Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data
Abstract 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...
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2021-02-01
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Online Access: | https://doi.org/10.1038/s41746-021-00394-8 |
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doaj-618f79c514f043dc8f26ffb41f8e1df62021-04-02T21:31:29ZengNature Publishing Groupnpj Digital Medicine2398-63522021-02-014111210.1038/s41746-021-00394-8Predicting adverse outcomes due to diabetes complications with machine learning using administrative health dataMathieu Ravaut0Hamed Sadeghi1Kin Kwan Leung2Maksims Volkovs3Kathy Kornas4Vinyas Harish5Tristan Watson6Gary F. Lewis7Alanna Weisman8Tomi Poutanen9Laura Rosella10Layer 6 AILayer 6 AILayer 6 AILayer 6 AIDalla Lana School of Public Health, University of TorontoDalla Lana School of Public Health, University of TorontoDalla Lana School of Public Health, University of TorontoDepartment of Medicine, Temerty Faculty of Medicine, University of TorontoLunenfeld-Tanenbaum Research Institute, Mt. Sinai HospitalLayer 6 AIDalla Lana School of Public Health, University of TorontoAbstract 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.https://doi.org/10.1038/s41746-021-00394-8 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mathieu Ravaut Hamed Sadeghi Kin Kwan Leung Maksims Volkovs Kathy Kornas Vinyas Harish Tristan Watson Gary F. Lewis Alanna Weisman Tomi Poutanen Laura Rosella |
spellingShingle |
Mathieu Ravaut Hamed Sadeghi Kin Kwan Leung Maksims Volkovs Kathy Kornas Vinyas Harish Tristan Watson Gary F. Lewis Alanna Weisman Tomi Poutanen Laura Rosella Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data npj Digital Medicine |
author_facet |
Mathieu Ravaut Hamed Sadeghi Kin Kwan Leung Maksims Volkovs Kathy Kornas Vinyas Harish Tristan Watson Gary F. Lewis Alanna Weisman Tomi Poutanen Laura Rosella |
author_sort |
Mathieu Ravaut |
title |
Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data |
title_short |
Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data |
title_full |
Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data |
title_fullStr |
Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data |
title_full_unstemmed |
Predicting adverse outcomes due to diabetes complications with machine learning using administrative health data |
title_sort |
predicting adverse outcomes due to diabetes complications with machine learning using administrative health data |
publisher |
Nature Publishing Group |
series |
npj Digital Medicine |
issn |
2398-6352 |
publishDate |
2021-02-01 |
description |
Abstract 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. |
url |
https://doi.org/10.1038/s41746-021-00394-8 |
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