• Quality Improvement in Health Care
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• v.9 no.1
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• pp.52-64
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• 2002

Background : Coding of principal diagnosis is essential component for producing reliable health statistics. We performed this study to evaluate the current practice of principal diagnoses determination and coding, and to give some basic data to improve coding of principal diagnosis. Method : Nineteen medical record administrators (MRAs) of 3 university hospitals participated in coding principal Dx. from August 1, 2001 to August 31, 2001. From each hospital, 10 medical records of patients with high frequency disease were selected randomly. Each 10 medical records were grouped into three (A. B, C). Then, these 30 medical records were given to each MRAs for coding. At the same time questionnaire was given to each of them. Questions were to prove how they decide and code the principal diagnosis among many current diagnoses; how they decide and code the principal diagnosis when they see irrelevant diagnosis recorded as the principal diagnosis in medical record, when only tentative diagnoses were recorded without final diagnosis, and when different diagnoses were recorded in different sheets of same record. Agreement of coding among 3 hospitals were compared and survey results were analysed with SAS 6.12. Results : Agreement of coding was found in medical records 5-6 of each 10 medical records. Causes of disagreement were as follows. Difference of clinician's opinion from each hospital; mixed use of guideline from KCD-3 and guideline from DRG; difference in 4th digit classification according to the absence of pathology report in the medical record; difference of abbreviations among hospitals. 57.9% of MRAs selected the principal diagnosis recorded by physician, 42.1% of MRAs decided principal diagnosis after consulting to KCD-3 guideline. When there were difficulties in determining the principal diagnosis, 42.1% of MRAs decided principal diagnosis after discussion with the physician, 26.3% after discussion with fellow MRAs. Conclusion : There were differences in codings among hospitals. To minimize the difference, we suggest the development of disease-specific guidelines for coding in addition to the current general guideline such as KCD-3. To do this, Coding Clinic which can produce guidelines is needed.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.134-143
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• 2017

Machine fault diagnosis recovers all the studies that aim to detect automatically faults or damages on machines. Generally, it is very difficult to diagnose a machine fault by conventional methods based on mathematical models because of the complexity of the real world systems and the obvious existence of nonlinear factors. This study develops an automatic machine fault diagnosis system that uses pattern recognition techniques such as principal component analysis (PCA) and artificial neural networks (ANN). The sounds emitted by the operating machine, a drill in this case, are obtained and analyzed for the different operating conditions. The specific machine conditions considered in this research are the undamaged drill and the defected drill with wear. Principal component analysis is first used to reduce the dimensionality of the original sound data. The first principal components are then used as the inputs of a neural network based classifier to separate normal and defected drill sound data. The results show that the proposed PCA-ANN method can be used for the sounds based automated diagnosis system.

• Quality Improvement in Health Care
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• v.15 no.1
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• pp.123-133
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• 2009

Background : The principal diagnosis has been used in many different fields such as hospital statistics, medical research, insurance claim, national health statistics and so on. Some principal diagnoses have a relatively low level of reliability in the medium-sized hospitals. The purpose of this study is to identify the reliability level of principal diagnoses and to suggest ways to improve reliability of the principal diagnosis. Method : Data were collected from a medium-sized hospital located in Pusan. The discharge summaries on 323 patients who were discharged in January, 2008 and the outpatient summaries on 251 patients who visited the hospital on March 28, 2008 were collected, and descriptive analysis was performed using SPSS version 12.0K. Result : The findings are the followings: (1) the diagnostic consistency rate between medical records and doctors' was 92.0%; (2) the diagnostic consistency rate between medical records and insurance claims was 86.1%; (3) the diagnostic consistency rate between doctors' diagnoses and insurance claims was 80.2%. The evidence seems to indicate that some principal diagnoses have reliability problems in the medium-sized hospitals. Conclusion : The results of this study suggest the followings: (1) employees should be trained and supervision of hospital activities are needed; (2) network systems should be constructed for each department; (3) professions need to be fostered (4) doctors' awareness of medical records should be changed.

• Journal of the Korean Society of Safety
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• v.29 no.4
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• pp.73-77
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• 2014

In the field of fault diagnosis, the deviations from normal operating conditions are monitored to identify the type of faults and find their root causes. One of the most representative methods is the statistical approaches, due to a large amount of advantages. However, ambiguous diagnosis results can be generated according to fault magnitudes, even if the same fault occurs. To tackle this issue, this work proposes principal component analysis (PCA) based method with qualitative information. The PCA model is constructed under normal operation data and the residuals from faulty conditions are calculated. The significant changes of these residuals are recorded to make the information for identifying the types of fault. This model can be employed easily and the tasks for building are smaller than these of other common approaches. The efficacy of the proposed model is illustrated in Tennessee Eastman process.

