• 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.

• 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.

• 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.

• 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.

• 한국임상약학회지
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• 제23권1호
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• pp.26-32
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• 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
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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.

• 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.

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

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2002년도 추계정기학술대회
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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.

• 대한간호학회지
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• 제40권6호
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• pp.892-902
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• 2010

Purpose: This study was done to identify participation by home healthcare nurses in clinical decision making and factors influencing clinical decision making. Methods: A descriptive survey was used to collect data from 68 home healthcare nurses in 22 hospital-based home healthcare services in Korea. To investigate participation, the researcher developed 3 scenarios through interviews with 5 home healthcare nurses. A self-report questionnaire composed of tools for characteristics, factors of clinical decision making, and participation was used. Results: Participation was relatively high, but significantly lower in the design phase (F=3.51, p=.032). Competency in clinical decision making (r=.45, p<.001), perception of the decision maker role (r=.47, p<.001), and perception of the utility of clinical practice guidelines (r=.25, p=.043) were significantly correlated with participation. Competency in clinical decision making (Odds ratio [OR]=41.79, p=.007) and perception of the decision maker role (OR=15.09, p=.007) were significant factors predicting participation in clinical decision making by home healthcare nurses. Conclusion: In order to encourage participation in clinical decision making, education programs should be provided to home healthcare nurses. Official clinical practice guidelines should be used to support home healthcare nurses’ participation in clinical decision making in cases where they can identify and solve the patient health problems.