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
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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