• 산업공학
• /
• 제20권4호
• /
• pp.488-497
• /
• 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.

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

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

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

• 한국임상약학회지
• /
• 제23권1호
• /
• pp.26-32
• /
• 2013

Objective: This study was to define the clinical effect on the clinical decision support system (CDSS) for prescribing antibiotics integrated with the order communication system in a National Hospital. Method: We extracted data collected before integrating the CDSS of 4,406 adult patients in 2007 and data collected after integrating the CDSS of 4,278 adult patients in 2009. These patients were 50.4% and 45.2% of all patients admitted in 2007 and 2009, respectively. The clinical effect was defined as the proportion of prescribed antibiotics, the length of antibiotics use, and the DDDs (defined daily doses) of antibiotics per 1,000 patient-days using these retrospective data. Results: There were a significant change in the proportion of patient prescribed penicillins with extended spectrum (OR=0.55, p=001), penicillins included beta-lactamase inhibitors (OR=0.75, p<.001), 3rd cephalosporin (OR=1.47, p<.001). The mean of the length of antibiotics use was decreased statistically from $6.09{\pm}5.48$ to $5.85{\pm}5.51$ days (p=.003). The DDD of glycopeptides was decreased from 24.43 DDD to 19.55 DDD per 1000 patient-days. The DDD of 3rd cephalosporins was also decreased from 15.88 to 11.65. Conclusion: Therefore, the clinical decision support system for prescribing antibiotics was effective for the clinical outcomes.

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

• 한국지능정보시스템학회:학술대회논문집
• /
• 한국지능정보시스템학회 2002년도 추계정기학술대회
• /
• pp.203-212
• /
• 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.

• 한국콘텐츠학회논문지
• /
• 제11권4호
• /
• pp.312-320
• /
• 2011

이 논문에서는 일개 POC(Point Of Care) 시스템을 사용하는 의료기관을 중심으로 의약품 처방조제지원 시스템(Clinical Decision Support System, CDSS)과 조직성과와의 관계를 규명하는 것에 목적을 두고 있다. 이를 위하여 정보시스템 평가요소에 대해 정의를 내리고, CDSS의 성과 평가 모형을 제시하여 설문조사 분석을 통해 의약품 처방조제지원시스템의 도입 효과를 밝히고자 하였다. 분석결과 시스템 품질을 제외하고는 각 평가 영역들 사이에 인과성이 존재하는 것으로 분석되었으며, 통계적으로 유의하게 지지되는 것으로 분석되었다. 평가모형 검증결과 의약품처방최적화를 위한 CDSS의 시스템 품질이 사용자 만족도에 영향을 미친다는 근거를 발견할 수 없었다. 그러나 정보품질이 사용자의 만족도에 긍정적인 영향을 미치며 사용자 만족은 조직성과에 긍정적인 영향을 미치는 것으로 나타났다.

• 한국콘텐츠학회논문지
• /
• 제5권6호
• /
• pp.20-30
• /
• 2005

혐기성 균의 동정과정은 업무영역 전체에 복잡성이 존재하며, 전문가의 비정형적인 경험적 지식을 주로 이용한다. 따라서 이와 같은 불완전한 지식체계를 시스템 내부에 표현하고 또한 사용자의 입장에서 진화하는 지식의 추가가 가능하여야 한다. 따라서 본 연구에서는 실질적으로 임상에서 이용이 가능하도록 혐기성 균을 모델로 임상의사결정지원시스템을 개발하여 원인 균 동정과정 시 동정경로 설정 및 해답의 도출에 조언이 가능하도록 하였다. 앞으로 혐기성 균뿐만 아니라 실제 진단검사의학과에서 분리빈도가 높은 호기성균을 포함하는 전체 세균을 대상으로 하는 확대된 영역의 임상의사결정지원시스템이 개발되면 전문가의 견해에서 정적, 동적, 지식을 제공해 줄 수 있는 기반이 되고, 이를 위해 본 연구가 기반으로 활용될 수 있을 것이다.

• International Journal of Computer Science & Network Security
• /
• 제24권9호
• /
• pp.97-104
• /
• 2024

Leukemia induced death has been listed in the top ten most dangerous mortality basis for human being. Some of the reason is due to slow decision-making process which caused suitable medical treatment cannot be applied on time. Therefore, good clinical decision support for acute leukemia type classification has become a necessity. In this paper, the author proposed a novel approach to perform acute leukemia type classification using sequential neural network classifier. Our experimental result only covers the first classification process which shows an excellent performance in differentiating normal and abnormal cells. Further development is needed to prove the effectiveness of second neural network classifier.