• Journal of Computing Science and Engineering
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• v.6 no.2
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• pp.168-177
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• 2012

The search for relevant and actionable information is a key to achieving clinical and research goals in biomedicine. Biomedical information exists in different forms: as text and illustrations in journal articles and other documents, in images stored in databases, and as patients' cases in electronic health records. This paper presents ways to move beyond conventional text-based searching of these resources, by combining text and visual features in search queries and document representation. A combination of techniques and tools from the fields of natural language processing, information retrieval, and content-based image retrieval allows the development of building blocks for advanced information services. Such services enable searching by textual as well as visual queries, and retrieving documents enriched by relevant images, charts, and other illustrations from the journal literature, patient records and image databases.

• Journal of Oriental Neuropsychiatry
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• v.13 no.2
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• pp.1-10
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• 2002

Dreams are experiences of life that is recognized to have taken place in .the mind while asleep. The phenomenon of dreams is occured by external stimuli such as somatic factor and emotion-arousing pre-sleep stimuli. The contents of dreams reflect the dreamer's unique psychic situation, we can view them from category of Yin and Yang and the Five Elements(陰陽五行). Also dreams are characterized by an objectivity that provides whatever in necessary for psychic balance, regardless of the ego's wishes. Hence dreams are objective sources of psychotherapy. The doctor must not treat patient by the doctor's subject mind. The Orient has taken the idea that human beings have the divine center in the heart, so dreams in the psychotherapy of the oriental medicine are more valuable as representation of inner world. The images of dream are classified as the deficiency and excessiveness of Yin-Ki and Yang-Ki of the internal organs, an property and source of stimulus. Dream applies to diagnose the disease of internal organs and to decide to the method of medical treatment.

• Korean Journal of Psychosomatic Medicine
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• v.3 no.2
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• pp.214-221
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• 1995

There appears to be an increased incidence of anxiety symptoms in association with certain medical illness. It is usually assumed that certain medical disorders are directly associated with anxiety symptoms in that the pathophysilolgy of the medical illness somehow causes the anxiety symptoms. However, the pathophysilolgies of many such medical illnesses as well as anxiety are not known and no discrete group of medical illnesses is clearly more likely to produce anxiety symptoms than any other Moreover, anxiety symptoms may be the representation of other psychiatric symptoms, like depression and delirium. Though it is important to differentiate primary and other causes(secondary) of anxiety clinically, the clear differentiation is somewhat arbitrary, as the confusion of mind-body dichotomy. for the purpose of accurate treatment of anxiety symptoms relating medical illness, this review article focuses on those clinical features and mechanisms of anxiety that are potentially useful in differential diagnosis.

• Journal of Cardiovascular Imaging
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• v.31 no.1
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• pp.1-17
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• 2023

The Coronary Artery Calcium Data and Reporting System (CAC-DRS) is a standardized reporting method for calcium scoring on computed tomography. CAC-DRS is applied on a per-patient basis and represents the total calcium score with the number of vessels involved. There are 4 risk categories ranging from CAC-DRS 0 to CAC-DRS 3. CAC-DRS also provides risk prediction and treatment recommendations for each category. The main strengths of CAC-DRS include a detailed and meaningful representation of CAC, improved communication between physicians, risk stratification, appropriate treatment recommendations, and uniform data collection, which provides a framework for education and research. The major limitations of CAC-DRS include a few missing components, an overly simple visual approach without any standard reference, and treatment recommendations lacking a basis in clinical trials. This consistent yet straightforward method has the potential to systemize CAC scoring in both gated and non-gated scans.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.116-118
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• 2002

It has been noted that Monte Carlo simulations are the most accurate method to calculate dose distributions in any material and geometry. Monte Carlo transport algorithms determine the absorbed dose by following the path of representative particles as they travel through the medium. Accurate Monte Carlo dose calculations rely on detailed modeling of the radiation source. We modeled the effects of beam modifiers such as collimators, blocks, wedges, etc. of our accelerator, Varian Clinac 600C/D to ensure accurate representation of the radiation source using the EGSnrc based BEAM code. These were used in the EGSnrc based DOSXYZ code for the simulation of particles transport through a voxel based Cartesian coordinate system. Because Monte Carlo methods use particle-by-particle methods to simulate a radiation transport, more particle histories yield the better representation of the actual dose. But the prohibitively long time required to get high resolution and accuracy calculations has prevented the use of Monte Carlo methods in the actual clinical spots. Our ultimate aim is to develop a Monte Carlo dose calculation system designed specifically for radiation therapy planning, which is distinguished from current dose calculation methods. The purpose of this study in the present phase was to get dose calculation results corresponding to measurements within practical time limit. We used parallel processing and some variance reduction techniques, therefore reduced the computational time, preserving a good agreement between calculations of depth dose distributions and measurements within 5% deviations.

• Journal of Korean Neurosurgical Society
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• v.45 no.6
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• pp.329-335
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• 2009

Objective : Determining language lateralization is important for the presurgical evaluation of patients with medically intractable epilepsy. The Wada test has been the gold standard for lateralization of language dominance before epilepsy surgery. However, it is an invasive test with risk, and have some limitations. Methods : We compared the volumetric analysis with Wada test, and studied the clinical potential of volumetric analysis to assess language laterality in large surgical candidates with temporal lobe epilepsy (TLE). To examine the efficacy of volumetric analysis to determine language lateralization during presurgical evaluation, we compared the volumetric analysis of the bilateral planum temporale with the results of Wada test in 59 patients with chronic intractable TLE (rTLE, n=32; lTLE, n=27) who underwent epilepsy surgery. We measured the gray matter volumes of planum temporale (PT) of each patients using the VoxelPlus2 program (Mevisys, Daejeon, Korea). Results : Overall congruence of the volumetric analysis with the Wada test was 97.75% in rTLE patients and 81.5% in lTLE patients. There were more significant leftward asymmetry of the PT in rTLE patients than lTLE patients. In lTLE patients, relatively high proportion (37%) of the patients showed bilateral or right hemispheric language dominance. Conclusion : These results provide evidence that the volumetric analysis of the PT could be used as an alternatives in language lateralization. Also, the results of the Wada test suggested that there was considerable plasticity of language representation in the brains of patients with intractable TLE and it was associated with an earlier age of brain injury.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.796-801
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• 2009

In medical practice, the diagnosis or prediction models requiring complicated computations are not widely recognized due to difficulty in interpreting the course of reasoning and the complexity of computations. Medical personnel have used the nomograms which are a graphical representation for numerical relationships that enables to easily compute a complicated function without help of computation machines. It has been widely paid attention in diagnosing diseases or predicting the progress of diseases. A nomogram is constructed from a set of clinical data which contain various attributes such as symptoms, lab experiment results, therapy history, progress of diseases or identification of diseases. It is of importance to select effective ones from available attributes, sometimes along with parameters accompanying the attributes. This paper introduces a nomogram construction method that uses a naive Bayesian technique to construct a nomogram as well as a genetic algorithm to select effective attributes and parameters. The proposed method has been applied to the construction of a nomogram for a real clinical data set.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.9
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• pp.3330-3335
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• 2010

Due to the acceleration of medical information age, the semantic interoperability of clinical information is rising up as a major issue. The laboratory results are known as the best significant area among clinical information to be required for exchanging and sharing. The aim of this research is to develop the clinical contents model for exchange laboratory results. This research was conducted from March 2008 to September 2008. Firstly, the method is to get the attributes and codes from LOINC which is one of the standard medical terminology system related to laboratory and the Reference Information Model(RIM) of Health Level 7(HL7). Secondly, the attributes from each work process around orders and reports of laboratory was analyzed. We evaluated the attributes whether they could be represented into the attributes contained in HL7's RIM. Thirdly, the prototype for hemoglobin case using the structure of clinical contents model and defined attribute. Fourthly, the face validity was done by one laboratory physician with four clinicians. The assessment contents were for the suitability involved in representation and exchange with proposed model. The results shows that the model corresponds with the aim of the research. Eventually the proposed model for the exchange of laboratory results could contributes to information interchange according to laboratory area for the future.

• The KIPS Transactions:PartB
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• v.9B no.5
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• pp.625-634
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• 2002

In this paper, we conducted various experiments with Bayesian networks in order to analyze clinical data of infertility patients. With these experiments, we tried to find out inter-dependencies among important factors playing the key role in clinical pregnancy, and to compare 3 different kinds of Bayesian network classifiers (including NBN, BAN, GBN) in terms of classification performance. As a result of experiments, we found the fact that the most important features playing the key role in clinical pregnancy (Clin) are indication (IND), stimulation, age of female partner (FA), number of ova (ICT), and use of Wallace (ETM), and then discovered inter-dependencies among these features. And we made sure that BAN and GBN, which are more general Bayesian network classifiers permitting inter-dependencies among features, show higher performance than NBN. By comparing Bayesian classifiers based on probabilistic representation and reasoning with other classifiers such as decision trees and k-nearest neighbor methods, we found that the former show higher performance than the latter due to inherent characteristics of clinical domain. finally, we suggested a feature reduction method in which all features except only some ones within Markov blanket of the class node are removed, and investigated by experiments whether such feature reduction can increase the performance of Bayesian classifiers.

• Journal of Veterinary Clinics
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• v.19 no.3
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• pp.312-315
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• 2002

Diagnostic tests often require the determination of cut-off values that discriminate uninfected from infected individuals. The receiver operating characteristic (ROC) curve has been frequently used to attain this purpose and gives a representation of diagnostic accuracy (sensitivity and specificity) of a prediction model when varying the cut-point of a decision rule on a whole spectrum. We have written and tested a visual basic application program in EXCEL for maximum likelihood estimation of a binormal ROC curve, which also computes univariate statistics of a diagnostic test employed. Examples applying for computed tomographic images in radiology and methicillin-resistant Staphylococcus aureus research are given to illustrate this approach. This stand-alone module is available from the first author on request.