Cargando…
Comparison of deep learning with traditional models to predict preventable acute care use and spending among heart failure patients
Recent health reforms have created incentives for cardiologists and accountable care organizations to participate in value-based care models for heart failure (HF). Accurate risk stratification of HF patients is critical to efficiently deploy interventions aimed at reducing preventable utilization....
Autores principales: | , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7806727/ https://www.ncbi.nlm.nih.gov/pubmed/33441908 http://dx.doi.org/10.1038/s41598-020-80856-3 |
_version_ | 1783636585243213824 |
---|---|
author | Lewis, Maor Elad, Guy Beladev, Moran Maor, Gal Radinsky, Kira Hermann, Dor Litani, Yoav Geller, Tal Pines, Jesse M. Shapiro, Nathan l. Figueroa, Jose F. |
author_facet | Lewis, Maor Elad, Guy Beladev, Moran Maor, Gal Radinsky, Kira Hermann, Dor Litani, Yoav Geller, Tal Pines, Jesse M. Shapiro, Nathan l. Figueroa, Jose F. |
author_sort | Lewis, Maor |
collection | PubMed |
description | Recent health reforms have created incentives for cardiologists and accountable care organizations to participate in value-based care models for heart failure (HF). Accurate risk stratification of HF patients is critical to efficiently deploy interventions aimed at reducing preventable utilization. The goal of this paper was to compare deep learning approaches with traditional logistic regression (LR) to predict preventable utilization among HF patients. We conducted a prognostic study using data on 93,260 HF patients continuously enrolled for 2-years in a large U.S. commercial insurer to develop and validate prediction models for three outcomes of interest: preventable hospitalizations, preventable emergency department (ED) visits, and preventable costs. Patients were split into training, validation, and testing samples. Outcomes were modeled using traditional and enhanced LR and compared to gradient boosting model and deep learning models using sequential and non-sequential inputs. Evaluation metrics included precision (positive predictive value) at k, cost capture, and Area Under the Receiver operating characteristic (AUROC). Deep learning models consistently outperformed LR for all three outcomes with respect to the chosen evaluation metrics. Precision at 1% for preventable hospitalizations was 43% for deep learning compared to 30% for enhanced LR. Precision at 1% for preventable ED visits was 39% for deep learning compared to 33% for enhanced LR. For preventable cost, cost capture at 1% was 30% for sequential deep learning, compared to 18% for enhanced LR. The highest AUROCs for deep learning were 0.778, 0.681 and 0.727, respectively. These results offer a promising approach to identify patients for targeted interventions. |
format | Online Article Text |
id | pubmed-7806727 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-78067272021-01-14 Comparison of deep learning with traditional models to predict preventable acute care use and spending among heart failure patients Lewis, Maor Elad, Guy Beladev, Moran Maor, Gal Radinsky, Kira Hermann, Dor Litani, Yoav Geller, Tal Pines, Jesse M. Shapiro, Nathan l. Figueroa, Jose F. Sci Rep Article Recent health reforms have created incentives for cardiologists and accountable care organizations to participate in value-based care models for heart failure (HF). Accurate risk stratification of HF patients is critical to efficiently deploy interventions aimed at reducing preventable utilization. The goal of this paper was to compare deep learning approaches with traditional logistic regression (LR) to predict preventable utilization among HF patients. We conducted a prognostic study using data on 93,260 HF patients continuously enrolled for 2-years in a large U.S. commercial insurer to develop and validate prediction models for three outcomes of interest: preventable hospitalizations, preventable emergency department (ED) visits, and preventable costs. Patients were split into training, validation, and testing samples. Outcomes were modeled using traditional and enhanced LR and compared to gradient boosting model and deep learning models using sequential and non-sequential inputs. Evaluation metrics included precision (positive predictive value) at k, cost capture, and Area Under the Receiver operating characteristic (AUROC). Deep learning models consistently outperformed LR for all three outcomes with respect to the chosen evaluation metrics. Precision at 1% for preventable hospitalizations was 43% for deep learning compared to 30% for enhanced LR. Precision at 1% for preventable ED visits was 39% for deep learning compared to 33% for enhanced LR. For preventable cost, cost capture at 1% was 30% for sequential deep learning, compared to 18% for enhanced LR. The highest AUROCs for deep learning were 0.778, 0.681 and 0.727, respectively. These results offer a promising approach to identify patients for targeted interventions. Nature Publishing Group UK 2021-01-13 /pmc/articles/PMC7806727/ /pubmed/33441908 http://dx.doi.org/10.1038/s41598-020-80856-3 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Lewis, Maor Elad, Guy Beladev, Moran Maor, Gal Radinsky, Kira Hermann, Dor Litani, Yoav Geller, Tal Pines, Jesse M. Shapiro, Nathan l. Figueroa, Jose F. Comparison of deep learning with traditional models to predict preventable acute care use and spending among heart failure patients |
title | Comparison of deep learning with traditional models to predict preventable acute care use and spending among heart failure patients |
title_full | Comparison of deep learning with traditional models to predict preventable acute care use and spending among heart failure patients |
title_fullStr | Comparison of deep learning with traditional models to predict preventable acute care use and spending among heart failure patients |
title_full_unstemmed | Comparison of deep learning with traditional models to predict preventable acute care use and spending among heart failure patients |
title_short | Comparison of deep learning with traditional models to predict preventable acute care use and spending among heart failure patients |
title_sort | comparison of deep learning with traditional models to predict preventable acute care use and spending among heart failure patients |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7806727/ https://www.ncbi.nlm.nih.gov/pubmed/33441908 http://dx.doi.org/10.1038/s41598-020-80856-3 |
work_keys_str_mv | AT lewismaor comparisonofdeeplearningwithtraditionalmodelstopredictpreventableacutecareuseandspendingamongheartfailurepatients AT eladguy comparisonofdeeplearningwithtraditionalmodelstopredictpreventableacutecareuseandspendingamongheartfailurepatients AT beladevmoran comparisonofdeeplearningwithtraditionalmodelstopredictpreventableacutecareuseandspendingamongheartfailurepatients AT maorgal comparisonofdeeplearningwithtraditionalmodelstopredictpreventableacutecareuseandspendingamongheartfailurepatients AT radinskykira comparisonofdeeplearningwithtraditionalmodelstopredictpreventableacutecareuseandspendingamongheartfailurepatients AT hermanndor comparisonofdeeplearningwithtraditionalmodelstopredictpreventableacutecareuseandspendingamongheartfailurepatients AT litaniyoav comparisonofdeeplearningwithtraditionalmodelstopredictpreventableacutecareuseandspendingamongheartfailurepatients AT gellertal comparisonofdeeplearningwithtraditionalmodelstopredictpreventableacutecareuseandspendingamongheartfailurepatients AT pinesjessem comparisonofdeeplearningwithtraditionalmodelstopredictpreventableacutecareuseandspendingamongheartfailurepatients AT shapironathanl comparisonofdeeplearningwithtraditionalmodelstopredictpreventableacutecareuseandspendingamongheartfailurepatients AT figueroajosef comparisonofdeeplearningwithtraditionalmodelstopredictpreventableacutecareuseandspendingamongheartfailurepatients |