A data-driven approach to a chemotherapy recommendation model based on deep learning for patients with colorectal cancer
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(2020) 20:241
RESEARCH ARTICLE
Open Access
A data-driven approach to a chemotherapy recommendation model based on deep learning for patients with colorectal cancer in Korea Jin-Hyeok Park1, Jeong-Heum Baek2†, Sun Jin Sym3, Kang Yoon Lee4 and Youngho Lee4*†
Abstract 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. Keywords: Colorectal Cancer, Knowledge-based clinical decision support system (CDSS), Deep learning, Chemotherapy recommendation
Background Becoming a medical specialist generally requires 10–15 years of training, starting from entrance into university. A medical specialist determines the condition of a patient and makes an appropriate diagnosis based on * Correspondence: [email protected] † Jeong-Heum Baek and Youngho Lee contributed equally to this work. 4 Department of Computer Engineering, Gachon University, 1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do 13120, Republic of Korea Full list of author information is available at the end of the article
medical and empirical knowledge acquired through years of experience. Nevertheless, many patients die every year from medical errors. According to a recent study performed at Johns Hopkins University, over 250,000 people in the United States died because of medical errors, which was the third leading cause of death after heart disease and cancer that year [1]. Medical errors also cost $20 billion annually in United States; minimizing medical errors is therefore crucial [2].
© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation,
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