• Journal of the Korea Society of Computer and Information
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• v.21 no.1
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• pp.115-123
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• 2016

In this study, we collect various side effect pairs which are appeared frequently at many drugs, and select side effect pairs that have higher severity. For every selected side effect pair, we extract common genetic networks which are shared by side effects' genes and drugs' target genes based on PPI(Protein-Protein Interaction) network. For this work, firstly, we gather drug related data, side effect data and PPI data. Secondly, for extracting common genetic network, we find shortest paths between drug target genes and side effect genes based on PPI network, and integrate these shortest paths. Thirdly, we develop a classification model which uses this common genetic network as a classifier. We calculate similarity score between the common genetic network and genetic network of a drug for classifying the drug. Lastly, we validate our classification model by means of AUC(Area Under the Curve) value.

• Health Policy and Management
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• v.14 no.1
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• pp.121-147
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• 2004

Since the pilot program for a DRG-based prospective payment system was introduced in 1997, the performance of KDRGs has been one of hotly debated issues. The objectives of this study are to refine the classification algorithm of the KDRGs and to assess the improvement achieved by the refinement. The U.S. Medicare DRGs version 17.0 and the Australian Refined DRGs version 4.1 were reviewed to identify areas of possible impro-vement. Refined changes in the classification and result of date analyses were submitted to a panel of 48 physicians for their reviews and suggestions. The refinement was evaluated by the variance reduction in resource utilization achieved by the KDRG The database of 2,182,168 claims submitted to the Health Insurance Review Agency during 2002 was used for evaluation. As the result of the refinement, three new MDCs were introduced and the number of ADEGs increased from 332 to 674. Various age splits and two to four levels of severity classification for secondary diagnoses were introduced as well. A total of 1,817 groups were defined in the refined KDRGs. The variance reduction for charges of all patients increased from 48.2％ to 53.6％ by the refinement, and from 65.6％ to 73.1％ for non-outlier patients. The r-square for length of stays of all patients was increased from 28.3％ to 32.6％, and from 40.4％ to 44.9％ for non-outlier patients. These results indicated a significant improvement in the classification accuracy of the KDRG system.

• Journal of Korean Academy of Nursing
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• v.17 no.1
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• pp.9-23
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• 1987

Nursing service, as the largest user of labor resources, has become concerned about appropriate allocation of staffing resources. Therefore, this project was designed to measure quantitatively the direct nursing care provided to patients and to develop a new patient classification system based on the direct nursing care activities. The initial step in the development of the classification instrument was to identify the content of direct nursing activities. The frequency with which these activities were carried out, the total time spent in carrying them out and the average time for one performance of each of the nursing activities was calculated. The next step was to select the items for the classification instrument taking into account these direct nursing activities. A list of 40 items was prepared. These items were then classified into 8 major categories: personal hygiene, moving ＆ exercise, nutrition & elimination, observation, medication, treatment, collecting specimens and other care activities for severity ill patients. Each item was assigned a value unit based on the average time required by the nursing staff to complete the specific item. The third step was to determine the practicality of the items and value units, so an attempt was made to establish content validity for these items and units by obtaing a consensus from 8 head nurses, representing eight different departments. The 4th step was to conducted a pilot study to establish the score range for the classification boundaries. For this purpose an instrument was designed using the list of items and value units and a prepared classification criteria as a guideline to validate the patient classification. A judgment group consisting of 52 supervisory nurses and head nurses were asked to select the proper patient to fit each classification criteria and to fill out the instrument for each patient. The total value unit and the frequency for each classification group was calculated. According to the frequency distribution, the score range for the classification group was determined as follows : 0~15 for groupI, 16~30 for group II, 31~50 for group III, and above 51 for group IV. Finally a patient classification form was developed.

• Journal of Auto-vehicle Safety Association
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• v.10 no.1
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• pp.20-26
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• 2018

The fatality of rollover accidents in motor vehicle crashes is high despite their low incidence. Through the investigation of a 12-passenger van rollover accident in which 10 passengers were involved, we intend to analyze the correlation between the severity of the injury and the position of the occupants. We collected accident information from medical records, interviews, photo-images of the damaged van, field surveys, and the results of the Korean New Car Assessment Program (KNCAP). Based on the occupants' position, we classified injury sites and estimated injury severity. Passenger injury severity was evaluated by trauma score calculation. The initiation type of the rollover accident was passenger side 'fall-over' and the Collision Deformation Classification (CDC) code for the damaged van was 00TDZO3. The crash of the van involved 10 passengers, with an average age of $16.3{\pm}4.2years$. Few of the occupants had fastened seat belts at the time of the incident, and there was no airbag installed. One patient sustained severe liver injury and another was diagnosed with a fracture of the right humerus. The most common injuries were at the upper extremities and the neck. The average of Injury Severity Score (ISS) was $4.8{\pm}5.9$, and the average ISS of right-seated, mid-seated and left-seated occupants was $7.5{\pm}9.3$, $1.5{\pm}0.7$, and $3.3{\pm}2.1$ respectively (p>0.05). In the rollover (to-passenger side) accident of occupant unfastened, the average ISS of right-seated occupants (near side) was higher, but there was no statistically significant difference.

