• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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• pp.57-61
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• 2001

A conceptional framework is proposed in which a general feedback predictive controller is taken to be a feedback interconnection of controller and GPC (General predictive Control). Numerical example are included to illustrate the procedure and to show the performance of the control system.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.19 no.4
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• pp.444-452
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• 2012

Purpose: To explore the usefulness of previous fall history as a triage variable for inpatients. Methods: Medical records of 21,382 patients, admitted to medical units of one tertiary hospital, were analyzed retrospectively. Inpatient falls were identified from the hospital's self-report system. Non-falls in 1,125 patients were selected by a stratified matching sampling with 125 patients with falls (0.59%). A comparative and predictive accuracy analysis was conducted to describe differences between the two groups with and without a history of falls. Logistic regression was used to measure the effect size of the fall history. Results: The fall history group showed higher prevalence by 9 fold than the non-fall history group. The relationships between falls and relevant variables which were significant in the non-fall history group, were not significant for the fall history group. Falls in the fall history group were 25 times more likely than in the non-fall group. Predictive accuracy of the risk assessment tool showed almost zero specificity in the fall history group. Conclusion: The presence of fall history, the fall prevalence, variables relevant to falls, and the accuracy of the risk tool were different, which support the usefulness of the fall history as a triage variable.

• Korean Journal of Adult Nursing
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• v.27 no.5
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• pp.559-571
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• 2015

Purpose: This study is to determine the predictive validity of the St. Thomas Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY) for inpatients' fall risk. Methods: A literature search was performed to identify all studies published between 1946 and 2014 from periodicals indexed in Ovid Medline, Embase, CINAHL, KoreaMed, NDSL and other databases, using the following key words; 'fall', 'fall risk assessment', 'fall screening', 'mobility scale', and 'risk assessment tool'. The QUADAS-II was applied to assess the internal validity of the diagnostic studies. Fourteen studies were analyzed using meta-analysis with MetaDisc 1.4. Results: The predictive validity of STRATIFY was as follows; pooled sensitivity .75 (95% CI: 0.72~0.78), pooled specificity .69 (95% CI: 0.69~0.70) respectively. In addition, the pooled sensitivity in the study that targets only the over 65 years of age was .89 (95% CI: 0.85~0.93). Conclusion: The STRATIFY's predictive validity for fall risk is at a moderate level. Although there is a limit to interpret the results for heterogeneity between the literature, STRATIFY is an appropriate tool to apply to hospitalized patients of the elderly at a potential risk of accidental fall in a hospital.

• Quality Improvement in Health Care
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• v.22 no.2
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• pp.27-40
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• 2016

Purpose: Globally, falls are a major public health problem. The study aimed to evaluate the predictive validity of the Timed Up and Go test (TUGT) as a screening tool for fall risk. Methods: An electronic search was performed Medline, EMBASE, CINAHL, Cochran Library, KoreaMed and the National Digital Science Library and other databases, using the following keywords: 'fall', 'fall risk assessment', 'fall screening', 'mobility scale', and 'risk assessment tool'. The QUADAS-II was applied to assess the internal validity of the diagnostic studies. Thirteen studies were analyzed using meta-analysis with MetaDisc 1.4. Results: The selected 13 studies reporting predictive validity of TUGT of fall risks were meta-analyzed with a sample size of 1004 with high methodological quality. Overall predictive validity of TGUT was as follows. The pooled sensitivity 0.72 (95% confidence interval [CI]: 0.67-0.77), pooled specificity 0.58 (95% CI: 0.54-0.63) and sROC AUC was 0.75 respectively. Heterogeneity among studies was a moderate level in sensitivity. Conclusion: The TGUT's predictive validity for fall risk is at a moderate level. Although there is a limit to interpret the results for heterogeneity between the literature, TGUT is an appropriate tool to apply to all patients at a potential risk of accidental fall in a hospital or long-term care facility.

