i
Early prediction of Alzheimer’s disease and related dementias using real-world electronic health records
-
8 2023
-
Source: Alzheimers Dement. 19(8):3506-3518
Details:
-
Alternative Title:Alzheimers Dement
-
Personal Author:
-
Description:Introduction:
This study aims to explore machine learning (ML) methods for early prediction of Alzheimer’s disease (AD) and related dementias (ADRD) using the real-world electronic health records (EHRs).
Methods:
A total of 23,835 ADRD and 1,038,643 control patients were identified from the OneFlorida+ Research Consortium. Two ML methods were used to develop the prediction models. Both knowledge-driven and data-driven approaches were explored. Four computable phenotyping algorithms were tested.
Results:
The gradient boosting tree (GBT) models trained with the data-driven approach achieved the best area under the curve (AUC) scores of 0.939, 0.906, 0.884, and 0.854 for early prediction of ADRD 0, 1, 3, or 5 years before diagnosis, respectively. A number of important clinical and sociodemographic factors were identified.
Discussion:
We tested various settings and showed the predictive ability of using ML approaches for early prediction of ADRD with EHRs. The models can help identify high-risk individuals for early informed preventive or prognostic clinical decisions.
-
Subjects:
-
Keywords:
-
Source:
-
Pubmed ID:36815661
-
Pubmed Central ID:PMC10976442
-
Document Type:
-
Funding:
-
Collection(s):
-
Main Document Checksum:
-
Download URL:
-
File Type: