Based on the idea of the ontology, knowledge representation and structure of knowledge base of clinical cases is proposed. The knowledge acquisition process of clinical cases is introduced, the methods of clinical case similarity calculation is proposed; and the experiments of case similarity calculation has been carried on using clinical data calculation is proposed; and the experiments of case similarity calculation has been carried on using clinical data from hospital.
Artificial Intelligence ; Humans ; Knowledge Bases ; Vocabulary, Controlled

A Web-based technology system was put forward aiming at the actual problems of the long maintenance cycle and the difficulties of the maintenance and repairing of medical equipments. Based on analysis of platform system structure and function, using the key technologies such as search engine, BBS, knowledge base and etc, a platform for medical equipment service technician to use by online or offline was designed. The platform provides users with knowledge services and interactive services, enabling users to get a more ideal solution.
Equipment and Supplies ; Internet ; Knowledge Bases ; Maintenance

A medical image segmentation is the primary issue in computer aided diagnosis. The traditional methods did not perform the image segmentation well because of varieties of image, inadequate informations, noises, uncertain images, and deficient image data. We Propose a new medical image segmentation by machine learning using background knowledge of segmentation pattern. The proposed algorithm is applied to real brain CT images. First, a region growing algorithm extracts the regions and statistical data. Also, shape informations about each regions are gathered. A supervisor makes a set of learning examples by selecting the regions which should be in one region. In the next step, some rules for merging regions are discovered from common properties of the examples. Also there will be verification procedure whether the pattern is the desired one. The procedure is achieved by machine learning technique from the patterns of positive or negative examples. The systems try to recognize the improved patterns in the next step, and make a knowledge base for the segmentation. From the experimental results of the proposed algorithm which is applied to various brain images, we obtain an adaptable knowledge base and a segmented image with proper regions of brain shape.
Machine Learning* ; Brain* ; Diagnosis ; Knowledge Bases ; Learning ; Noise

Evidence-based medicine (EBM) has been defined as the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients. EBM could be a meme in medicine that is transferred from one clinical filed to another. The knowledge base that informs clinical decision has been growing with a very rapid pace making it a difficult challenge for the busy clinician to keep up with this growing and high volume of research findings. To keep up to date with the best research evidence, clinicians need a set of strategies. EBM may be the solution of this challenge. The term, Evidence-Based Psychiatry (EBP) was introduced by Elliot Goldner and Dan Bilsker in 1995. The purposes of this review are to introduce EBP and to find the best way to adopt the evidence-based approach to the practice of psychiatry in Korea. For these purposes, we reviewed the practice of EBM and discussed the application of EBP in Korean psychiatric field of medicine.
Clinical Medicine ; Evidence-Based Medicine ; Humans ; Knowledge Bases ; Korea

PURPOSE: We wanted to develop and test an artificial intelligence (AI) to assist physicians in making the thin-section CT diagnosis of diffuse pulmonary diseases. MATERIALS AND METHODS: The AI was composed of knowledge bases (KB) of 12 diffuse pulmonary diseases and an inference engine (IE). The KB of a disease included both the inclusion criteria (IC) and the exclusion criteria (EC), which were the clinical or thin-section CT findings that were known to be present or absent in that particular disease, respectively. From imputing the clinical or thin-section CT findings by the operator who was reading the thin-section CT, AI instantly executed the following two steps. First, the IE eliminated all diseases from the list which the EC had for those particular findings. Next, from a list of remaining diseases, the AI selected those diseases having those findings in its IC to formulate the 1st-step differential diagnosis (DD1). For the differential diagnosis in the next step, the reader could choose one more clinical or thin-section CT finding from the new list: [(all the findings in the IC or EC of DD1) - (the findings in the IC common to all the DD1s)]. The reader could proceed even further if needed. The system was tested on 10 radiology residents who solved 24 problems (two problems for each of 12 diffuse pulmonary diseases) without and then with the aid of the AI. The scores were compared using the Wilcoxon signed rank test. RESULTS: An AI was made; it was composed of 280 rules (214 IC and 66 EC) and three interfaces (two for program management and another for problem solving). Contestants scored higher (p = 0.0078) using the AI (167 vs. 110 respectively), and they responded that they felt that the program was helpful in making decisions. CONCLUSION: AI appeared to be helpful in making thin-section CT diagnosis.
Artificial Intelligence* ; Diagnosis* ; Diagnosis, Differential ; Knowledge Bases ; Lung ; Lung Diseases*

