• Journal of the Korean Data and Information Science Society
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• v.25 no.5
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• pp.1095-1106
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• 2014

Symbolic data analysis extends the data mining and exploratory data analysis to the knowledge mining, we can suggest the SDA tree model on clinical and genomic data with new knowledge mining SDA approach. Using SDA application for huge genomic SNP data, we can get the correlation the availability of understanding of hidden structure of HCC data could be proved. We can confirm validity of application of SDA to the tree structured progression model and to quantify the clinical lab data and SNP data for early diagnosis of HCC. Our proposed model constructs the representative model for HCC survival time and causal association with their SNP gene data. To fit the simple and easy interpretation tree structured survival model which could reduced from huge clinical and genomic data under the new statistical theory of knowledge mining with SDA.

• Journal of Biomedical Engineering Research
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• v.32 no.2
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• pp.134-143
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• 2011

The selection of meaningful clinical tests and its reference values from a high-dimensional clinical data with imbalanced class distribution, one class is represented by a large number of examples while the other is represented by only a few, is an important issue for differential diagnosis between similar diseases, but difficult. For this purpose, this study introduces methods based on the concepts of both discernibility matrix and function in rough set theory (RST) with two discretization approaches, equal width and frequency discretization. Here these discretization approaches are used to define the reference values for clinical tests, and the discernibility matrix and function are used to extract a subset of significant clinical tests from the translated nominal attribute values. To show its applicability in the differential diagnosis problem, we have applied it to extract the significant clinical tests and its reference values between normal (N = 351) and abnormal group (N = 101) with either cholecystitis or cholelithiasis disease. In addition, we investigated not only the selected significant clinical tests and the variations of its reference values, but also the average predictive accuracies on four evaluation criteria, i.e., accuracy, sensitivity, specificity, and geometric mean, during l0-fold cross validation. From the experimental results, we confirmed that two discretization approaches based rough set approximation methods with relative frequency give better results than those with absolute frequency, in the evaluation criteria (i.e., average geometric mean). Thus it shows that the prediction model using relative frequency can be used effectively in classification and prediction problems of the clinical data with imbalanced class distribution.

• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.312-320
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• 2011

The goal of this paper is to examine the effects of a CDSS(Clinical Decision Support System) for drug prescription on organizational performance in medical institutions using POC(Point Of Care) systems. For achieving this goal, evaluation factors for influencing performance of information system were identified by using the performance evaluation model for CDSS. In the results, there was significant causality between each evaluation domain except system quality domain. In addition, the system quality of CDSS for optimal drug prescription has no influence on user satisfaction. But information quality has positive influence on user satisfaction which has also a positive influence on organizational performance.

• The Journal of Korean Physical Therapy
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• v.24 no.3
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• pp.171-180
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• 2012

Purpose: The purpose of this study was to investigate the clinical decision making (CDM) development process throughout the comparison between novice and expert physical therapist as well as develop a CDM model for physical therapists under the fee-for-service (FFS) and physicians' prescriptions. Methods: Purposive sampling techniques were used to select 10 clinical physical therapists paired into five groups (each pair consisted of 1 novice and 1 expert physical therapist). The coding schemes were extracted from interviews and through within- and across-case analyses, cases were summarized. The reliability of coding schemes was confirmed by checking of case summaries by the participants. Results: Novice and expert physical therapists were influenced by two themes, internalized theme and external forces or information. Novice clinicians depended more on external forces or information. Although clinicians should care patients under the FFS and physician's prescription, expert clinicians were more likely to rely on internalized knowledge. Conclusion: The findings of the present study may be used by educators or association officials enhance CDM abilities and knowledge pools of student or novices as well as develop a guide to suitable novices or students under the specific context limiting the development of their CDM.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.1083-1090
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• 2016

In clinical studies, different types of outcomes (e.g. repeated measures data and time-to-event data) for the same subject tend to be observed, and these data can be correlated. For example, a response variable of interest can be measured repeatedly over time on the same subject and at the same time, an event time representing a terminating event is also obtained. Joint modelling using a shared random effect is useful for analyzing these data. Inferences based on marginal likelihood may involve the evaluation of analytically intractable integrations over the random-effect distributions. In this paper we propose a joint HGLM approach for analyzing such outcomes using the HGLM (hierarchical generalized linear model) method based on h-likelihood (i.e. hierarchical likelihood), which avoids these integration itself. The proposed method has been demonstrated using various numerical studies.

• Clinical Nutrition Research
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• v.12 no.2
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• pp.138-153
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• 2023

The prevalence of metabolic syndrome (MetS) and its cost are increasing due to lifestyle changes and aging. This study aimed to develop a deep neural network model for prediction and classification of MetS according to nutrient intake and other MetS-related factors. This study included 17,848 individuals aged 40-69 years from the Korea National Health and Nutrition Examination Survey (2013-2018). We set MetS (3-5 risk factors present) as the dependent variable and 52 MetS-related factors and nutrient intake variables as independent variables in a regression analysis. The analysis compared and analyzed model accuracy, precision and recall by conventional logistic regression, machine learning-based logistic regression and deep learning. The accuracy of train data was 81.2089, and the accuracy of test data was 81.1485 in a MetS classification and prediction model developed in this study. These accuracies were higher than those obtained by conventional logistic regression or machine learning-based logistic regression. Precision, recall, and F1-score also showed the high accuracy in the deep learning model. Blood alanine aminotransferase (β = 12.2035) level showed the highest regression coefficient followed by blood aspartate aminotransferase (β = 11.771) level, waist circumference (β = 10.8555), body mass index (β = 10.3842), and blood glycated hemoglobin (β = 10.1802) level. Fats (cholesterol [β = -2.0545] and saturated fatty acid [β = -2.0483]) showed high regression coefficients among nutrient intakes. The deep learning model for classification and prediction on MetS showed a higher accuracy than conventional logistic regression or machine learning-based logistic regression.

