Integration and Evaluation of Clinical Decision Support Systems for Diagnosis Idopathics Pulmonary Fibrosis (IPF)

Lee, Yunhee; Chae, Young-Moon; Jeon, Sungwan

doi:10.4258/hir.2010.16.4.260

Cited by 8 publications

(8 citation statements)

References 18 publications

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“…Clinical rules were de ned for a total of 18 cytotoxic drugs, and only 112 of the 2681 prescriptions generated warnings. Similar studies also present a differential diagnosis of pulmonary brosis CDSS [17].…”

Section: Introductionsupporting

confidence: 54%

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

Park

Lee

Baek

et al. 2020

Preprint

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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.

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Section: Introductionsupporting

confidence: 54%

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

Park

Lee

Baek

et al. 2020

Preprint

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show abstract

“…One of the appropriate methods to improve care is using “collaborative” methods utilizing use case modeling [ 3 ]. These care models use team “longitudinal” method and organize care again so that cooperation between an active, aware patient and a ready, equipped care team is improved [ 10 ].…”

Section: Resultsmentioning

confidence: 99%

“…Use case modeling simply shows in an information system which does what job and with what aim [ 2 ]. This technique has been developed as a part of UML in the form of a standard objective-oriented modeling language [ 3 ] which is based on the system's simple structure and direct understanding, usually presenting considerable details about the interaction between a system and its users in a structured story-like manner [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

The Application of Use Case Modeling in Designing Medical Imaging Information Systems

et al. 2013

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Introduction. The essay at hand is aimed at examining the application of use case modeling in analyzing and designing information systems to support Medical Imaging services. Methods. The application of use case modeling in analyzing and designing health information systems was examined using electronic databases (Pubmed, Google scholar) resources and the characteristics of the modeling system and its effect on the development and design of the health information systems were analyzed. Results. Analyzing the subject indicated that Provident modeling of health information systems should provide for quick access to many health data resources in a way that patients' data can be used in order to expand distant services and comprehensive Medical Imaging advices. Also these experiences show that progress in the infrastructure development stages through gradual and repeated evolution process of user requirements is stronger and this can lead to a decline in the cycle of requirements engineering process in the design of Medical Imaging information systems. Conclusion. Use case modeling approach can be effective in directing the problems of health and Medical Imaging information systems towards understanding, focusing on the start and analysis, better planning, repetition, and control.

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“…Clinical rules were de ned for a total of 18 cytotoxic drugs, and only 112 of the 2,681 prescriptions generated warnings. A similar study presented a CDSS for the differential diagnosis of pulmonary brosis [17].…”

Section: Introductionmentioning

confidence: 91%

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

Park

Lee

Baek

et al. 2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

Integration and Evaluation of Clinical Decision Support Systems for Diagnosis Idopathics Pulmonary Fibrosis (IPF)

Cited by 8 publications

References 18 publications

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

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

The Application of Use Case Modeling in Designing Medical Imaging Information Systems

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

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