The task for chromosome karyotyping and diagnosis is requiring repetitive, time consuming job and high cost even it is done by well-experienced cytogenetists. Therefore an web based chromosome karyotyping instruction system has been established to be able to analyze chromosomes and obtain necessary advises from the database instead of human experts and the database is including 2 divisions with database and agent.For the first of all, database model was constructed with relational database consisting of Patient_DB, image_DB, Disease_DB and Manage_DB. As the second procedure, knowledge base by IF THEN production rule was implemented to a knowledge domain with normal and abnormal chromosomes. For the last, independent agent with the inference by knowledge base could enter the inference data into the database.Experimental data were composed of normal chromosomes of 2,736 patients' cases and abnormal chromosomes of 259 patients' cases that have been obtained from GTG-banding metaphase peripheral blood and amniotic fluid samples.The completed system provides variously morphological information by analysis of normal or abnormal chromosomes and it also makes users enable to control and search the information in a short period with learning of high amount of knowledge.
Amniotic Fluid ; Diagnosis ; Female ; Humans ; Karyotyping* ; Knowledge Bases ; Learning ; Metaphase

Two matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS)-based methods were compared for their ability to identify viridans streptococci. One approach employed a reference database and software developed in-house. All inhouse measurements were performed using an Autoflex II Instrument (Bruker Daltonics GmbH, Germany). The other system, a VITEK-MS (BioMérieux, France) was operated on the commercially available V2.0 Knowledge Base for Clinical Use database. Clinical isolates of viridans streptococci (n=184) were examined. Discrepant results were resolved by 16S rDNA sequencing. Species-level identification percentages were compared by a chi-square test. The in-house method correctly identified 179 (97%) and 175 (95%) isolates to the group and species level respectively. In comparison, the VITEK-MS system correctly identified 145 (79%) isolates to the group and species level. The difference between the two methods was statistically significant at both group and species levels. Using the Autoflex II instrument combined with an extraction method instead of whole cell analysis resulted in more reliable viridans streptococci identification. Our results suggest that combining extraction with powerful analysis software and the careful choice of well-identified strains included into the database was useful for identifying viridans streptococci species.
DNA, Ribosomal ; Knowledge Bases ; Mass Spectrometry* ; Methods* ; Viridans Streptococci*

A wide range of e-learning modalities are widely integrated in medical education. However, some of the key questions related to the role of e-learning remain unanswered, such as (1) what is an effective approach to integrating technology into pre-clinical vs. clinical training?; (2) what evidence exists regarding the type and format of e-learning technology suitable for medical specialties and clinical settings?; (3) which design features are known to be effective in designing on-line patient simulation cases, tutorials, or clinical exams?; and (4) what guidelines exist for determining an appropriate blend of instructional strategies, including on-line learning, face-to-face instruction, and performance-based skill practices? Based on the existing literature and a variety of e-learning examples of synchronous learning tools and simulation technology, this paper addresses the following three questions: (1) what is the current trend of e-learning in medical education?; (2) what do we know about the effective use of e-learning?; and (3) what is the role of e-learning in facilitating newly emerging competency-based training? As e-learning continues to be widely integrated in training future physicians, it is critical that our efforts in conducting evaluative studies should target specific e-learning features that can best mediate intended learning goals and objectives. Without an evolving knowledge base on how best to design e-learning applications, the gap between what we know about technology use and how we deploy e-learning in training settings will continue to widen.
Computer-Assisted Instruction ; Education, Medical* ; Knowledge Bases ; Learning ; Patient Simulation

As an methodology for promoting the quality and efficiency of health care, clinical decision support systems (CDSSs) have gained much improvement. The knowledge base (KB) plays an important role in DSS. For CDSSs, the construction of KB means modeling the medical knowledge based on a suitable model. This study analyzes the SAGE model, then implements it on knowledge of diagnosis and treatment of Metabolic Syndrome (MS), and improves the SAGE to enhance its expression ability. The model is constructed as the KB in CDSS, and be applied in hospital. The evaluation result of CDSS reveals that the SAGE model should be useful in clinical application. Finally, this study propounds some points yet to be improved in the SAGE.
Decision Support Systems, Clinical ; Knowledge Bases ; Models, Theoretical ; Research Design

OBJECTIVES: This research presents the design and development of a software architecture using natural language processing tools and the use of an ontology of knowledge as a knowledge base. METHODS: The software extracts, manages and represents the knowledge of a text in natural language. A corpus of more than 200 medical domain documents from the general medicine and palliative care areas was validated, demonstrating relevant knowledge elements for physicians. RESULTS: Indicators for precision, recall and F-measure were applied. An ontology was created called the knowledge elements of the medical domain to manipulate patient information, which can be read or accessed from any other software platform. CONCLUSIONS: The developed software architecture extracts the medical knowledge of the clinical histories of patients from two different corpora. The architecture was validated using the metrics of information extraction systems.
Humans ; Information Storage and Retrieval ; Knowledge Bases ; Knowledge Management ; Natural Language Processing ; Palliative Care