• Electronics and Telecommunications Trends
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• v.31 no.4
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• pp.77-85
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• 2016

구글 딥마인드 알파고와 이세돌 선수와의 바둑대결 후 인공지능의 활용처로 의료분야가 거론되면서 임상의사결정지원시스템(Clinical Decision Support System: CDSS)이 최근 주목받고 있다. 기본적으로 CDSS는 환자 진료에 있어 예방, 진단, 치료, 처방 그리고 예후의 각 단계에서 임상의의 의사결정을 도와주는 시스템을 말한다. 본고에서는 CDSS의 국내외 도입 및 시장현황과 관련 기술현황을 검토하여 의료현장에서 CDSS의 활용이 활성화되기 위한 방안을 도출하고자 한다.

• Journal of the Korea Society of Computer and Information
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• v.20 no.11
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• pp.119-126
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• 2015

CDSS provides clinical doctors with knowledge to be required when they diagnose or make decision about treatment strategy. Arden Syntax is one of the language with which we write MLM that is a component of CDSS. It was designated as a standard by HL7/ANSI. ArdenML is an XML version of Arden Syntax. In this paper we propose a tool which translates Arden Syntax MLMs into ArdenML MLM. To this end we first defines the corresponding relation between two languages. Next we presents a modified version of Arden Syntax grammar to improve performance of lexical analysis and minimize parsing conflicts. Finally we presents syntax and semantics gaps between the both languages, which are a structural representation problem, a data type problem, and a disrelation problem. Our translator resolves such issues and generates exact ArdenML codes for an arbitrary Arden Syntax MLM.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.11a
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• pp.203-212
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• 2002

This study examined the predictive power of data mining algorithms by comparing the performance of logistic regression and decision tree algorithm, called CHAID (Chi-squared Automatic Interaction Detection), On the contrary to the previous studies, decision tree performed better than logistic regression. We have also developed a CDSS (Clinical Decision Support System) with three modules (doctor, nurse, and patient) based on data warehouse architecture. Data warehouse collects and integrates relevant information from various databases from hospital information system (HIS ). This system can help improve decision making capability of doctors and improve accessibility of educational material for patients.

• Journal of Korean Society for Quality Management
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• v.42 no.1
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• pp.81-89
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• 2014

Purpose: The purpose of this study is to propose Clinical Practice Guideline(CPG) model and Clinical Index(CI) for implementing CDSS in digital hospitals. Methods: This study uses EMR data at department of family practice in A hospital; 636 patients, 570 diseases (based on ICD 10-CM criteria), and 37,000 data related with labs and treatments. This study focuses on disease J342 which is the most high rate of incidence. Results: Using the suggested model, this study calculates frequency matrix and probability matrix to find out the correlation of diseases and labs. This study indicates the lab sets of Disease (J342) as CI for CPG. Conclusion: This study suggests CPG model including Lab-based, Disease-Based and Case-based modules. Through 6 level cased-based CPG model, especially, this study develops Clinical Index(CI) such as the Incidence Rate, Lab Rate, Disease Lab Rate, Disease confirmed by Lab.

• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.117-124
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• 2019

Recently, medical IT solutions are being provided on a distributed environment basis. In Korea, the necessity of developing a clinical decision support system that can share medical information in a distributed environment has been recognized and studied. The existing clinical decision support system is being built using only medical information of its own within the hospital. This makes it difficult for existing systems to achieve good results in terms of efficiency and accuracy of decision support. In order to solve these limitations, this paper proposes a design and implementation method of clinical decision support system based on common data model in medical field. To explain the application process of the proposed model, we describe the development scenario of the clinical decision support system for the diagnosis of colorectal cancer. We also propose the essential requirements for the development of successful clinical decision support systems. Through this, it is expected that it will be possible to develop clinical decision support system that can be used in various hospitals and improve the efficiency and accuracy of the system.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.255-256
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• 2022

