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Forecasting inpatient glycemic control: extension of damped trend methods to subpopulations

AIM: Evaluate forecasting models applied to smaller geographic locations within the hospital. MATERIALS & METHODS: Damped trend models were applied to blood glucose measurements of progressively smaller inpatient geographic subpopulations. Mean absolute percentage error (MAPE) and 95% prediction...

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Autores principales: Saulnier, George E, Castro, Janna C, Cook, Curtiss B
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Future Science Ltd 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7720372/
http://dx.doi.org/10.2144/fsoa-2020-0096
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author Saulnier, George E
Castro, Janna C
Cook, Curtiss B
author_facet Saulnier, George E
Castro, Janna C
Cook, Curtiss B
author_sort Saulnier, George E
collection PubMed
description AIM: Evaluate forecasting models applied to smaller geographic locations within the hospital. MATERIALS & METHODS: Damped trend models were applied to blood glucose measurements of progressively smaller inpatient geographic subpopulations. Mean absolute percentage error (MAPE) and 95% prediction intervals (PIs) assessed validity of the models to forecasts 48 weeks into the future. RESULTS: MAPE values increased, and 95% PIs widened, when data from progressively smaller geographic areas were analyzed. MAPE values were highest and 95% PIs were broadest with the smallest geographic areas. In contrast, observations missed at larger geographical locations were more evident with smaller subpopulations. CONCLUSION: The utility of damped trend models to forecast inpatient glucose control diminished when applied to smaller geographic areas within the hospital.
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spelling pubmed-77203722020-12-11 Forecasting inpatient glycemic control: extension of damped trend methods to subpopulations Saulnier, George E Castro, Janna C Cook, Curtiss B Future Sci OA Research Article AIM: Evaluate forecasting models applied to smaller geographic locations within the hospital. MATERIALS & METHODS: Damped trend models were applied to blood glucose measurements of progressively smaller inpatient geographic subpopulations. Mean absolute percentage error (MAPE) and 95% prediction intervals (PIs) assessed validity of the models to forecasts 48 weeks into the future. RESULTS: MAPE values increased, and 95% PIs widened, when data from progressively smaller geographic areas were analyzed. MAPE values were highest and 95% PIs were broadest with the smallest geographic areas. In contrast, observations missed at larger geographical locations were more evident with smaller subpopulations. CONCLUSION: The utility of damped trend models to forecast inpatient glucose control diminished when applied to smaller geographic areas within the hospital. Future Science Ltd 2020-11-02 2020-09 /pmc/articles/PMC7720372/ http://dx.doi.org/10.2144/fsoa-2020-0096 Text en © 2020 Mayo Foundation for Medical Education and Research This work is licensed under the Creative Commons Attribution 4.0 License (http://creativecommons.org/licenses/by/4.0/)
spellingShingle Research Article
Saulnier, George E
Castro, Janna C
Cook, Curtiss B
Forecasting inpatient glycemic control: extension of damped trend methods to subpopulations
title Forecasting inpatient glycemic control: extension of damped trend methods to subpopulations
title_full Forecasting inpatient glycemic control: extension of damped trend methods to subpopulations
title_fullStr Forecasting inpatient glycemic control: extension of damped trend methods to subpopulations
title_full_unstemmed Forecasting inpatient glycemic control: extension of damped trend methods to subpopulations
title_short Forecasting inpatient glycemic control: extension of damped trend methods to subpopulations
title_sort forecasting inpatient glycemic control: extension of damped trend methods to subpopulations
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7720372/
http://dx.doi.org/10.2144/fsoa-2020-0096
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