• Journal of Convergence for Information Technology
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• v.11 no.8
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• pp.53-61
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• 2021

The purpose of this study was to investigate the menstrual pain, clinical practice stress, and clinical competence and identify influencing factors of clinical competence of 3rd nursing students who start clinical practice for the first tim. The survey was conducted on 155 nursing students from June 1 to October 30, 2020. Data were analyzed using t-test, ANOVA, Scheffe test, Pearson correlation coefficients and multiple regression analysis. The degree of influence menstrual pain was 5.01points, clinical practice stress was 2.82points, clinical competence was 3.42points. Menstrual pain was positive correlated with clinical practice stress(r=.319, p=.000), and menstrual pain was negative correlated with clinical competence(r=-.279, p=.000). Clinical practice stress was negative correlated with clinical competence(r=-.333, p=.005). Menstrual pain was main factor that affects clinical competence. The model explained 25.0% of the variables. Therefore, intervention education is needed to reduce menstrual pain in order to improve the clinical competence of nursing students.

• Korean Medical Education Review
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• v.22 no.3
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• pp.173-188
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• 2020

Medical humanities education (MHE) is as essential as basic medical sciences and clinical medicine education. Despite the importance of MHE, MHE curriculum development (CD) has proven to be challenging. This critical review examines the MHE CD at one medical school. The critical review methodology was developed based on Kern's six step CD model to systematically examine the CD of "Doctoring and Medical Humanities (DMH)" at the Yonsei University College of Medicine. Five review questions were developed related to (1) necessity, (2) direction and purpose, (3) design, (4) operation, and (5) evaluation of CD based on Kern's model. The review showed that the process of DMH CD mapped to components of Kern's model. The DMH curriculum content selected was closely related to medical practice and aimed to combine the acquisition of understanding and skills by designing a student-participatory curriculum based on clinical cases. Assessment methods that emphasized students' reflections were actively introduced in the evaluation section. Since the regular committee for DMH continued the work of the special ad hoc committees for DMH CD, the CD was effectively completed. However, the planning and evaluation functions and responsibilities of the DMH committee need to be strengthened. Despite the apparent limitations, the fact that students showed a high satisfaction rate and preferred small group discussions based on clinical cases has significant implications in the instructional design of MHE, where changes in self-awareness and attitude are more important than the acquisition of information. It is necessary to systematically review and study students' reflection results produced by the changed assessment methods and to develop assessment indicators for MHE that reflect the achievements of the MHE competencies of students.

• International Journal of Advanced Culture Technology
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• v.12 no.1
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• pp.242-248
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• 2024

Contemporary societal and environmental transformations coincide with the emergence of novel mental health challenges. anxiety disorder, a chronic and highly debilitating illness, presents with diverse clinical manifestations. Epidemiological investigations indicate a global prevalence of 5%, with an additional 10% exhibiting subclinical symptoms. Notably, 9% of adolescents demonstrate clinical features. Untreated, anxiety disorder exerts profound detrimental effects on individuals, families, and the broader community. Therefore, it is very meaningful to predict anxiety disorder through machine learning algorithm analysis model. The main research content of this paper is the analysis of the prediction model of anxiety disorder by machine learning algorithms. The research purpose of machine learning algorithms is to use computers to simulate human learning activities. It is a method to locate existing knowledge, acquire new knowledge, continuously improve performance, and achieve self-improvement by learning computers. This article analyzes the relevant theories and characteristics of machine learning algorithms and integrates them into anxiety disorder prediction analysis. The final results of the study show that the AUC of the artificial neural network model is the largest, reaching 0.8255, indicating that it is better than the other two models in prediction accuracy. In terms of running time, the time of the three models is less than 1 second, which is within the acceptable range.

• The Korean Journal of Health Service Management
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• v.11 no.4
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• pp.53-65
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• 2017

Objectives : The purpose of this study was to explore and describe the factors related to clinical nurses' organizational socialization, process and to find out the strategic information for successful organizational socialization. Methods : Data were collected with a structured questionnaires from 300 clinical nurses. The data were analyzed with SPSS/WIN 21.0. Results : First, the average score for the organizational socialization($2.95{\pm}0.37$), organization climate($3.28{\pm}0.43$), autonomy($3.23{\pm}0.43$), role stress($3.21{\pm}0.56$), professional self-concept($3.19{\pm}0.46$), organization value internalization($3.11{\pm}0.59$), and perceptional justice($2.91{\pm}0.50$). Second, influencing factor of organizational socialization of the participant were organizational climate, role stress, professional self-concept, Job esteem, Living arrangement type, collaboration between medical professionals in hospital, the other hospital work experience, role model or Mentor, total hospital career, perceived health status, spouse, perceptional justice, Adjusted $R^2=.702$. Conclusions : These results suggest that organizational socialization of clinical nurses could be enhanced by organizational climate. Thus creating a positive organizational climate are mandated for clinical nurses to have constructive organizational socialization.