PubMed ID: 31053119
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Development and validation of algorithms to identify newly diagnosed type 1 and type 2 diabetes in pediatric population using electronic medical records and claims data.
    Teltsch DY; Fazeli Farsani S; Swain RS; Kaspers S; Huse S; Cristaldi C; Nordstrom BL; Brodovicz KG
    Pharmacoepidemiol Drug Saf; 2019 Feb; 28(2):234-243. PubMed ID: 30677205
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Assessing the validity of administrative health data for the identification of children and youth with autism spectrum disorder in Ontario.
    Brooks JD; Arneja J; Fu L; Saxena FE; Tu K; Pinzaru VB; Anagnostou E; Nylen K; Saunders NR; Lu H; McLaughlin J; Bronskill SE
    Autism Res; 2021 May; 14(5):1037-1045. PubMed ID: 33694293
    [TBL] [Abstract][Full Text] [Related]  

  • 20. The role of health system penetration rate in estimating the prevalence of type 1 diabetes in children and adolescents using electronic health records.
    Li P; Lyu T; Alkhuzam K; Spector E; Donahoo WT; Bost S; Wu Y; Hogan WR; Prosperi M; Schatz DA; Atkinson MA; Haller MJ; Shenkman EA; Guo Y; Bian J; Shao H
    J Am Med Inform Assoc; 2023 Dec; 31(1):165-173. PubMed ID: 37812771
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 15.