Dataset Information

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

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.

SUBMITTER: Park JH

PROVIDER: S-EPMC7510149 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Park Jin-Hyeok JH Baek Jeong-Heum JH Sym Sun Jin SJ Lee Kang Yoon KY Lee Youngho Y

BMC medical informatics and decision making 20200922 1

<h4>Background</h4>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.<h4>Methods</h4>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 n ...[more]

PMID: 32962726

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Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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

Similar Datasets

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