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A process mining- deep learning approach to predict survival in a cohort of hospitalized COVID‐19 patients
BACKGROUND: Various machine learning and artificial intelligence methods have been used to predict outcomes of hospitalized COVID-19 patients. However, process mining has not yet been used for COVID-19 prediction. We developed a process mining/deep learning approach to predict mortality among COVID-...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9309593/ https://www.ncbi.nlm.nih.gov/pubmed/35879715 http://dx.doi.org/10.1186/s12911-022-01934-2 |
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author | Pishgar, M. Harford, S. Theis, J. Galanter, W. Rodríguez-Fernández, J. M. Chaisson, L. H Zhang, Y. Trotter, A. Kochendorfer, K. M. Boppana, A. Darabi, H. |
author_facet | Pishgar, M. Harford, S. Theis, J. Galanter, W. Rodríguez-Fernández, J. M. Chaisson, L. H Zhang, Y. Trotter, A. Kochendorfer, K. M. Boppana, A. Darabi, H. |
author_sort | Pishgar, M. |
collection | PubMed |
description | BACKGROUND: Various machine learning and artificial intelligence methods have been used to predict outcomes of hospitalized COVID-19 patients. However, process mining has not yet been used for COVID-19 prediction. We developed a process mining/deep learning approach to predict mortality among COVID-19 patients and updated the prediction in 6-h intervals during the first 72 h after hospital admission. METHODS: The process mining/deep learning model produced temporal information related to the variables and incorporated demographic and clinical data to predict mortality. The mortality prediction was updated in 6-h intervals during the first 72 h after hospital admission. Moreover, the performance of the model was compared with published and self-developed traditional machine learning models that did not use time as a variable. The performance was compared using the Area Under the Receiver Operator Curve (AUROC), accuracy, sensitivity, and specificity. RESULTS: The proposed process mining/deep learning model outperformed the comparison models in almost all time intervals with a robust AUROC above 80% on a dataset that was imbalanced. CONCLUSIONS: Our proposed process mining/deep learning model performed significantly better than commonly used machine learning approaches that ignore time information. Thus, time information should be incorporated in models to predict outcomes more accurately. |
format | Online Article Text |
id | pubmed-9309593 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-93095932022-07-25 A process mining- deep learning approach to predict survival in a cohort of hospitalized COVID‐19 patients Pishgar, M. Harford, S. Theis, J. Galanter, W. Rodríguez-Fernández, J. M. Chaisson, L. H Zhang, Y. Trotter, A. Kochendorfer, K. M. Boppana, A. Darabi, H. BMC Med Inform Decis Mak Research BACKGROUND: Various machine learning and artificial intelligence methods have been used to predict outcomes of hospitalized COVID-19 patients. However, process mining has not yet been used for COVID-19 prediction. We developed a process mining/deep learning approach to predict mortality among COVID-19 patients and updated the prediction in 6-h intervals during the first 72 h after hospital admission. METHODS: The process mining/deep learning model produced temporal information related to the variables and incorporated demographic and clinical data to predict mortality. The mortality prediction was updated in 6-h intervals during the first 72 h after hospital admission. Moreover, the performance of the model was compared with published and self-developed traditional machine learning models that did not use time as a variable. The performance was compared using the Area Under the Receiver Operator Curve (AUROC), accuracy, sensitivity, and specificity. RESULTS: The proposed process mining/deep learning model outperformed the comparison models in almost all time intervals with a robust AUROC above 80% on a dataset that was imbalanced. CONCLUSIONS: Our proposed process mining/deep learning model performed significantly better than commonly used machine learning approaches that ignore time information. Thus, time information should be incorporated in models to predict outcomes more accurately. BioMed Central 2022-07-25 /pmc/articles/PMC9309593/ /pubmed/35879715 http://dx.doi.org/10.1186/s12911-022-01934-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Pishgar, M. Harford, S. Theis, J. Galanter, W. Rodríguez-Fernández, J. M. Chaisson, L. H Zhang, Y. Trotter, A. Kochendorfer, K. M. Boppana, A. Darabi, H. A process mining- deep learning approach to predict survival in a cohort of hospitalized COVID‐19 patients |
title | A process mining- deep learning approach to predict survival in a cohort of hospitalized COVID‐19 patients |
title_full | A process mining- deep learning approach to predict survival in a cohort of hospitalized COVID‐19 patients |
title_fullStr | A process mining- deep learning approach to predict survival in a cohort of hospitalized COVID‐19 patients |
title_full_unstemmed | A process mining- deep learning approach to predict survival in a cohort of hospitalized COVID‐19 patients |
title_short | A process mining- deep learning approach to predict survival in a cohort of hospitalized COVID‐19 patients |
title_sort | process mining- deep learning approach to predict survival in a cohort of hospitalized covid‐19 patients |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9309593/ https://www.ncbi.nlm.nih.gov/pubmed/35879715 http://dx.doi.org/10.1186/s12911-022-01934-2 |
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