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A data-driven approach to a chemotherapy recommendation model based on deep learning for patients with colorectal cancer in Korea

BACKGROUND: Clinical Decision Support Systems (CDSSs) have recently attracted attention as a method for minimizing medical errors. Existing CDSSs are limited in that they do not reflect actual data. To overcome this limitation, we propose a CDSS based on deep learning. METHODS: We propose the Colore...

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Autores principales: Park, Jin-Hyeok, Baek, Jeong-Heum, Sym, Sun Jin, Lee, Kang Yoon, Lee, Youngho
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7510149/
https://www.ncbi.nlm.nih.gov/pubmed/32962726
http://dx.doi.org/10.1186/s12911-020-01265-0
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author Park, Jin-Hyeok
Baek, Jeong-Heum
Sym, Sun Jin
Lee, Kang Yoon
Lee, Youngho
author_facet Park, Jin-Hyeok
Baek, Jeong-Heum
Sym, Sun Jin
Lee, Kang Yoon
Lee, Youngho
author_sort Park, Jin-Hyeok
collection PubMed
description BACKGROUND: Clinical Decision Support Systems (CDSSs) have recently attracted attention as a method for minimizing medical errors. Existing CDSSs are limited in that they do not reflect actual data. To overcome this limitation, we propose a CDSS based on deep learning. METHODS: We propose the Colorectal Cancer Chemotherapy Recommender (C3R), which is a deep learning-based chemotherapy recommendation model. Our model improves on existing CDSSs in which data-based decision making is not well supported. C3R is configured to study the clinical data collected at the Gachon Gil Medical Center and to recommend appropriate chemotherapy based on the data. To validate the model, we compared the treatment concordance rate with the National Comprehensive Cancer Network (NCCN) Guidelines, a representative set of cancer treatment guidelines, and with the results of the Gachon Gil Medical Center’s Colorectal Cancer Treatment Protocol (GCCTP). RESULTS: For the C3R model, the treatment concordance rates with the NCCN guidelines were 70.5% for Top-1 Accuracy and 84% for Top-2 Accuracy. The treatment concordance rates with the GCCTP were 57.9% for Top-1 Accuracy and 77.8% for Top-2 Accuracy. CONCLUSIONS: This model is significant, i.e., it is the first colon cancer treatment clinical decision support system in Korea that reflects actual data. In the future, if sufficient data can be secured through cooperation among multiple organizations, more reliable results can be obtained.
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spelling pubmed-75101492020-09-24 A data-driven approach to a chemotherapy recommendation model based on deep learning for patients with colorectal cancer in Korea Park, Jin-Hyeok Baek, Jeong-Heum Sym, Sun Jin Lee, Kang Yoon Lee, Youngho BMC Med Inform Decis Mak Research Article BACKGROUND: Clinical Decision Support Systems (CDSSs) have recently attracted attention as a method for minimizing medical errors. Existing CDSSs are limited in that they do not reflect actual data. To overcome this limitation, we propose a CDSS based on deep learning. METHODS: We propose the Colorectal Cancer Chemotherapy Recommender (C3R), which is a deep learning-based chemotherapy recommendation model. Our model improves on existing CDSSs in which data-based decision making is not well supported. C3R is configured to study the clinical data collected at the Gachon Gil Medical Center and to recommend appropriate chemotherapy based on the data. To validate the model, we compared the treatment concordance rate with the National Comprehensive Cancer Network (NCCN) Guidelines, a representative set of cancer treatment guidelines, and with the results of the Gachon Gil Medical Center’s Colorectal Cancer Treatment Protocol (GCCTP). RESULTS: For the C3R model, the treatment concordance rates with the NCCN guidelines were 70.5% for Top-1 Accuracy and 84% for Top-2 Accuracy. The treatment concordance rates with the GCCTP were 57.9% for Top-1 Accuracy and 77.8% for Top-2 Accuracy. CONCLUSIONS: This model is significant, i.e., it is the first colon cancer treatment clinical decision support system in Korea that reflects actual data. In the future, if sufficient data can be secured through cooperation among multiple organizations, more reliable results can be obtained. BioMed Central 2020-09-22 /pmc/articles/PMC7510149/ /pubmed/32962726 http://dx.doi.org/10.1186/s12911-020-01265-0 Text en © The Author(s) 2020 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/. The Creative Commons Public Domain Dedication waiver (http://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 Article
Park, Jin-Hyeok
Baek, Jeong-Heum
Sym, Sun Jin
Lee, Kang Yoon
Lee, Youngho
A data-driven approach to a chemotherapy recommendation model based on deep learning for patients with colorectal cancer in Korea
title A data-driven approach to a chemotherapy recommendation model based on deep learning for patients with colorectal cancer in Korea
title_full A data-driven approach to a chemotherapy recommendation model based on deep learning for patients with colorectal cancer in Korea
title_fullStr A data-driven approach to a chemotherapy recommendation model based on deep learning for patients with colorectal cancer in Korea
title_full_unstemmed A data-driven approach to a chemotherapy recommendation model based on deep learning for patients with colorectal cancer in Korea
title_short A data-driven approach to a chemotherapy recommendation model based on deep learning for patients with colorectal cancer in Korea
title_sort data-driven approach to a chemotherapy recommendation model based on deep learning for patients with colorectal cancer in korea
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7510149/
https://www.ncbi.nlm.nih.gov/pubmed/32962726
http://dx.doi.org/10.1186/s12911-020-01265-0
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