• Journal of the Korean Society of Safety
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• v.31 no.4
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• pp.35-41
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• 2016

In order to guarantee the process safety and prevent accidents, the deviations from normal operating conditions should be monitored and their root causes have to be identified as soon as possible. The statistical theories-based method among various fault diagnosis methods has been gaining popularity, due to simplicity and quickness. However, according to fault magnitudes, the scalar value generated by statistical methods can be changed and this point can lead to produce wrong information. To solve this difficulty, this work employs PCA (Principal Component Analysis) based method with qualitative information. In the case study of our previous study, the number of assumed faults is much smaller than that of process variables. In the case study of this study, the number of predefined faults is 19, while that of process variables is 6. It means that a fault diagnosis becomes more difficult and it is really hard to isolate a single fault with a small number of variables. The PCA model is constructed under normal operation data in order to get a loading vector and the data set of assumed faulty conditions is applied with PCA model. The significant changes on PC (Principal Components) axes are monitored with CUSUM (Cumulative Sum Control Chart) and recorded to make the information, which can be used to identify the types of fault.

• Korean Chemical Engineering Research
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• v.60 no.2
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• pp.223-228
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• 2022

In chemical processes, unintended faults can make serious accidents. To tackle them, proper fault diagnosis models should be designed to identify the root cause of faults. To design a fault diagnosis model, a process and its data should be analyzed. However, most previous researches in the field of fault diagnosis just handle the data set of benchmark processes simulated on commercial programs. It indicates that it is really hard to get fresh data sets on real processes. In this study, real faulty conditions of an industrial polystyrene process are tested. In this process, a runaway reaction occurred and this caused a large loss since operators were late aware of the occurrence of this accident. To design a proper fault diagnosis model, we analyzed this process and a real accident data set. At first, a mode classification model based on support vector machine (SVM) was trained and principal component analysis (PCA) model for each mode was constructed under normal operation conditions. The results show that a proposed model can quickly diagnose the occurrence of a fault and they indicate that this model is able to reduce the potential loss.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.645-648
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• 2003

Induction motors are a critical component of industrial processes. Sudden failures of such machines can cause the heavy economical losses and the deterioration of system reliability. Based on the reliability and cost competitiveness of driving system (motors), the faults detection and the diagnosis of system are considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis (MCSA) method are emphasized. In this paper, MCSA method is used for induction motor fault diagnosis. This method analyses the motor's supply current. since this diagnoses faults of the motor. The diagnostic algorithm is based on the principal component analysis(PCA), and the diagnosis system is programmed by using LabVIEW and MATLAB.

• Asian-Australasian Journal of Animal Sciences
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• v.4 no.3
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• pp.245-250
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• 1991

Analysis of the characteristics and the grouping of the species of sown and weeds in artificial pastures was studied applying the principal component analysis method. Presency and coverage of six sown species and fifteen weed species which occurred in pastures of under-grazing and optimumgrazing were subject to analysis. From field survey, species were divided into three groups: the group A included five species such as Festuca arundinacea, Lolium perenne and Dactylis glomerata, etc., the group B included eleven species such as Polygonum longisetum, Agrostis alba and Rumex obtusifolius, etc., and the group C included five species such as Miscanthus sinensis, Rubus palmatus and Artemisia princeps, etc. The group A species corresponded to good pasture conditions and management. On the contrary, the group C species occurred in poor pasture conditions with inadequate management. The group B species corresponded to intermediate pasture conditions and management. Interrelated pair species co-existing and species non-co-existing were discovered. Factor loading as negative for the group A species. positive for the group C species and positive but lower than the group C species for the group B species. From these results it is concluded that the principal component analysis seems to one of the useful tools for the analysis of characteristics of species and the diagnosis of sown pasture vegetation, although further studies are required to get more general information about species characteristics.

• Proceedings of the KSR Conference
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• 2003.10c
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• pp.529-534
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• 2003

Within industry induction motors have a broad application area to drive pumps, fans, elevators and electric trains. Sudden failures of such machines can cause the heavy economical losses and the deterioration of system reliability. Based on the reliability and cost competitiveness of driving system (motors), the faults detection and the diagnosis of system are considered very important factors. In order to perform the faults detection and diagnosis of motors, the vibration monitoring method and motor current signature analysis (MCSA) method are emphasized. In this paper, MCSA method are used for induction motor fault diagnosis. This method analyzes the motor's supply current, since this diagnoses faults of the motor. The diagnostic algorithm is based on the principal component analysis(PCA), and the diagnosis system is programmed by using LabVIEW and MATLAB.

• Quality Improvement in Health Care
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• v.5 no.2
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• pp.216-223
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• 1998

We surveyed the discordance rate of principal diagnosis made at emergency room(ER) & made at ward on discharge of the patients. Subjects were four hundred eighty cases who came to the ER of one third-line hospital from January 1, 1998 to January 31, 1998. The discordance rate was higher in patients admitted to medical department(8.2%) than surgical department(1.5%). If the patients were transferred to other department during hospital stay, discordance rate increased from 3.3% to 6.3%. In conclusion, discordance rate of principal diagnosis made at ER and made at ward was higher in patients with complicated problems. Medical record department should keep these findings in mind if it has a plan to support the management of ER record.