• 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 Software Engineering Society
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• v.25 no.2
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• pp.29-34
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• 2012

Quality control in medical is getting very important issue so that the importance of CDS(Clinical Decision Support) System has been increased. Local clinics as well as big hospitals are required to implement the CDS System. But the cost and complexity of CDS system implementation is so high since many different activities including knowledge authoring, software development, and integrating the legacy system are necessary. In this paper, we suggest the CDS system architecture to be sharable and interoperable and evaluate the availability and efficiency of this architecture.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.35-40
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• 2022

In this paper, we propose a method of constructing clinical knowledge with clinical concept relations and predicting diseases based on a deep learning model to support clinical decision-making. Clinical terms in UMLS(Unified Medical Language System) and cancer-related medical knowledge are classified into five categories. Medical related documents in Wikipedia are extracted using the classified clinical terms. Clinical concept relations are established by matching the extracted medical related documents with the extracted clinical terms. After deep learning using clinical knowledge, a disease is predicted based on medical terms expressed in a query. Thereafter, medical terms related to the predicted disease are selected as an extended query for clinical document retrieval. To validate our method, we have experimented on TREC Clinical Decision Support (CDS) and TREC Precision Medicine (PM) test collections.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.478-485
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• 2001

In this study, we have developed a prototype of clinical decision support systems (CDSS) for diagnosing neurogenic bladder and compared its predicted diagnoses with the actual diagnoses using 92 patient\`s Urodynamic study cases. The CDSS was developed using a Visual Basic based on the evidence-based rules extracted from guidelines and other references regarding a diagnosis of neurogenic bladder. To compare with the 92 final diagnoses made by doctors at the Yonsei Rehabilitation Center, we classified all diagnoses into 5 groups. The predictive rates of the CDSS were: 48.0% for areflexic neurogenic bladder; 60.0% for hyperreflexic neurogenic bladder in a spinal shock recovery stage; 72.9% for hyperreflexic neurogenic bladder, and 80.0% for areflexic neurogenic bladder in a spinal shock stage, which was the highest predicted rate. There were only 2 cases for hyperreflexic neurogenic bladder in a well controlled detrusor activity, and its predictive rate was 0%. The study results showed that CDSS for diagnosing neurogenic bladder could provide a helpful advice on decision-making for doctors. The findings also suggest that physicians should be involved in all development stages to ensure that systems are developed in a fashion that maximizes their beneficial effect on patient care, and that systems are acceptable to both professionals and patients. The future studies will concentrate on including more validating the system.

• Korean Journal of Oriental Medicine
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• v.12 no.3 s.18
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• pp.17-29
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• 2006

The Syndrome Differentiation(辨證) means to make diagnosises and to classify symptoms based oriental medicine theory. Questionnaires and clinical decision support system would assist the doctor checks up symptoms of a patient. We analyze about domestic and foreign patents and papers of these diagnosis tools and catch the trend. In patents, the system examines by telemedicine and offers medical information and prescriptions to patient. Papers was itemized fields ; theory, methods of analysis, clinical application, questionnaire, clinical decision support system. The results of this research can be applied to develop the high-quality tool to support syndrome differentiation.

• Korean Journal of Oriental Medicine
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• v.13 no.2 s.20
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• pp.75-81
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• 2007

The need for the study of the revealing Sasang constitution at scientific term is increasing as the application of this discipline to the patient produces more accurate result. To obtain scientific evidence of Sasang constitution, it is crucial to analyze accumulated clinical information and associate them to the biological indices that may classify Sasang constitution. Thus, the analysis of clinical information is the most important stepping stone to go toward to the stage of developing model and decision support system (DSS) for classifying Sasang constitution. This study is a preliminary analysis of 1,109 samples collected with 171 clinical indices. To find meaningful clinical indices for classifying Sasang constitutional medicine, we applied decision tree model for them. The skin of 66.5% within whole Taeeumin is thick and non feeble. In the case of 69.8% within whole Soyangin, the skin is non feeble and slippery. In the case of 64.4% within whole Soeumin. they have feeble skin. Therefore, the property of skin can be suggested to be more important than any other index for the classification of Sasang constitution.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.174-185
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• 2020

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 cover 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.284-310
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• 2024

Identifying clinical pathways for disease diagnosis and treatment process recommendations are seriously decision-intensive tasks for health care practitioners. It requires them to rely on their expertise and experience to analyze various categories of health parameters from a health record to arrive at a decision in order to provide an accurate diagnosis and treatment recommendations to the end user (patient). Technological adaptation in the area of medical diagnosis using AI is dispensable; using expert systems to assist health care practitioners in decision-making is becoming increasingly popular. Our work architects a novel knowledge-based recommender system model, an expert system that can bring adaptability and transparency in usage, provide in-depth analysis of a patient's medical record, and prescribe diagnostic results and treatment process recommendations to them. The proposed system uses a set of parallel discrete fuzzy rule-based classifier systems, with each of them providing recommended sub-outcomes of discrete medical conditions. A novel knowledge-based combiner unit extracts significant relationships between the sub-outcomes of discrete fuzzy rule-based classifier systems to provide holistic outcomes and solutions for clinical decision support. The work establishes a model to address disease diagnosis and treatment recommendations for primary lung disease issues. In this paper, we provide some samples to demonstrate the usage of the system, and the results from the system show excellent correlation with expert assessments.

• The KIPS Transactions:PartB
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• v.17B no.4
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• pp.317-326
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• 2010

To improve the diagnosis and prescription for military personnel, it is required to adopt Clinical Decision Support System (CDSS) in armed forces hospitals. The objective of this paper is to suggest a CDSS for armed forces hospitals using semantic web technologies. To this end, we designed military medical ontologies and military medical rules which consist of the various concepts and rules for supporting medical activities. We developed a semantic web-based CDSS to demonstrate the use of the ontologies and rules for treating military patients. We also showed the process of semantic search for the medical records which are created from the semantic web-based CDSS.

• 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.