A PHP Error was encountered

Severity: Warning

Message: Creating default object from empty value

Filename: models/publicacao_item.php

Line Number: 20

Big Data Analytical Approaches to the NACC Dataset: Aiding... » Isaúde
  Pesquisar Publicações Científicas  
  Especialidade: carregando especialidades...  Carregando...
Nome da revista:   Volume:   Número:
Alzheimer Disease and Associated Disorders
2018-01-01 06:00:00

Big Data Analytical Approaches to the NACC Dataset: Aiding Preclinical Trial Enrichment

Descrição: Background: Clinical trials increasingly aim to retard disease progression during presymptomatic phases of Mild Cognitive Impairment (MCI) and thus recruiting study participants at high risk for developing MCI is critical for cost-effective prevention trials. However, accurately identifying those who are destined to develop MCI is difficult. Collecting biomarkers is often expensive. Methods: We used only noninvasive clinical variables collected in the National Alzheimer-s Coordinating Center (NACC) Uniform Data Sets version 2.0 and applied machine learning techniques to build a low-cost and accurate Mild Cognitive Impairment (MCI) conversion prediction calculator. Cross-validation and bootstrap were used to select as few variables as possible accurately predicting MCI conversion within 4 years. Results: A total of 31,872 unique subjects, 748 clinical variables, and additional 128 derived variables in NACC data sets were used. About 15 noninvasive clinical variables are identified for predicting MCI/aMCI/naMCI converters, respectively. Over 75% Receiver Operating Characteristic Area Under the Curves (ROC AUC) was achieved. By bootstrap we created a simple spreadsheet calculator which estimates the probability of developing MCI within 4 years with a 95% confidence interval. Conclusions: We achieved reasonably high prediction accuracy using only clinical variables. The approach used here could be useful for study enrichment in preclinical trials where enrolling participants at risk of cognitive decline is critical for proving study efficacy, and also for developing a shorter assessment battery.

Seção: Original Articles
Volume: 0
Autor: Ganguli, Mary; Albanese, Emiliano; Seshadri, Sudha; Bennett, David A.; Lyketsos, Constantine; Kukull, Walter A.; Skoog, Ingmar; Hendrie, Hugh C.


Mais informações

  • Twitter iSaúde
publicidade
Jornal Informe Saúde

Indique o portal
Fechar [X]
  • Você está indicando a notícia: http://www.isaude.net
  • Para que seu amigo(a) receba esta indicação preencha os dados abaixo:

RSS notícias do portal  iSaúde.net
Receba o newsletter do portal  iSaúde.net
Indique o portal iSaúde.net
Notícias do  iSaúde.net em seu blog ou site.
Receba notícias com assunto de seu interesse.
© 2000-2011 www.isaude.net Todos os direitos reservados.