• Asian Pacific Journal of Cancer Prevention
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• v.16 no.15
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• pp.6605-6608
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• 2015

Background: Colorectal cancer is a major cause of morbidity and mortality throughout the world. Colorectal cancer screening is an optimal way for reducing of morbidity and mortality and a clinical decision support system (CDSS) plays an important role in predicting success of screening processes. DSS is a computer-based information system that improves the delivery of preventive care services. The aim of this article was to detail engineering of information requirements and work flow design of CDSS for a colorectal cancer screening program. Materials and Methods: In the first stage a screening minimum data set was determined. Developed and developing countries were analyzed for identifying this data set. Then information deficiencies and gaps were determined by check list. The second stage was a qualitative survey with a semi-structured interview as the study tool. A total of 15 users and stakeholders' perspectives about workflow of CDSS were studied. Finally workflow of DSS of control program was designed by standard clinical practice guidelines and perspectives. Results: Screening minimum data set of national colorectal cancer screening program was defined in five sections, including colonoscopy data set, surgery, pathology, genetics and pedigree data set. Deficiencies and information gaps were analyzed. Then we designed a work process standard of screening. Finally workflow of DSS and entry stage were determined. Conclusions: A CDSS facilitates complex decision making for screening and has key roles in designing optimal interactions between colonoscopy, pathology and laboratory departments. Also workflow analysis is useful to identify data reconciliation strategies to address documentation gaps. Following recommendations of CDSS should improve quality of colorectal cancer screening.

• 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.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.323-326
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• 2011

스마트기기가 많은 부분에서 쓰임에 따라 의료 부분에서 스마트기기용 앱이 사용 할 수 있게 되었다. 의료용 앱이라고 하여 이 앱을 사용함으로서 의료인은 진료에 도움이 될 수 있고, 일반인들은 자신의 건강을 관리 할 수 있게 되었다. 의료용 앱의 정의에 따라 스마트기기 부분 대하여 사용 될 수 있는 시스템이 있는데, 그것은 바로 CDSS이다. 임상 의사 결정 지원 시스템이라고 하며, 의료인들이 의료 결정을 할 때 도움을 주는 시스템이다. 이것은 HL7(Health Level 7) 표준이며, 국제 표준인 Arden Syntax로 표현 할 수 있다. 그리고 크게 3가지의 카테고리로 이루어져 있으며 각각 부분에서 의료 진료에 관한 내용을 표현한다. 또한 시스템간의 XML로 교환하여 사용 할 수 있다. 하지만 스마트기기는 그 자체의 기능과 성능으로 CDSS 서비스를 하기에는 많이 부족하다. 이 부족한 점은 모바일 클라우드를 사용한다면 의료용 앱의 완성도와 신뢰성이 더 높아 질 것이다. 의료인들이 이러한 의료용 앱을 사용함으로써 환자 진료에 많은 도움이 될 수 있을 것이다.

• 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.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.5
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• pp.16-26
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• 2001

In this paper, we present a new scheduling method called CDSS(Carried-Dependence Self-Scheduling) for efficiently execution of the loop with intra dependency between iterations based on the central queue. We also implemented it on shared memory system using Java language. Also, we study the modification that converts the existing self-scheduling method based on the central task queue for parallel loops onto the same form applied to loop with loop-carried dependences. The proposed method is self scheduling and assigns the loops in three-level considering the synchronization point according to the dependence distance of the loops. To adapt the proposed scheme and modified methods into various platforms, including a uni-processor system, we use threads for implementation. Compared to other assignment algorithms with various changes of application and system parameters, CDSS is found to be more efficient than other methods in overall execution time including scheduling overheads. CDSS shows improved performance over modified SS, Factoring, GSS and CSS by about 0.02, 40.5, 46.1 and 53.6%, respectively. In CDSS, we achieve the best performance on varying application programs using a few threads, which equal the dependence distance.

• 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.

• Journal of Internet Computing and Services
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• v.11 no.5
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• pp.59-70
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• 2010

The Ubiquitous Healthcare business are growing recently by medical service development. According to this environment, many healthcare service model have been studying and suggested. At the same time, medical world market has been reorganized into a traditional medical science out of the west medical science. But in spite of this trend, domestic U-Healthcare market in traditional medical science is for lack of profit service model. So it is true that the presentation is demanded from oriental medicine U-Healthcare service model these days in oriental field. Thus, in this paper we propose the healthcare service model that can be applied to the oriental field efficiently. Our method is based on fuzzy rule method that analyze the patient data by CDSS processing. In experiment, proposed method is more profitable and efficient than west service model. For future works, we will research about the standardization and security of processed data.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.9-17
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• 2021

In this study, we propose a part of the CDSS(Clinical Decision Support System) study, a system that can classify chemotherapy, one of the treatment methods for colorectal cancer patients. In the treatment of colorectal cancer, the selection of chemotherapy according to the patient's condition is very important because it is directly related to the patient's survival period. Therefore, in this study, chemotherapy was classified using a machine learning algorithm by creating a baseline model, a pathological model, and a combined model using both characteristics of the patient using the individual and pathological characteristics of colorectal cancer patients. As a result of comparing the prediction accuracy with Top-n Accuracy, ROC curve, and AUC, it was found that the combined model showed the best prediction accuracy, and that the LGBM algorithm had the best performance. In this study, a chemotherapy classification model suitable for the patient's condition was constructed by classifying the model by patient characteristics using a machine learning algorithm. Based on the results of this study in future studies, it will be helpful for CDSS research by creating a better performing chemotherapy classification model.

• Journal of Yeungnam Medical Science
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• v.22 no.2
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• pp.150-165
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• 2005

Purpose: This study was conducted to investigate the prognostic factors of postpsychotic depressive symptoms in patients with schizophrenia. Materials and Methods: Eighty patients were selected based on the diagnostic criteria from the DSM-IV, PANSS and ESRS. For all patients information was collected on demographic and clinical characteristics. The subjective depressive symptoms and the objective depressive symptoms, as well as patients insight regarding psychosis were evaluated. The subjective depressive symptoms were evaluated by BDI and ZDS; the objective depressive symptoms were evaluated by HDRS and CDSS, and patient insight into the psychosis was evaluated by KISP. Results: The comparisons using demographic and clinical characteristics showed that HDRS and CDSS had significant difference with regard to gender and suicide attempts; the BDI was associated with difference in education level and age of onset. The patients with scores above cuff-off score for each scale were 20(25.0%) for the BDI, 16(20.0%) for the ZDS, 18(22.5%) for the CDSS and 6(7.5%) for the HDRS. The results of the stepwise multiple regression analysis showed that the scores for the KISP, education levels, gender and suicide attempts were the main prognostic factors in patients with the psychotic depressive disorder of schizophrenia. Conclusion: The main prognostic factors in psychotic depressive disorder of schizophrenia included: insight into psychosis, suicidal attempts. Insight into the psychosis was the most reliable prognostic factor but this characteristic had a negative relationship to the with depressive symptoms.