• Journal of Korean Clinical Nursing Research
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• v.28 no.3
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• pp.270-276
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• 2022

Purpose: The purpose of this study was to validate the Edmonson psychiatric fall risk assessment tool (EPFRAT) for psychiatric inpatients. Methods: Data from retrospective study were collected from 670 adult inpatients in two departments of mental health medicine of a tertiary general hospital by reviewing their electronic medical records. There were 41 patients who experienced falls and 629 patients who did not experience falls during the period from January to December 2019. Data were analyzed by sensitivity, specificity, positive predictive value, negative predictive value, and a receiver-operating characteristic curve (ROC) for validity assessment using the IBM SPSS/WIN 26.0 program. Results: Factors affecting falls were the participant's age, guardian's residence, high-risk determination at the time of admission, and comorbidity. At the 85 points where the point of sum of the sensitivity and specificity was largest, the sensitivity, specificity, positive predictive value, and negative predictive value of EPFRAT were 92.7%, 79.7%, 22.9%, and 99.4%, respectively. The area under the ROC to assess the overall validity of the tool was .92 (95% CI 0.89~0.94). Conclusion: The EPFRAT was proved to be valid and reasonable for predicting falls in psychiatric inpatients. Based on the results of this study, it could be used for the assessment of high-risk patients for falls in psychiatric units.

• Communications for Statistical Applications and Methods
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• v.30 no.5
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• pp.437-451
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• 2023

Generalized linear models and generalized linear mixed models (GLMMs) are fundamental tools for predictive analyses. In insurance, GLMMs are particularly important, because they provide not only a tool for prediction but also a theoretical justification for setting premiums. Although thousands of resources are available for introducing GLMMs as a classical and fundamental tool in statistical analysis, few resources seem to be available for the insurance industry. This study targets insurance professionals already familiar with basic actuarial mathematics and explains GLMMs and their linkage with classical actuarial pricing tools, such as the Buhlmann premium method. Focus of the study is mainly on the modeling aspect of GLMMs and their application to pricing, while avoiding technical issues related to statistical estimation, which can be automatically handled by most statistical software.

• ICROS
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• v.2 no.1
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• pp.59-71
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• 1996

In the last decade, the model predictive control methods have enjoyed many industrial applications with successful results. Although the general predictive control methods for nonlinear chemical processes are not yet formulated, the promising features of the model predictive control methods attract attentions of many researchers who are involved with difficult but important nonlinear process control problems. Recently, the class of bilinear model has been introduced as an useful tool for examining many nonlinear phenomena. Since their structural properties are similar to those of linear models, it is not difficult to develop a robust adaptive model predictive control method based on bilinear model. We expect that the model predictive control method based on bilinear model will expand its region in the world of nonlinear systems.

• Journal of Korean Academy of Nursing
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• v.33 no.2
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• pp.162-169
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• 2003

Purpose: This study was to compare the predictive validity of Norton Scale(1962), Cubbin & Jackson Scale(1991), and Song & Choi Scale(1991). Method: Data were collected three times per week from 48~72hours after admission based on the four pressure sore risk assessment scales and a skin assessment tool for pressure sore on 112 intensive care unit(ICU) patients in a educational hospital Ulsan during Dec, 11, 2000 to Feb, 10, 2001. Four indices of validity and area under the curve(AUC) of receiver operating characteristic(ROC) were calculated. Result: Based on the cut off point presented by the developer, sensitivity, specificity, positive predictive value, negative predictive value were as follows : Norton Scale : 97%, 18%, 35%, 93% respectively; Cubbin & Jackson Scale : 89%, 61%, 51%, 92%, respectively; and Song & Choi Scale : 100%, 18%, 36%, 100% respectively. Area under the curves(AUC) of receiver operating characteristic(ROC) were Norton Scale .737, Cubbin & Jackson Scale .826, Song & Choi Scale .683. Conclusion: The Cubbin & Jackson Scale was found to be the most valid pressure sore risk assessment tool. Further studies on patients with chronic conditions may be helpful to validate this finding.

• Clinical and Experimental Pediatrics
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• v.63 no.3
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• pp.102-103
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• 2020

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.215-225
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• 2024

Predictive maintenance has been considered fundamental in the industrial applications in the last few years. It contributes to improve reliability, availability, and maintainability of the systems and to avoid breakdowns. These breakdowns could potentially lead to system shutdowns and to decrease the production efficiency of the manufacturing plants. The present article aims to study how predictive maintenance could be planed into the production scheduling, through a systematic review of literature. . The review includes the research articles published in international journals indexed in the Scopus database. 165 research articles were included in the search using #predictive maintenance# AND #production scheduling#. Press articles, conference and non-English papers are not considered in this study. After careful evaluation of each study for its purpose and scope, 50 research articles are selected for this review by following the 2020 Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA) statement. A benchmarking of predictive maintenance methods was used to understand the parameters that contributed to improve the production scheduling. The results of the comparative analysis highlight that artificial intelligence is a promising tool to anticipate breakdowns. An additional impression of this study is that each equipment has its own parameters that have to be collected, monitored and analyzed.