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Lidia M V R Moura

Datos Biográficos

ID5539701
NOMBRELidia M V R Moura
NOMBRESLidia M V R
APELLIDOMoura
FIRMAMOURA L M V R
AFILIACIONESNeurology
ORCID0000-0002-1191-1315
VERIFICADOSí
TOTAL DE OBRAS3
TOTAL DE CITAS0
TOTAL COMO AUTOR3
TOTAL COMO EDITOR0
PRIMER AÑO DE PUBLICACIÓN2019
AÑO MÁS RECIENTE DE PUBLICACIÓN2022
ÍNDICE H0
  • Evaluation of Claims-Based Ascertainment of Alzheimer Disease and Related Dementias Across Health Care Settings

    Open Access•Natalia Festa, Mary Price et al.•ARTICLE•JAMA Health Forum•2022

    This cohort study evaluates the ascertainment of Alzheimer disease and related dementia using diagnostic codes in various health care settings

  • Identifying Medicare Beneficiaries With Delirium

    Lidia M V R Moura, Sahar Zafar et al.•ARTICLE•Medical Care•2022•Referencias: 33

    BACKGROUND: Each year, thousands of older adults develop delirium, a serious, preventable condition. At present, there is no well-validated method to identify patients with delirium when using Medicare claims data or other large datasets. We developed and assessed the performance of classification algorithms based on longitudinal Medicare administrative data that included International Classification of Diseases, 10th Edition diagnostic codes. ME…

  • Epilepsy Among Elderly Medicare Beneficiaries

    Lidia M V R Moura, Jason R Smith et al.•ARTICLE•Medical Care•2019•Referencias: 29

    BACKGROUND: Uncertain validity of epilepsy diagnoses within health insurance claims and other large datasets have hindered efforts to study and monitor care at the population level. OBJECTIVES: To develop and validate prediction models using longitudinal Medicare administrative data to identify patients with actual epilepsy among those with the diagnosis. RESEARCH DESIGN, SUBJECTS, MEASURES: We used linked electronic health records and Medicare a…

Sin obras prominentes en esta página.

  • Epilepsy Among Elderly Medicare Beneficiaries

    Lidia M V R Moura, Jason R Smith et al.•ARTICLE•Medical Care•2019•Referencias: 29

    BACKGROUND: Uncertain validity of epilepsy diagnoses within health insurance claims and other large datasets have hindered efforts to study and monitor care at the population level. OBJECTIVES: To develop and validate prediction models using longitudinal Medicare administrative data to identify patients with actual epilepsy among those with the diagnosis. RESEARCH DESIGN, SUBJECTS, MEASURES: We used linked electronic health records and Medicare a…

  • Evaluation of Claims-Based Ascertainment of Alzheimer Disease and Related Dementias Across Health Care Settings

    Open Access•Natalia Festa, Mary Price et al.•ARTICLE•JAMA Health Forum•2022

    This cohort study evaluates the ascertainment of Alzheimer disease and related dementia using diagnostic codes in various health care settings

  • Identifying Medicare Beneficiaries With Delirium

    Lidia M V R Moura, Sahar Zafar et al.•ARTICLE•Medical Care•2022•Referencias: 33

    BACKGROUND: Each year, thousands of older adults develop delirium, a serious, preventable condition. At present, there is no well-validated method to identify patients with delirium when using Medicare claims data or other large datasets. We developed and assessed the performance of classification algorithms based on longitudinal Medicare administrative data that included International Classification of Diseases, 10th Edition diagnostic codes. ME…

Medicine (3 obras) · Machine Learning in Healthcare (2 obras) · Psychiatry (2 obras) · Actuarial science (1 obras) · Alzheimer's disease (1 obras) · Anesthesia and Sedative Agents (1 obras) · Business (1 obras) · Confidence interval (1 obras) · Delirium (1 obras) · Dementia (1 obras)

Ethnos_APP • Proyecto Open Source • Licencia MIT • Frontend v2.0.0 • Privacidad y Cookies • Documentación de la API: api.ethnos.app/docs • Código de la API: GitHub • DOI: 10.5281/zenodo.17049435 • Código del Frontend: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae