• 산업공학
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• 제20권4호
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• pp.488-497
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• 2007

In order to provide appropriate decision supports in medical domain, it is required that clinical knowledge should be implemented in a computable form and integrated with hospital information systems. Healthcare organizations are increasingly adopting tools that provide decision support functions to improve patient outcomes and reduce medical errors. This paper proposes a process centric clinical decision support system based on medical knowledge. The proposed system consists of three major parts - CPG (Clinical Practice Guideline) repository, service pool, and decision support module. The decision support module interprets knowledge base generated by the CPG and service part and then generates a personalized and patient centered clinical process satisfying specific requirements of an individual patient during the entire treatment in hospitals. The proposed system helps health professionals to select appropriate clinical procedures according to the circumstances of each patient resulting in improving the quality of care and reducing medical errors.

• 한국융합학회논문지
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• 제10권11호
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• pp.117-124
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• 2019

최근 의료IT 분야 솔루션들이 분산 환경 기반으로 제공되고 있는 추세이다. 국내에서도 분산 환경에서 의료정보를 공유할 수 있는 임상의사결정지원시스템 개발의 필요성이 인식되어 연구되고 있다. 기존 임상의사결정지원시스템은 병원 내의 자체적인 의료정보만을 사용하여 구축되고 있다. 이로 인해 기존의 시스템은 의사결정지원의 효율성 및 정확성 측면에서 좋은 결과를 얻기 어렵다. 이러한 한계점을 해결하기 위해 이 논문에서는 의료분야의 공통 데이터 모델을 기반으로 하는 임상의사결정지원시스템 모델을 설계하고 구축방안을 제시한다. 제안 모델의 적용 과정을 설명하기 위해 대장암 진단을 위한 임상의사결정지원시스템의 개발 시나리오를 기술한다. 또한 성공적인 임상의사결정지원시스템 개발을 위한 필수 요구사항을 제시한다. 이를 통해 여러 병원에서 공통으로 사용이 가능하고 시스템의 효율성과 정확성을 높일 수 있는 임상의사결정지원시스템 개발이 가능할 것으로 기대한다.

• 한국산업융합학회 논문집
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• 제25권6_2호
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• pp.1083-1090
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• 2022

Since renal cell carcinoma(RCC) has various examination and treatment methods according to clinical stage and histopathological characteristics, it is required to determine accurate and efficient treatment methods in the clinical field. However, the process of collecting and processing RCC medical data is difficult and complex, so there is currently no AI-based clinical decision support system for RCC treatments worldwide. In this study, we propose a clinical decision support system that helps clinicians decide on a precision treatment to each patient. RCC standard big database is built by collecting structured and unstructured data from the standard common data model and electronic medical information system. Based on this, various machine learning classification algorithms are applied to support a better clinical decision making.

• Journal of the Korean Data and Information Science Society
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• 제14권4호
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• pp.817-824
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• 2003

We employed a hierarchical document classification method to classify a massive collection of electronic medical records(EMR) written in both Korean and English. Our experimental system has been learned from 5,000 records of EMR text data and predicted a newly given set of EMR text data over 68% correctly. We expect the accuracy rate can be improved greatly provided a dictionary of medical terms or a suitable medical thesaurus. The classification system might play a key role in some clinical decision support systems and various interpretation systems for clinical data.

• 대한간호학회지
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• 제37권6호
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• pp.863-871
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• 2007

Purpose: The purpose of this study was to investigate the factors influencing nurses' clinical decision making focusing on critical thinking disposition. Methods: The subjects of this study consisted of 505 nurses working at one of the general hospitals located in Seoul. Data was collected by a self-administered questionnaire between December 2006 and January 2007. Data was analyzed by one way ANOVA, Pearson correlation coefficients, and stepwise multiple regression using SPSS Win 14.0. Results: The mean scores of critical thinking disposition and clinical decision making were 99.10 and 134.32 respectively. Clinical decision making scores were significantly higher in groups under continuing education, with a master or higher degree, with clinical experience more than 5 years, or with experts. Critical thinking disposition and its subscales have a significant correlation with clinical decision making. Intellectual eagerness/curiosity, prudence, clinical experience, intellectual honesty, self-confidence, and healthy skepticism were important factors influencing clinical decision making(adjusted $R^2=33%$). Conclusion: Results of this study suggest that various strategies such as retaining experienced nurses, encouraging them to continue with education and enhancing critical thinking disposition are warranted for development of clinical decision making.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제11권1호
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• pp.35-40
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• 2022

