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

2020 ◽  
Author(s):  
Jin-Hyeok Park ◽  
Jeong-Heum Baek ◽  
Sun Jin Sym ◽  
KangYoon Lee ◽  
Youngho Lee
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Decision Support ◽  
Cancer Treatment ◽  
Clinical Decision Support ◽  
Treatment Guidelines ◽  
Treatment Protocol ◽  
Clinical Decision ◽  
Actual Data ◽  
Nccn Guidelines

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 CR3 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.

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

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Jin-Hyeok Park ◽  
Jeong-Heum Baek ◽  
Sun Jin Sym ◽  
Kang Yoon Lee ◽  
Youngho Lee
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Decision Support ◽  
Cancer Treatment ◽  
Clinical Decision Support ◽  
Treatment Guidelines ◽  
Treatment Protocol ◽  
Clinical Decision ◽  
Actual Data ◽  
Nccn Guidelines

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.

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

2020 ◽  
Author(s):  
Jinhyeok Park ◽  
KangYoon Lee ◽  
Jeong-Heum Baek ◽  
Youngho Lee
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Decision Support ◽  
Cancer Treatment ◽  
Clinical Decision Support ◽  
Treatment Guidelines ◽  
Treatment Protocol ◽  
Clinical Decision ◽  
Actual Data ◽  
Nccn Guidelines

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 CR3 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.

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

2020 ◽  
Author(s):  
Jinhyeok Park ◽  
Kang-Yoon Lee ◽  
Jeong-Heum Baek ◽  
Youngho Lee
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Decision Support ◽  
Decision Support System ◽  
Cancer Treatment ◽  
Support System ◽  
Treatment Guideline ◽  
Concordance Rate ◽  
Actual Data ◽  
Nccn Guidelines

Abstract Background: Recently, the Clinical Decision Support System (CDSS) has attracted attention as a method for minimizing medical errors. To overcome the limitation that existing CDSS does not reflect actual data, we proposed CDSS based on deep learning. Methods: We proposed Colorectal Cancer Chemotherapy Recommender (C3R), a deep learning-based chemotherapy recommendation model. This supplements the limitation that the existing CDSS is difficult to support data-based decision making. It is configured to study the clinical data generated at Gachon Gil Medical Center and recommend appropriate chemotherapy. To validate the model, we compared the diagnosis concordance rate with the NCCN Guidelines, a representative cancer treatment guideline, and the results of the Gachon Gil Medical Center’s Colorectal Cancer Treatment Protocol (GCCTP). Results: The diagnosis concordance rates of the C3R model with the NCCN guidelines were 70.5% for the Top-1 Accuracy and 84% for the Top-2 Accuracy. Also, the diagnosis concordance rate with the GCCTP were 57.9% for the Top-1 Accuracy and 77.8 for the Top-2 Accuracy. Conclusions: This model is meaningful in that it is Korea’s first colon cancer treatment method decision support system that reflects actual data. In the future, if sufficient data is secured through multi-organization, more reliable results can be obtained.

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

2020 ◽  
Author(s):  
Jinhyeok Park ◽  
Kang-Yoon Lee ◽  
Jeong-Heum Baek ◽  
Youngho Lee
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Decision Support ◽  
Decision Support System ◽  
Cancer Treatment ◽  
Support System ◽  
Treatment Guideline ◽  
Concordance Rate ◽  
Actual Data ◽  
Nccn Guidelines

Abstract Background: Recently, the Clinical Decision Support System (CDSS) has attracted attention as a method for minimizing medical errors. To overcome the limitation that existing CDSS does not reflect actual data, we proposed CDSS based on deep learning.Methods: We proposed Colorectal Cancer Chemotherapy Recommender (C3R), a deep learning-based chemotherapy recommendation model. This supplements the limitation that the existing CDSS is difficult to support data-based decision making. It is configured to study the clinical data generated at Gachon Gil Medical Center and recommend appropriate chemotherapy. To validate the model, we compared the treatment concordance rate with the NCCN Guidelines, a representative cancer treatment guideline, and the results of the Gachon Gil Medical Center’s Colorectal Cancer Treatment Protocol (GCCTP). Results: The treatment concordance rates of the C3R model with the NCCN guidelines were 70.5% for the Top-1 Accuracy and 84% for the Top-2 Accuracy. Also, the treatment concordance rate with the GCCTP were 57.9% for the Top-1 Accuracy and 77.8 for the Top-2 Accuracy. Conclusions: This model is meaningful in that it is Korea’s first colon cancer treatment method decision support system that reflects actual data. In the future, if sufficient data is secured through multi-organization, more reliable results can be obtained.

Organizational Factors Influencing the Use of Clinical Decision Support for Improving Cancer Screening Within Community Health Centers

2016 ◽  
pp. 118-148 ◽  
Author(s):  
Timothy Jay Carney ◽  
Michael Weaver ◽  
Anna M. McDaniel ◽  
Josette Jones ◽  
David A. Haggstrom
Keyword(s):  
Colorectal Cancer ◽  
Health Care ◽  
Decision Support ◽  
Cancer Screening ◽  
Community Health ◽  
Clinical Decision Support ◽  
Community Health Centers ◽  
Clinical Decision ◽  
Health Centers ◽  
Practice Setting

Adoption of clinical decision support (CDS) systems leads to improved clinical performance through improved clinician decision making, adherence to evidence-based guidelines, medical error reduction, and more efficient information transfer and to reduction in health care disparities in under-resourced settings. However, little information on CDS use in the community health care (CHC) setting exists. This study examines if organizational, provider, or patient level factors can successfully predict the level of CDS use in the CHC setting with regard to breast, cervical, and colorectal cancer screening. This study relied upon 37 summary measures obtained from the 2005 Cancer Health Disparities Collaborative (HDCC) national survey of 44 randomly selected community health centers. A multi-level framework was designed that employed an all-subsets linear regression to discover relationships between organizational/practice setting, provider, and patient characteristics and the outcome variable, a composite measure of community health center CDS intensity-of-use. Several organizational and provider level factors from our conceptual model were identified to be positively associated with CDS level of use in community health centers. The level of CDS use (e.g., computerized reminders, provider prompts at point-of-care) in support of breast, cervical, and colorectal cancer screening rate improvement in vulnerable populations is determined by both organizational/practice setting and provider factors. Such insights can better facilitate the increased uptake of CDS in CHCs that allows for improved patient tracking, disease management, and early detection in cancer prevention and control within vulnerable populations.

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