• Tuberculosis and Respiratory Diseases
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• v.81 no.3
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• pp.247-255
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• 2018

Background: Patients with acute respiratory failure secondary to tuberculous destroyed lung (TDL) have a poor prognosis. The aim of the present retrospective study was to develop a mortality prediction model for TDL patients who require mechanical ventilation. Methods: Data from consecutive TDL patients who had received mechanical ventilation at a single university-affiliated tertiary care hospital in Korea were reviewed. Binary logistic regression was used to identify factors predicting intensive care unit (ICU) mortality. A TDL on mechanical Ventilation (TDL-Vent) score was calculated by assigning points to variables according to ${\beta}$ coefficient values. Results: Data from 125 patients were reviewed. A total of 36 patients (29%) died during ICU admission. On the basis of multivariate analysis, the following factors were included in the TDL-Vent score: age ${\geq}65$ years, vasopressor use, and arterial partial pressure of oxygen/fraction of inspired oxygen ratio <180. In a second regression model, a modified score was then calculated by adding brain natriuretic peptide. For TDL-Vent scores 0 to 3, the 60-day mortality rates were 11%, 27%, 30%, and 77%, respectively (p<0.001). For modified TDL-Vent scores 0 to ${\geq}3$, the 60-day mortality rates were 0%, 21%, 33%, and 57%, respectively (p=0.001). For both the TDL-Vent score and the modified TDL-Vent score, the areas under the receiver operating characteristic curve were larger than that of other illness severity scores. Conclusion: The TDL-Vent model identifies TDL patients on mechanical ventilation with a high risk of mortality. Prospective validation studies in larger cohorts are now warranted.

• Childhood Kidney Diseases
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• v.15 no.2
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• pp.116-124
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• 2011

Acute kidney injury (AKI) can result in mortality or progress to chronic kidney disease in hospitalized patients. Although serum creatinine has long been used as the best biomarker for diagnosis of AKI, it has some clinical limitations, especially in children. New biomarkers are needed for early diagnosis, differential diagnosis, and reliable prediction of prognosis in AKI. Up to the present, candidate AKI biomarkers include neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), interleukin-18 (IL-18), livertype fatty acid-binding protein (L-FABP), matrix metalloproteinase-9 (MMP-9), and Nacetyl-$\ss$-D-glucosaminidase (NAG). However, whether these are superior to serum creatinine in the confirmation of diagnosis and prediction of prognosis in AKI is unclear. Further studies are needed for clinical application of these new biomarkers in AKI.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.77-88
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• 2021

Heart Failure represents a critical pathological case that is challenging to predict and discover at an early age, with a notable increase in morbidity and mortality. Machine Learning and Neural Network techniques play a crucial role in predicting heart attacks, diseases and more. These techniques give valuable perspectives for clinicians who may then adjust their diagnosis for each individual patient. This paper evaluated neural network models for heart attacks predictions. Several online learning methods were investigated to automatically and accurately predict heart attacks. The UCI dataset was used in this work to train and evaluate First Order and Second Order Online Learning methods; namely Backpropagation, Delta bar Delta, Levenberg Marquardt and QuickProp learning methods. An optimizer technique was also used to minimize the random noise in the database. A regularization concept was employed to further improve the generalization of the model. Results show that a three layers' NN model with a Backpropagation algorithm and Nadam optimizer achieved a promising accuracy for the heart attach prediction tasks.

• Genomics & Informatics
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• v.21 no.2
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• pp.25.1-25.12
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• 2023

Adaptation of infections and hosts has resulted in several metabolic mechanisms adopted by intracellular pathogens to combat the defense responses and the lack of fuel during infection. Human tuberculosis caused by Mycobacterium tuberculosis (MTB) is the world's first cause of mortality tied to a single disease. This study aims to characterize and anticipate potential antigen characteristics for promising vaccine candidates for the hypothetical protein of MTB through computational strategies. The protein is associated with the catalyzation of dithiol oxidation and/or disulfide reduction because of the protein's anticipated disulfide oxidoreductase properties. This investigation analyzed the protein's physicochemical characteristics, protein-protein interactions, subcellular locations, anticipated active sites, secondary and tertiary structures, allergenicity, antigenicity, and toxicity properties. The protein has significant active amino acid residues with no allergenicity, elevated antigenicity, and no toxicity.

