Machine learning models to predict onset of dementia: A label learning approach.
Alzheimers Dement (N Y)
; 5: 918-925, 2019.
Article
em En
| MEDLINE
| ID: mdl-31879701
ABSTRACT
INTRODUCTION:
The study objective was to build a machine learning model to predict incident mild cognitive impairment, Alzheimer's Disease, and related dementias from structured data using administrative and electronic health record sources.METHODS:
A cohort of patients (n = 121,907) and controls (n = 5,307,045) was created for modeling using data within 2 years of patient's incident diagnosis date. Additional cohorts 3-8 years removed from index data are used for prediction. Training cohorts were matched on age, gender, index year, and utilization, and fit with a gradient boosting machine, lightGBM.RESULTS:
Incident 2-year model quality on a held-out test set had a sensitivity of 47% and area-under-the-curve of 87%. In the 3-year model, the learned labels achieved 24% (71%), which dropped to 15% (72%) in year 8.DISCUSSION:
The ability of the model to discriminate incident cases of dementia implies that it can be a worthwhile tool to screen patients for trial recruitment and patient management.
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MEDLINE
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En
Ano de publicação:
2019
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Article