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Artificial intelligence to predict in-hospital mortality using novel anatomical injury score
The aim of the study is to develop artificial intelligence (AI) algorithm based on a deep learning model to predict mortality using abbreviate injury score (AIS). The performance of the conventional anatomic injury severity score (ISS) system in predicting in-hospital mortality is still limited. AIS...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8651670/ https://www.ncbi.nlm.nih.gov/pubmed/34876644 http://dx.doi.org/10.1038/s41598-021-03024-1 |
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author | Kang, Wu Seong Chung, Heewon Ko, Hoon Kim, Nan Yeol Kim, Do Wan Cho, Jayun Shim, Hongjin Kim, Jin Goo Jang, Ji Young Kim, Kyung Won Lee, Jinseok |
author_facet | Kang, Wu Seong Chung, Heewon Ko, Hoon Kim, Nan Yeol Kim, Do Wan Cho, Jayun Shim, Hongjin Kim, Jin Goo Jang, Ji Young Kim, Kyung Won Lee, Jinseok |
author_sort | Kang, Wu Seong |
collection | PubMed |
description | The aim of the study is to develop artificial intelligence (AI) algorithm based on a deep learning model to predict mortality using abbreviate injury score (AIS). The performance of the conventional anatomic injury severity score (ISS) system in predicting in-hospital mortality is still limited. AIS data of 42,933 patients registered in the Korean trauma data bank from four Korean regional trauma centers were enrolled. After excluding patients who were younger than 19 years old and those who died within six hours from arrival, we included 37,762 patients, of which 36,493 (96.6%) survived and 1269 (3.4%) deceased. To enhance the AI model performance, we reduced the AIS codes to 46 input values by organizing them according to the organ location (Region-46). The total AIS and six categories of the anatomic region in the ISS system (Region-6) were used to compare the input features. The AI models were compared with the conventional ISS and new ISS (NISS) systems. We evaluated the performance pertaining to the 12 combinations of the features and models. The highest accuracy (85.05%) corresponded to Region-46 with DNN, followed by that of Region-6 with DNN (83.62%), AIS with DNN (81.27%), ISS-16 (80.50%), NISS-16 (79.18%), NISS-25 (77.09%), and ISS-25 (70.82%). The highest AUROC (0.9084) corresponded to Region-46 with DNN, followed by that of Region-6 with DNN (0.9013), AIS with DNN (0.8819), ISS (0.8709), and NISS (0.8681). The proposed deep learning scheme with feature combination exhibited high accuracy metrics such as the balanced accuracy and AUROC than the conventional ISS and NISS systems. We expect that our trial would be a cornerstone of more complex combination model. |
format | Online Article Text |
id | pubmed-8651670 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-86516702021-12-08 Artificial intelligence to predict in-hospital mortality using novel anatomical injury score Kang, Wu Seong Chung, Heewon Ko, Hoon Kim, Nan Yeol Kim, Do Wan Cho, Jayun Shim, Hongjin Kim, Jin Goo Jang, Ji Young Kim, Kyung Won Lee, Jinseok Sci Rep Article The aim of the study is to develop artificial intelligence (AI) algorithm based on a deep learning model to predict mortality using abbreviate injury score (AIS). The performance of the conventional anatomic injury severity score (ISS) system in predicting in-hospital mortality is still limited. AIS data of 42,933 patients registered in the Korean trauma data bank from four Korean regional trauma centers were enrolled. After excluding patients who were younger than 19 years old and those who died within six hours from arrival, we included 37,762 patients, of which 36,493 (96.6%) survived and 1269 (3.4%) deceased. To enhance the AI model performance, we reduced the AIS codes to 46 input values by organizing them according to the organ location (Region-46). The total AIS and six categories of the anatomic region in the ISS system (Region-6) were used to compare the input features. The AI models were compared with the conventional ISS and new ISS (NISS) systems. We evaluated the performance pertaining to the 12 combinations of the features and models. The highest accuracy (85.05%) corresponded to Region-46 with DNN, followed by that of Region-6 with DNN (83.62%), AIS with DNN (81.27%), ISS-16 (80.50%), NISS-16 (79.18%), NISS-25 (77.09%), and ISS-25 (70.82%). The highest AUROC (0.9084) corresponded to Region-46 with DNN, followed by that of Region-6 with DNN (0.9013), AIS with DNN (0.8819), ISS (0.8709), and NISS (0.8681). The proposed deep learning scheme with feature combination exhibited high accuracy metrics such as the balanced accuracy and AUROC than the conventional ISS and NISS systems. We expect that our trial would be a cornerstone of more complex combination model. Nature Publishing Group UK 2021-12-07 /pmc/articles/PMC8651670/ /pubmed/34876644 http://dx.doi.org/10.1038/s41598-021-03024-1 Text en © The Author(s) 2021 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 Kang, Wu Seong Chung, Heewon Ko, Hoon Kim, Nan Yeol Kim, Do Wan Cho, Jayun Shim, Hongjin Kim, Jin Goo Jang, Ji Young Kim, Kyung Won Lee, Jinseok Artificial intelligence to predict in-hospital mortality using novel anatomical injury score |
title | Artificial intelligence to predict in-hospital mortality using novel anatomical injury score |
title_full | Artificial intelligence to predict in-hospital mortality using novel anatomical injury score |
title_fullStr | Artificial intelligence to predict in-hospital mortality using novel anatomical injury score |
title_full_unstemmed | Artificial intelligence to predict in-hospital mortality using novel anatomical injury score |
title_short | Artificial intelligence to predict in-hospital mortality using novel anatomical injury score |
title_sort | artificial intelligence to predict in-hospital mortality using novel anatomical injury score |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8651670/ https://www.ncbi.nlm.nih.gov/pubmed/34876644 http://dx.doi.org/10.1038/s41598-021-03024-1 |
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