Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data.
Journal
NPJ digital medicine
ISSN: 2398-6352
Titre abrégé: NPJ Digit Med
Pays: England
ID NLM: 101731738
Informations de publication
Date de publication:
12 Feb 2021
12 Feb 2021
Historique:
received:
18
08
2020
accepted:
11
01
2021
entrez:
13
2
2021
pubmed:
14
2
2021
medline:
14
2
2021
Statut:
epublish
Résumé
Across jurisdictions, government and health insurance providers hold a large amount of data from patient interactions with the healthcare system. We aimed to develop a machine learning-based model for predicting adverse outcomes due to diabetes complications using administrative health data from the single-payer health system in Ontario, Canada. A Gradient Boosting Decision Tree model was trained on data from 1,029,366 patients, validated on 272,864 patients, and tested on 265,406 patients. Discrimination was assessed using the AUC statistic and calibration was assessed visually using calibration plots overall and across population subgroups. Our model predicting three-year risk of adverse outcomes due to diabetes complications (hyper/hypoglycemia, tissue infection, retinopathy, cardiovascular events, amputation) included 700 features from multiple diverse data sources and had strong discrimination (average test AUC = 77.7, range 77.7-77.9). Through the design and validation of a high-performance model to predict diabetes complications adverse outcomes at the population level, we demonstrate the potential of machine learning and administrative health data to inform health planning and healthcare resource allocation for diabetes management.
Identifiants
pubmed: 33580109
doi: 10.1038/s41746-021-00394-8
pii: 10.1038/s41746-021-00394-8
pmc: PMC7881135
doi:
Types de publication
Journal Article
Langues
eng
Pagination
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