My Cancer Genome: Web-based clinical decision support for genome-directed lung cancer treatment.

2011 ◽  
Vol 29 (15_suppl) ◽  
pp. 7576-7576 ◽  
Author(s):  
M. A. Levy ◽  
C. M. Lovly ◽  
L. Horn ◽  
R. Naser ◽  
W. Pao
Keyword(s):  
Lung Cancer ◽  
Decision Support ◽  
Cancer Treatment ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Cancer Genome ◽  
Web Based ◽  
Lung Cancer Treatment

scholarly journals Challenges involved in establishing a web-based clinical decision support tool in community health centers

Healthcare ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 100488
Author(s):  
Rachel Gold ◽  
Mary Middendorf ◽  
John Heintzman ◽  
Joan Nelson ◽  
Patrick O'Connor ◽  
...  
Keyword(s):  
Decision Support ◽  
Community Health ◽  
Clinical Decision Support ◽  
Community Health Centers ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Health Centers ◽  
Web Based ◽  
Support Tool ◽  
Clinical Decision Support Tool

Clinical decision support for symptom management in lung cancer patients: A group RCT

2021 ◽  
Author(s):  
Mary E. Cooley ◽  
Emanuele Mazzola ◽  
Niya Xiong ◽  
Fangxin Hong ◽  
David F. Lobach ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Decision Support ◽  
Cancer Patients ◽  
Clinical Decision Support ◽  
Symptom Management ◽  
Clinical Decision ◽  
Lung Cancer Patients

scholarly journals Web-based Stroke Calculators in Clinical Decision Support: Retrospective Analysis of Usage Patterns (Preprint)

10.2196/28266 ◽  
2021 ◽  
Author(s):  
Benjamin R Kummer ◽  
Lubaina Shakir ◽  
Rachel Kwon ◽  
Joseph Habboushe ◽  
Nathalie Jetté
Keyword(s):  
Decision Support ◽  
Retrospective Analysis ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Web Based ◽  
Usage Patterns

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.

Guidance DS: A web-based clinical decision support system for antimicrobial stewardship in hospitals

2009 ◽  
Vol 42 (12) ◽  
pp. 354-358
Author(s):  
Karin Thursky ◽  
Marion Robertson ◽  
Susan Luu ◽  
James Black ◽  
Michael Richards ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Antimicrobial Stewardship ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Web Based
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