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dc.contributor.authorYi, Seung Eunen_US
dc.contributor.authorHarish, Vinyasen_US
dc.contributor.authorGutierrez, Jahiren_US
dc.contributor.authorRavaut, Mathieuen_US
dc.contributor.authorKornas, Kathyen_US
dc.contributor.authorWatson, Tristanen_US
dc.contributor.authorPoutanen, Tomien_US
dc.contributor.authorGhassemi, Marzyehen_US
dc.contributor.authorVolkovs, Maksimsen_US
dc.contributor.authorRosella, Laura C.en_US
dc.identifier.citationYi, S. E., Harish, V., Gutierrez, J., Ravaut, M., Kornas, K., Watson, T., Poutanen, T., Ghassemi, M., Volkovs, M. & Rosella, L. C. (2022). Predicting hospitalisations related to ambulatory care sensitive conditions with machine learning for population health planning: derivation and validation cohort study. BMJ Open, 12(4), e051403-.
dc.description.abstractObjective: To predict older adults’ risk of avoidable hospitalisation related to ambulatory care sensitive conditions (ACSC) using machine learning applied to administrative health data of Ontario, Canada. Design, setting and participants: A retrospective cohort study was conducted on a large cohort of all residents covered under a single-payer system in Ontario, Canada over the period of 10 years (2008– 2017). The study included 1.85 million Ontario residents between 65 and 74 years old at any time throughout the study period. Data sources: Administrative health data from Ontario, Canada obtained from the (ICES formerly known as the Institute for Clinical Evaluative Sciences Data Repository. Main outcome measures: Risk of hospitalisations due to ACSCs 1 year after the observation period. Results: The study used a total of 1 854 116 patients, split into train, validation and test sets. The ACSC incidence rates among the data points were 1.1% for all sets. The final XGBoost model achieved an area under the receiver operating curve of 80.5% and an area under precision–recall curve of 0.093 on the test set, and the predictions were well calibrated, including in key subgroups. When ranking the model predictions, those at the top 5% of risk as predicted by the model captured 37.4% of those presented with an ACSC-related hospitalisation. A variety of features such as the previous number of ambulatory care visits, presence of ACSC-related hospitalisations during the observation window, age, rural residence and prescription of certain medications were contributors to the prediction. Our model was also able to capture the geospatial heterogeneity of ACSC risk in Ontario, and especially the elevated risk in rural and marginalised regions. Conclusions: This study aimed to predict the 1-year risk of hospitalisation from ambulatory-care sensitive conditions in seniors aged 65–74 years old with a single, large-scale machine learning model. The model shows the potential to inform population health planning and interventions to reduce the burden of ACSC-related hospitalisations.en_US
dc.relation.ispartofBMJ Openen_US
dc.rights© Author(s) (or their employer(s)) 2022. Published by BMJ. Open access. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:
dc.subjectEngineering::Computer science and engineeringen_US
dc.titlePredicting hospitalisations related to ambulatory care sensitive conditions with machine learning for population health planning: derivation and validation cohort studyen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.versionPublished versionen_US
dc.subject.keywordsAdministrative Health Dataen_US
dc.subject.keywordsAmbulatory Careen_US
dc.description.acknowledgementThis study was supported by ICES, which is funded by an annual grant from the Ontario Ministry of Health (MOH) and the Ministry of Long-Term Care (MLTC). This work was supported by the New Frontiers in Research Fund (NFRFE2018-00662), a Canada Research Chair in Population Health Analytics (950- 230702) (LR), Ontario Graduate Scholarship (number N/A) (VH), Canadian Institutes of Health Research Banting and Best Canada Graduate Scholarship Master’s and Doctoral awards (numbers N/A) (VH), and Vector Institute Post-graduate Fellowship (number N/A) (VH).en_US
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