본 논문에서는 임상 의사 결정 지원을 위하여 의학 지식을 통해 임상 관계를 추출하고 딥러닝 모델을 이용하여 질병을 예측하는 방법을 제안한다. 의학 사전인 UMLS(Unified Medical Language System)와 암 관련 의학 지식에 포함된 임상 용어를 5가지로 분류한다. 분류된 임상 용어들을 사용하여 위키피디아 의학 문서를 추출한다. 추출한 위키피디아 의학 문서와 추출한 임상 용어를 매칭하여 임상 관계를 구축한다. 구축한 임상 관계를 이용하여 딥러닝 학습을 진행한 후 질의에서 표현된 의학 용어를 바탕으로 질의와 연관된 질병을 예측한다. 이후, 예측한 질병과 관계가 있는 의학 용어를 확장 질의로 선택한 뒤 질의를 확장한다. 제안 방법의 유효성을 검증하기 위해 TREC Clinical Decision Support(CDS), TREC Precision Medicine(PM) 테스트 컬렉션에 대해 비교 평가한다.

• 간호행정학회지
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• 제7권2호
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• pp.301-314
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• 2001

The main purpose of this research is to describe comprehensively the processes of clinical decision making in novice critical care nurses through clinical experience. This research was an exploratory, longitudinal study using a fieldwork approach incorporating "think-aloud" method and in-depth interviews with the study participants. The study participants consisted of 5 novice nurses assigned to critical care units at a tertiary medical center located in Seoul, among a group of 27 novice nurses who started at the same period at this hospital. The data were collected from March 1999 to April 2000. The major findings of the study is that the novice nurses followed the analytic linear model of clinical decision making in the beginning, but were changed increasingly to follow the comprehensive, integrated model of clinical decision making. Through repeated experience that resulted in increasing repertoire of clinical schema and familiarity of task environments of clinical practice the novice nurses expanded their ability to arrive at comprehensive integration of information and to arrive at accurate and time-efficient decisions. Both the analytic, linear model mostly used at the beginning period and the comprehensive, integrated model that seems to be the mode significantly dependent upon experience seem to have strengths and weaknesses as decision making processes in clinical situations. Hence, it is imperative to develop an effective orientation and training program for novice nurses through the use of clinical preceptors. In addition, students should be exposed to the process of clinical decision making early in their nursing education through an appropriate clinical experiences and clinical assignments.

• Asian Pacific Journal of Cancer Prevention
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• 제16권15호
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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.

• 전기학회논문지
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• 제57권6호
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• pp.1058-1062
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• 2008

In clinical decision support system(CDSS), unlike rule-based expert method, appropriate data-driven machine learning method can easily provide the information of individual feature(clinical test) for disease classification. However, currently developed methods focus on the improvement of the classification accuracy for diagnosis. With the analysis of feature importance in classification, one may infer the novel clinical test sets which highly differentiate the specific diseases or disease states. In this background, we introduce a novel CDSS that integrate a classifier and feature selection module together. Random forest algorithm is applied for the classifier and the feature importance measure. The system selects the significant clinical tests discriminating the diseases by examining the classification error during backward elimination of the features. The superior performance of random forest algorithm in clinical classification was assessed against artificial neural network and decision tree algorithm by using breast cancer, diabetes and heart disease data in UCI Machine Learning Repository. The test with the same data sets shows that the proposed system can successfully select the significant clinical test set for each disease.

• 가정∙방문간호학회지
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• 제21권2호
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• pp.147-155
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• 2014

Purpose: Clinical decision-making carried out by nurses is a complex process that influences the quality of care provided and various patient outcomes. This study examined the relationship between critical thinking disposition and clinical decision-making abilities in home health advanced practice nurses. Method: The study had a non-experimental correlational design. Data were collected from 100 home health advanced practice nurses in 20 hospitals. Results: The mean critical thinking disposition score was $3.69{\pm}.39$ out of 5 and the mean score for clinical decision-making abilities was $3.48{\pm}.22$ out of 5. In this correlation analysis, a significant positive correlation (r=.58, p<.001) was found between critical thinking disposition and clinical decision-making abilities of home health advanced practice nurses. Conclusion: In order to improve the clinical decision-making ability of home health advanced practice nurses, we need to improve their critical thinking disposition. In order to make this change, appropriate training program are needed to increase the critical thinking disposition and clinical decision-making abilities of home health advanced practice nurses.