A data-driven approach to a chemotherapy recommendation model based on deep learning for patients with colorectal cancer in Korea
Autor: | Jeong-Heum Baek, Youngho Lee, Kang Yoon Lee, Sun Jin Sym, Jinhyeok Park |
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Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
medicine.medical_specialty
020205 medical informatics Colorectal cancer Concordance Knowledge-based clinical decision support system (CDSS) Health Informatics 02 engineering and technology lcsh:Computer applications to medicine. Medical informatics Clinical decision support system Health informatics Data-driven Recommendation model 03 medical and health sciences 0302 clinical medicine Republic of Korea 0202 electrical engineering electronic engineering information engineering medicine Humans Medical physics Colorectal Cancer business.industry Health Policy Deep learning Cancer Decision Support Systems Clinical medicine.disease Computer Science Applications 030220 oncology & carcinogenesis Colonic Neoplasms lcsh:R858-859.7 Artificial intelligence Colorectal Neoplasms business Chemotherapy recommendation Research Article |
Zdroj: | BMC Medical Informatics and Decision Making BMC Medical Informatics and Decision Making, Vol 20, Iss 1, Pp 1-12 (2020) |
ISSN: | 1472-6947 |
Popis: | 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. |
Databáze: | OpenAIRE |
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