• Journal of Korean Academy of Nursing
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• v.36 no.4
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• pp.621-629
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• 2006

Purpose. The purpose of this study was to identify the clinical variables that predict functional and cognitive recovery at 1- and 6-month in both severe and moderate/mild traumatic brain injury patients. Methods. The subjects of this study were 82 traumatically brain-injured patients who were admitted to a Neurological Intensive Care Unit at a university hospital. Potential prognostic factors included were age, motor and pupillary response, systolic blood pressure, heart rate, and the presence of intracranial hematoma at admission. Results. The significant predictors of functional disability in severe traumatic brain injury subjects were, age, systolic blood pressure, the presence of intracranial hematoma, motor response, and heart rate at admission. In moderate/mild traumatic brain injury patients, motor response, abnormal pupil reflex, and heart rate at admission were identified as significant predictors of functional disability. On the other hand, the significant predictors of cognitive ability for severe traumatic brain injury patients were motor response and the presence of intracranial hematoma at admission, whereas those for moderate/mild patients were motor response, pupil reflex, systolic blood pressure at admission, and age. Conclusions. The results of the present study indicate that the significant predictors of TBI differ according to TBI severity on admission, outcome type, and outcome measurement time. This can be meaningful to critical care nurses for a better understanding on the prediction of brain injury patients. On the other hand, the model used in the present study appeared to produce relatively low explicabilities for functional and cognitive recovery although a direct comparison of our results with those of others is difficult due to differences in outcome definition and validation methods. This implies that other clinical variables should be added to the model used in the present study to increase its predicting power for determining functional and cognitive outcomes.

• Journal of Biomedical Engineering Research
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• v.33 no.3
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• pp.148-154
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• 2012

Hemorrhagic shock is a primary cause of deaths resulting from injury in the world. Although many studies have tried to diagnose accurately hemorrhagic shock in the early stage, such attempts were not successful due to compensatory mechanisms of humans. The objective of this study was to construct a survival prediction model of rats in acute hemorrhagic shock using a random forest (RF) model. Heart rate (HR), mean arterial pressure (MAP), respiration rate (RR), lactate concentration (LC), and peripheral perfusion (PP) measured in rats were used as input variables for the RF model and its performance was compared with that of a logistic regression (LR) model. Before constructing the models, we performed 5-fold cross validation for RF variable selection, and forward stepwise variable selection for the LR model to examine which variables were important for the models. For the LR model, sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (ROC-AUC) were 0.83, 0.95, 0.88, and 0.96, respectively. For the RF models, sensitivity, specificity, accuracy, and AUC were 0.97, 0.95, 0.96, and 0.99, respectively. In conclusion, the RF model was superior to the LR model for survival prediction in the rat model.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.230-237
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• 2022

It is difficult to predict industrial accidents in the construction industry because many accident factors, such as human-related factors and environment-related factors, affect the accidents. Many studies have analyzed the severity of injuries and types of accidents; however, there were few studies on the prediction of injured body parts. This study aims to develop a classification model to predict the part of the injured body based on accident-related factors. Construction accident cases from June 2018 to July 2021 provided by the Korea Construction Safety Management Integrated Information were collected through web crawling and then preprocessed. A naïve Bayes classifier, one of the supervised learning algorithms, was employed to construct a classification model of the injured body part, which has four categories: 1) torso, 2) upper extremity, 3) head, and 4) lower extremity. The predictor variables are accident type, type of work, facility type, injury source, and activity type. As a result, the average accuracy for each injured body part was 50.4%. The accuracy of the upper extremity and lower extremity was relatively higher than the cases of the torso and head. Unlike the other classifications, such as spam mail filtering, a naïve Bayes classifier does not provide a good classification performance in construction accidents. The reasons are discussed in the study. Based on the results of this study, more detailed guidelines for construction safety management can be provided, which help establish safety measures at the construction site.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.1
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• pp.50-59
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• 2023

The purpose of this study was to analyze the patients hospitalized with damage by injury code based on data for two years in 2020 and 2021 of A institution located in Gangneung, Gangwon-do. Analyzed the injury codes with a large number of occurrences per year, and analyzed the injury codes of external causes accordingly. The cause of the injury code was analyzed by analyzing the frequency of the injury code of external causes. Injury code S0650 had the highest frequency of injury code of external causes W189 and X5999, which was judged to be the cause of traumatic subdural hemorrhage without open intracranial wounds when falling in an unspecified place or toilet. Injury code S72120 had the highest frequency of injury code of external causes W010 and W180, and it was judged to be the cause of obstructive femoral intertrochanteric fracture that occurs when falling in the residence. The injury code S32090 had a high frequency of X5999, and it was analyzed that it caused the obstructive fracture of the lumbar region due to an accident caused by exposure in an unspecified place, and the injury code S72.090 had a high frequency of W010 and W180. It was confirmed that the cause of the obstructive fracture of the femoral neck was mainly caused by slipping or slipping in the residence, and the injury code S0220 had a high frequency of the injury code of external causes Y049, and it was confirmed that the fibula was fractured mainly by the force or fist. As such, the cause of the injury code was analyzed by analyzing the frequency of the injury code for each injury code of external causes.