임상의사결정지원시스템(Clinical Decision Support System)은 환자의 질병을 진단하고 치료할 때 의사결정을 도와주는 시스템이다.[1] 본 논문에서는 블록체인과 XAI 기술을 활용해 임상의사결정지원시스템의 아키텍처를 제안한다. 제안 아키텍처는 데이터의 중앙화, 의료데이터의 보안을 블록체인기술로 해결하고 블록체인을 기반으로 한 보반 기술인 DID 기술을 활용해 데이터의 신뢰성과 보안성을 확보하였다. 또한 XAI 모듈을 활용해 예측 결과의 신뢰도와 투명성도 제공해 의료인의 의사결정을 지원하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.450-451
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• 2012

The need of Clinical Decision Support System (CDSS) in healthcare setup is increasing day by day. EHR Meaningful Use advocates CDSS as an important component of EHR/EMR systems. CDSS can be ranged from a simple to a very sophisticated system. The more complex CDSS systems need more attention to develop because of many reasons including its Knowledge Base (KB) structure/maintenance/evolution, inference capabilities and usability. Above all the KB maintenance and evolution is very crucial and important from the perspective of useful decision capabilities. Also the richness of the KB is important to cover the decision gaps handling a particular situation in the course of patient care. It cannot be expected from the clinicians to remember everything in regard to patient diagnosis and treatment. Similarly, it is also crucial for clinicians to keep themselves updated with the new research in the area. That is the reason they frequently require accessing to the online knowledge resources. Literature proved that online knowledge resources are capable providing answers to questions that might not be answered rely only on clinician wisdom and experience. This paper provides the theme of meaningful utilization of online knowledge resources in the context of diagnosis and treatment process for cancer patients more specifically Head and Neck cancer.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.13-22
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• 2021

The clinical decision support system uses accumulated medical data to apply an AI model learned by machine learning to patient diagnosis and treatment prediction. However, the existing black box-based AI application does not provide a valid reason for the result predicted by the system, so there is a limitation in that it lacks explanation. To compensate for these problems, this paper proposes a system model that applies XAI that can be explained in the development stage of the clinical decision support system. The proposed model can supplement the limitations of the black box by additionally applying a specific XAI technology that can be explained to the existing AI model. To show the application of the proposed model, we present an example of XAI application using LIME and SHAP. Through testing, it is possible to explain how data affects the prediction results of the model from various perspectives. The proposed model has the advantage of increasing the user's trust by presenting a specific reason to the user. In addition, it is expected that the active use of XAI will overcome the limitations of the existing clinical decision support system and enable better diagnosis and decision support.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.1
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• pp.221-229
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• 2012

A model for expressing meta data syntax in the eXtensible Markup Language(XML) was developed to increase the portability of the Arden Syntax in medical treatment. In this model that is Arden syntax uses two syntax checking mechanisms, first an XML validation process, and second, a syntax check using an XSL style sheet. Two hundred seventy-seven examples of MLMs were transformed into MLMs in ArdenML and validated against the schema and style sheet. Both the original MLMs and reverse-parsed MLMs in ArdenML were checked using a Arden Syntax checker. The textual versions of MLMs were successfully transformed into XML documents using the model, and the reverse-parse yielded the original text version of MLMs.

• Korean Journal of Artificial Intelligence
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• v.7 no.2
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• pp.19-24
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• 2019

Lung cancer is a chronic disease which ranks fourth in cancer incidence with 11 percent of the total cancer incidence in Korea. To deal with such issues, there is an active study on the usefulness and utilization of the Clinical Decision Support System (CDSS) which utilizes machine learning. Thus, this study reviews existing studies on artificial intelligence technology that can be used in determining the lung cancer, and conducted a study on the applicability of machine learning in determination of the lung cancer by comparison and analysis using Azure ML provided by Microsoft. The results of this study show different predictions yielded by three algorithms: Support Vector Machine (SVM), Two-Class Support Decision Jungle and Multiclass Decision Jungle. This study has its limitations in the size of the Big data used in Machine Learning. Although the data provided by Kaggle is the most suitable one for this study, it is assumed that there is a limit in learning the data fully due to the lack of absolute figures. Therefore, it is claimed that if the agency's cooperation in the subsequent research is used to compare and analyze various kinds of algorithms other than those used in this study, a more accurate screening machine for lung cancer could be created.