• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.10
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• pp.4910-4918
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• 2013

This study aims to design a Severity-Adjusted LOS(Length of Stay) Model in order to efficiently manage LOS of AMI(Acute Myocardial Infarction) patients. We designed a Severity-Adjusted LOS Model with using data-mining methods(multiple regression analysis, decision trees, and neural network) which covered 6,074 AMI patients who showed the diagnosis of I21 from 2004-2009 Korean National Hospital Discharge in-depth Injury Survey. A decision tree model was chosen for the final model that produced superior results. This study discovered that the execution of CABG, status at discharge(alive or dead), comorbidity index, etc. were major factors affecting a Sevirity-Adjustment of LOS of AMI patients. The difference between real LOS and adjusted LOS resulted from hospital location and bed size. The efficient management of LOS of AMI patients requires that we need to perform various activities after identifying differentiating factors. These factors can be specified by applying each hospital's data into this newly designed Severity-Adjusted LOS Model.

• Journal of the Korea Convergence Society
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• v.12 no.8
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• pp.1-11
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• 2021

The purpose of this study was to analyze the trends and characteristics of 'stress urinary incontinence' research through word frequency analysis, and their relationships were modeled using word embedding. Abstract data of 9,868 papers containing abstracts in PubMed's MEDLINE were extracted using a Python program. Then, through frequency analysis, 10 keywords were selected according to the high frequency. The similarity of words related to keywords was analyzed by Word2Vec machine learning algorithm. The locations and distances of words were visualized using the t-SNE technique, and the groups were classified and analyzed. The number of studies related to stress urinary incontinence has increased rapidly since the 1980s. The keywords used most frequently in the abstract of the paper were 'woman', 'urethra', and 'surgery'. Through Word2Vec modeling, words such as 'female', 'urge', and 'symptom' were among the words that showed the highest relevance to the keywords in the study on stress urinary incontinence. In addition, through the t-SNE technique, keywords and related words could be classified into three groups focusing on symptoms, anatomical characteristics, and surgical interventions of stress urinary incontinence. This study is the first to examine trends in stress urinary incontinence-related studies using the keyword frequency analysis and word embedding of the abstract. The results of this study can be used as a basis for future researchers to select the subject and direction of the research field related to stress urinary incontinence.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.3
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• pp.1234-1243
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• 2011

Our study was carried out to develop the severity-adjustment model for length of stay in hospital for community-acquired pneumonia so that we analysed the factors on the variation in length of stay(LOS). The subjects were 5,353 community-acquired pneumonia inpatients of the Korean National Hospital Discharge In-depth Injury Survey data from 2004 through 2006. The data were analyzed using t-test and ANOVA and the severity-adjustment model was developed using data mining technique. There are differences according to gender, age, type of insurance, type of admission, but there is no difference of whether patients died in hospital. After yielding the standardized value of the difference between crude and expected length of stay, we analysed the variation of length of stay for community-acquired pneumonia. There was variation of LOS in regional differences and insurance type, though there was no variation according whether patients receive their care in their residences. The variation of length of stay controlling the case mix or severity of illness can be explained the factors of provider. This supply factors in LOS variations should be more studied for individual practice style or patient management practices and healthcare resources or environment. We expect that the severity-adjustment model using administrative databases should be more adapted in other diseases in practical.

• Journal of Digital Convergence
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• v.14 no.2
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• pp.325-332
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• 2016

The purpose of this study is to investigate the attributes influencing the prevalence of metabolic syndrome and develop the prediction model for metabolic syndrome over 40-aged people from Korea Health and Nutrition Examination Study 2012. The researcher chose the attributes for prediction model through literature review. Also, we used the decision tree, logistic regression, artificial neural network of data mining algorithm through Weka 3.6. As results, social economic status factors of input attributes were ranked higher than health-related factors. Additionally, prediction model using decision tree algorithm showed finally the highest accuracy. This study suggests that, first of all, prevention and management of metabolic syndrome will be approached by aspect of social economic status and health-related factors. Also, decision tree algorithms known from other research are useful in the field of public health due to their usefulness of interpretation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.435-447
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• 2019

In order to prevent unnecessary re-admission, it is necessary to intensively manage the groups with high probability of re-admission. For this, it is necessary to develop a re-admission prediction model. Two - year discharge summary data of one university hospital were collected from 2016 to 2017 to develop a predictive model of re-admission. In this case, the re-admitted patients were defined as those who were discharged more than once during the study period. We conducted descriptive statistics and crosstab analysis to identify the characteristics of rehospitalized patients. The re-admission prediction model was developed using logistic regression, neural network, and decision tree. AUC (Area Under Curve) was used for model evaluation. The logistic regression model was selected as the final re-admission predictive model because the AUC was the best at 0.81. The main variables affecting the selected rehospitalization in the logistic regression model were Residental regions, Age, CCS, Charlson Index Score, Discharge Dept., Via ER, LOS, Operation, Sex, Total payment, and Insurance. The model developed in this study was limited to generalization because it was two years data of one hospital. It is necessary to develop a model that can collect and generalize long-term data from various hospitals in the future. Furthermore, it is necessary to develop a model that can predict the re-admission that was not planned.