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Leveraging deep survival models to predict quality of care risk in diverse hospital readmissions
Hospital readmissions rate is reportedly high and has caused huge financial burden on health care systems in many countries. It is viewed as an important indicator of health care providers’ quality of care. We examine the use of machine learning-based survival analysis to assess quality of care risk...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10307854/ https://www.ncbi.nlm.nih.gov/pubmed/37380704 http://dx.doi.org/10.1038/s41598-023-37477-3 |
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author | Tran, Nhat Quang Goel, Gautam Pudota, Nirmala Suesserman, Michael Helms, John Lasaga, Daniel Olson, Dan Bowen, Edward Bhattacharya, Sanmitra |
author_facet | Tran, Nhat Quang Goel, Gautam Pudota, Nirmala Suesserman, Michael Helms, John Lasaga, Daniel Olson, Dan Bowen, Edward Bhattacharya, Sanmitra |
author_sort | Tran, Nhat Quang |
collection | PubMed |
description | Hospital readmissions rate is reportedly high and has caused huge financial burden on health care systems in many countries. It is viewed as an important indicator of health care providers’ quality of care. We examine the use of machine learning-based survival analysis to assess quality of care risk in hospital readmissions. This study applies various survival models to explore the risk of hospital readmissions given patient demographics and their respective hospital discharges extracted from a health care claims dataset. We explore advanced feature representation techniques such as BioBERT and Node2Vec to encode high-dimensional diagnosis code features. To our knowledge, this study is the first to apply deep-learning based survival-analysis models for predicting hospital readmission risk agnostic of specific medical conditions and a fixed window for readmission. We found that modeling the time from discharge date to readmission date as a Weibull distribution as in the SparseDeepWeiSurv model yields the best discriminative power and calibration. In addition, embedding representations of the diagnosis codes do not contribute to improvement in model performance. We find dependency of each model’s performance on the time point at which it is evaluated. This time dependency of the models’ performance on the health care claims data may necessitate a different choice of model in quality of care issue detection at different points in time. We show the effectiveness of deep-learning based survival-analysis models in estimating the quality of care risk in hospital readmissions. |
format | Online Article Text |
id | pubmed-10307854 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-103078542023-06-30 Leveraging deep survival models to predict quality of care risk in diverse hospital readmissions Tran, Nhat Quang Goel, Gautam Pudota, Nirmala Suesserman, Michael Helms, John Lasaga, Daniel Olson, Dan Bowen, Edward Bhattacharya, Sanmitra Sci Rep Article Hospital readmissions rate is reportedly high and has caused huge financial burden on health care systems in many countries. It is viewed as an important indicator of health care providers’ quality of care. We examine the use of machine learning-based survival analysis to assess quality of care risk in hospital readmissions. This study applies various survival models to explore the risk of hospital readmissions given patient demographics and their respective hospital discharges extracted from a health care claims dataset. We explore advanced feature representation techniques such as BioBERT and Node2Vec to encode high-dimensional diagnosis code features. To our knowledge, this study is the first to apply deep-learning based survival-analysis models for predicting hospital readmission risk agnostic of specific medical conditions and a fixed window for readmission. We found that modeling the time from discharge date to readmission date as a Weibull distribution as in the SparseDeepWeiSurv model yields the best discriminative power and calibration. In addition, embedding representations of the diagnosis codes do not contribute to improvement in model performance. We find dependency of each model’s performance on the time point at which it is evaluated. This time dependency of the models’ performance on the health care claims data may necessitate a different choice of model in quality of care issue detection at different points in time. We show the effectiveness of deep-learning based survival-analysis models in estimating the quality of care risk in hospital readmissions. Nature Publishing Group UK 2023-06-28 /pmc/articles/PMC10307854/ /pubmed/37380704 http://dx.doi.org/10.1038/s41598-023-37477-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Tran, Nhat Quang Goel, Gautam Pudota, Nirmala Suesserman, Michael Helms, John Lasaga, Daniel Olson, Dan Bowen, Edward Bhattacharya, Sanmitra Leveraging deep survival models to predict quality of care risk in diverse hospital readmissions |
title | Leveraging deep survival models to predict quality of care risk in diverse hospital readmissions |
title_full | Leveraging deep survival models to predict quality of care risk in diverse hospital readmissions |
title_fullStr | Leveraging deep survival models to predict quality of care risk in diverse hospital readmissions |
title_full_unstemmed | Leveraging deep survival models to predict quality of care risk in diverse hospital readmissions |
title_short | Leveraging deep survival models to predict quality of care risk in diverse hospital readmissions |
title_sort | leveraging deep survival models to predict quality of care risk in diverse hospital readmissions |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10307854/ https://www.ncbi.nlm.nih.gov/pubmed/37380704 http://dx.doi.org/10.1038/s41598-023-37477-3 |
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