• Korean Journal of Occupational Health Nursing
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• v.25 no.1
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• pp.65-74
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• 2016

Purpose: The purpose of this study is to predict industrial accident rate using time series analysis. Methods: The rates of industrial accident and occupational injury death were analyzed using industrial accident statistics analysis system of the Korea Occupational Safety and Health Agency from 2001 to 2014. Time series analysis was done using the most recent data, such as raw materials of Economically Active Population Survey, Economic Statistics System of the Bank of Korea, and e-National indicators. The best-fit model with time series analysis to predict occupational injury was developed by identifying predictors when the value of Akaike Information Criteria was the lowest point. Variables into the model were selected through a series of expertises' consultations and literature review, which consisted of socioeconomic structure, labor force structure, working conditions, and occupational accidents. Results: Indexes at the meso- and macro-levels predicting well occurrence of occupational accidents and occupational injury death were labor force participation rate for ages 45-49 and budget for small scaled workplace support. The rates of industrial accident and occupational injury death are expected to decline. Conclusion: For reducing industrial accident continuously, we call for safe employment policy of economically active middle aged adults and support for improving safety work environment of small sized workplace.

• Fire Science and Engineering
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• v.26 no.5
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• pp.13-20
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• 2012

Many fire-fighters suffer from the burn injuries, and the severe burns are the most catastrophic injury a person can survive, resulting in pain, emotional stress, and tremendous economic costs. It is important to understand the physiology of burns for prevention from skin burns and a successful treatment of a burn patient. But a few researches have been presented because the complex physical phenomena of our inside body like non-linearity characteristics of human skin make them difficult. Thus in this study, thermal analyses of biological tissues exposed to a flash fire causing severe tissue damage were studied by using a finite difference method based on the Pennes bio-heat equation. The several previous models for skin thermo-physical properties were summarized, and the calculated values with those models of tissue injury were compared with the results obtained by the previous experiment for low heat flux conditions. The skin models with good agreement could be found. Also, the skin burn injury prediction results with the best model for high heat flux conditions by flash flame were suggested.

• Journal of Integrative Natural Science
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• v.10 no.3
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• pp.119-124
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• 2017

Glucagon-like peptide 2 receptor, a GPCR, binds with the glucagon-like peptide, GLP-2 and regulates various metabolic functions in the gastrointestinal tract. It plays an important role in the nutrient homeostasis related to nutrient assimilation by regulating mucosal epithelium. GLP-2 receptor affects the cellular response to external injury, by controlling the intestinal crypt cell proliferation. As they are therapeutically attractive towards diseases related with the gastrointestinal tract, it becomes essential to analyse their structural features to study the pathophysiology of the diseases. As the three dimensional structure of the protein is not available, in this study, we have performed the homology modelling of the receptor based on single- and multiple template modeling. The models were subjected to model validation and a reliable model based on the validation statistics was identified. The predicted model could be useful in studying the structural features of GLP-2 receptor and their role in various diseases related to them.

• Journal of Korean Society of Transportation
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• v.31 no.6
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• pp.53-66
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• 2013

The major purpose of this study is to evaluate methodologies to predict the injury severity of pedestrian-vehicle collisions. Methodologies to be evaluated and compared in this study include Binary Logistic Regression(BLR), Ordered Probit Model(OPM), Support Vector Machine(SVM) and Decision Tree(DT) method. Valuable insights into applying methodologies to analyze the characteristics of pedestrian injury severity are derived. For the purpose of identifying causal factors affecting the injury severity, statistical approaches such as BLR and OPM are recommended. On the other hand, to achieve better prediction performance, heuristic approaches such as SVM and DT are recommended. It is expected that the outcome of this study would be useful in developing various countermeasures for enhancing pedestrian safety.

• Journal of Digital Convergence
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• v.16 no.1
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• pp.231-242
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• 2018

The efficient management of the Length of Stay(LOS) is important in hospital. It is import to reduce medical cost for patients and increase profitability for hospitals. In order to efficiently manage LOS, it is necessary to develop an artificial intelligence-based prediction model that supports hospitals in benchmarking and reduction ways of LOS. In order to develop a predictive model of LOS for acute stroke patients, acute stroke patients were extracted from 2013 and 2014 discharge injury patient data. The data for analysis was classified as 60% for training and 40% for evaluation. In the model development, we used traditional regression technique such as multiple regression analysis method, artificial intelligence technique such as interactive decision tree, neural network technique, and ensemble technique which integrate all. Model evaluation used Root ASE (Absolute error) index. They were 23.7 by multiple regression, 23.7 by interactive decision tree, 22.7 by neural network and 22.7 by esemble technique. As a result of model evaluation, neural network technique which is artificial intelligence technique was found to be superior. Through this, the utility of artificial intelligence has been proved in the development of the prediction LOS model. In the future, it is necessary to continue research on how to utilize artificial intelligence techniques more effectively in the development of LOS prediction model.

• Korean Journal of Agricultural and Forest Meteorology
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• v.4 no.3
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• pp.164-168
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• 2002

Yield prediction model of rice based on the boundary line analysis of the relationships between rice yield and meteorological conditions during rice growing period was reported in the previous report (Kim et al, 2001). Using the 15-year data of the 20 locations used for the model formulation and of the 12 locations not used, the model was tested for its predictability of location to location, year to year, and variety to variety variation of rice yield. The model predicted reliably the mean yield differences among locations, the yearly yield variation in each location, and the yield variation by variety. However, the model showed relatively lower predictability for the years of cool weather injury especially in mountainous locations. In conclusion, the model using boundary line analysis could be used to predict the yield responses to meteorological conditions during rice growth period and the locational, yearly, and varietal variations of rice yield. And the predictability of the present yield prediction model might be improved by including the boundary line analysis for the other factors such as soil characteristics, fertilization levels, etc.