• Clinics in Shoulder and Elbow
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• v.19 no.2
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• pp.90-95
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• 2016

Background: The aim of this study was to investigate the correlation between the type of subscapularis tendon tears diagnosed during arthroscopy and the outcomes of physical tests and of isokinetic muscle strength tests. Methods: We preoperatively evaluated physical outcomes and isokinetic muscle strength of 60 consecutive patients who underwent an arthroscopic rotator cuff repair and/or subacromial decompression. We divided the patients into five groups according to the type of subscapularis tear, which we classified using Lafosse classification system during diagnostic arthroscopic surgery. Results: When we performed a trend analysis between the outcomes of the physical tests and the severity of subscapularis tendon tear, we found that both the incidence of positive sign of the collective physical tests and that of individual physical tests increased significantly as the severity of the subscapularis tear increased (p<0.001). Similarly, the deficit in isokinetic muscle strength showed a tendency to increase as the severity of subscapularis tear increased, but this positive correlation was statistically significant in only the deficit between those with Lafosse type II tears and those with Lafosse type III tears. Conclusions: Although no single diagnostic test surpasses above others in predicting the severity of a subscapularis tear, our study implies that, as a collective unit of tests, the total incidence of the positive rate of the physical tests and the extent of isokinetic strength deficit may correlate with severity of subscapularis tears.

• Journal of Digital Convergence
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• v.19 no.4
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• pp.25-31
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• 2021

Moving toward an aged society, traffic accidents involving elderly drivers have also attracted broader public attention. A rapid increase of senior involvement in crashes calls for developing appropriate crash-severity prediction models specific to senior drivers. In that regard, this study leverages machine learning (ML) algorithms so as to predict the severity of vehicle-pedestrian collisions induced by elderly drivers. Specifically, four ML algorithms (i.e., Logistic model, K-nearest Neighbor (KNN), Random Forest (RF), and Support Vector Machine (SVM)) have been developed and compared. Our results show that Logistic model and SVM have outperformed their rivals in terms of the overall prediction accuracy, while precision measure exhibits in favor of RF. We also clarify that driver education and technology development would be effective countermeasures against severity risks of senior driver-induced collisions. These allow us to support informed decision making for policymakers to enhance public safety.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1123-1130
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• 2020

This study aims to classify the severity in car crashes using five classification learning models. The dataset used in this study contains 21,013 vehicle crashes, obtained from Korea Expressway Corporation, between the year of 2015-2017 and the LightGBM(Light Gradient Boosting Model) performed well with the highest accuracy. LightGBM, the number of involved vehicles, type of accident, incident location, incident lane type, types of accidents, types of vehicles involved in accidents were shown as priority factors. Based on the results of this model, the establishment of a management strategy for response of highway traffic accident should be presented through a consistent prediction process of accident severity level. This study identifies applicability of Machine Learning Models for Predicting of the Severity of Car Traffic Accidents on a Highway and suggests that various machine learning techniques based on big data that can be used in the future.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.95-100
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• 2023

Due to Traffic accidents people faces health and economical casualties around the world. As the population increases vehicles on road increase which leads to congestion in cities. Congestion can lead to increasing accident risks due to the expansion in transportation systems. Modern cities are adopting various technologies to minimize traffic accidents by predicting mathematically. Traffic accidents cause economical casualties and potential death. Therefore, to ensure people's safety, the concept of the smart city makes sense. In a smart city, traffic accident factors like road condition, light condition, weather condition etcetera are important to consider to predict traffic accident severity. Several machine learning models can significantly be employed to determine and predict traffic accident severity. This research paper illustrated the performance of a hybridized neural network and compared it with other machine learning models in order to measure the accuracy of predicting traffic accident severity. Dataset of city Leeds, UK is being used to train and test the model. Then the results are being compared with each other. Particle Swarm optimization with artificial neural network (PSO-ANN) gave promising results compared to other machine learning models like Random Forest, Naïve Bayes, Nearest Centroid, K Nearest Neighbor Classification. PSO- ANN model can be adopted in the transportation system to counter traffic accident issues. The nearest centroid model gave the lowest accuracy score whereas PSO-ANN gave the highest accuracy score. All the test results and findings obtained in our study can provide valuable information on reducing traffic accidents.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.1009-1017
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• 2022

Log data has been used as a basis in understanding and deciding the main functions and state of information systems. It has also been used as an important input for the various applications in cybersecurity. It is an essential part to get necessary information from log data, to make a decision with the information, and to take a suitable countermeasure according to the information for protecting and operating systems in stability and reliability, but due to the explosive increase of various types and amounts of log, it is quite challenging to effectively and efficiently deal with the problem using existing tools. Therefore, this study has suggested a multiclass classification of the security severity level of multi-source event log using machine learning based on natural language processing. The experimental results with the training and test samples of 472,972 show that our approach has archived the accuracy of 99.59%.

• Tuberculosis and Respiratory Diseases
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• v.49 no.5
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• pp.585-593
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• 2000

Background : International consensus guidelines have recently been developed to improve the assessment and management of asthma. One of the major recommendation of these guidelines is that asthma severity should be assessed through the recognition of key symptoms, such as nocturnal waking, medication requirements, and objective measurements of lung function. Differential classification of asthma severity would lead to major differences in both long term pharmacological management and the treatment of severe exacerbation. Methods : This study examined the relationship between the symptom score and measurements of $FEV_1$ and PEF when expressed as a percentage of predicted values in asthmatics (n=107). Results : The correlation of $FEV_1$ % with PEFR% was highly significant (r=0.83, p<0.01). However, there was agreement in terms of the classification of asthma severity in 76.6% of the paired measurements of $FEV_1$ % and PEFR%. Agreement in the classification of asthma severity was also found in 57.1% of the paired analysis of $FEV_1$ % and symptom score. 39% of the patients classified as having moderate asthma on the basis of $FEV_1$ % recording would be considered to have severe asthma if symptom score alone were used. Low baseline $FEV_1$ and high bronchial responsiveness were associated with a low degree of perception of airway obstruction. Conclusion : The relationships between the symptom score, PEFR and $FEV_1$ were generally poor. When assessing asthma severity, age, duration, $PC_{20}$, and baseline $FEV_1$ should be considered.