• Physical Therapy Korea
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• v.17 no.3
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• pp.59-67
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• 2010

Mortality rates from stroke have been declining. Because of this, more people are living with residual disability. Rehabilitation plays an important role in functional recovery of stroke survivors. In stroke rehabilitation, early prediction of the obtainable level of functional recovery is desirable to deliver efficient care, set realistic goals, and provide appropriate discharge planning. The purpose of this study was to identify predictors of functional outcome after stroke using inpatient rehabilitation as measured by Functional Independence Measure (FIM) total scores. Correlation and stepwise multiple regression analyses were performed on data collected retrospectively from two-hundred thirty-five patients. More than moderate correlation was found between FIM total scores at the time of hospital admission and FIM total scores at the time of discharge from the hospital. Significant predictors of FIM at the time of discharge were FIM total scores at the time of hospital admission, age, and onset-admission interval. The equation was as follows: expected discharge FIM total score = $76.12+.62{\times}$(admission FIM total score)-$.38{\times}(age)-.15{\times}$(onset-admission interval). These findings suggest that FIM total scores at the time of hospital admission, age, and onset-admission interval are important determinants of functional outcome.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.417-419
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• 2023

Hypertension is one of the leading causes of mortality worldwide. In recent years, the incidence of hypertension has increased dramatically, not only among the elderly but also among young people. In this regard, the use of machine-learning methods to diagnose the causes of hypertension has increased in recent years. In this study, we improved the prediction of hypertension detection using Mahalanobis distance-based multivariate outlier removal using the KNHANES database from the Korean national health data and the COVID-19 dataset from Kaggle. This study was divided into two modules. Initially, the data preprocessing step used merged datasets and decision-tree classifier-based feature selection. The next module applies a predictive analysis step to remove multivariate outliers using the Mahalanobis distance from the experimental dataset and makes a prediction of hypertension. In this study, we compared the accuracy of each classification model. The best results showed that the proposed MAH_RF algorithm had an accuracy of 82.66%. The proposed method can be used not only for hypertension but also for the detection of various diseases such as stroke and cardiovascular disease.

• Proceedings of the KAIS Fall Conference
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• 2012.05a
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• pp.22-24
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• 2012

The tools that classify the severity of patients based on the prediction of mortality include APACHE, SAPS, and MPM. Theses tools rely crucially on the evaluation of patients' general clinical status on the first date of their admission to ICU. Nursing activities are one of the most crucial factors influencing on the quality of treatment that patients receive and one of the contributing factors for their prognosis and safety. The purpose of this study was to identify the goodness-of-fit of CPSCS of critical patient severity classification system(CPSCS) and Glasgow coma scale(GCS) and the clinical usefulness of its death rate prediction. Data were collected from the medical records of 187 neurological patients who were admitted to the ICU of C University Hospital. The data were analyzed through $x^2$ test, t-test, Mann-Whitney, Kruskal-Wallis, goodness-of-fit test, and ROC curve. In accordance with patients' general and clinical characteristics, patient mortality turned out to be statistically different depending on ICU stay, endotracheal intubation, central venous catheter, and severity by CPSCS. Homer-Lemeshow goodness-of-fit tests were CPSCS and GCS and the results of the discrimination test using the ROC curve were $CPSCS_0$, .734, $GCS_0$,.583, $CPSCS_{24}$,.734, $GCS_{24}$, .612, $CPSCS_{48}$,.591, $GCS_{48}$,.646, $CPSCS_{72}$,.622, and $GCS_{72}$,.623. Logistic regression analysis showed that each point on the CPSCS score signifies1.034 higher likelihood of dying. Applied to neurologically ill patients, early CPSCS scores can be regarded as a useful tool.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2012.04a
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• pp.190-193
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• 2012

The tools that classify the severity of patients based on the prediction of mortality include APACHE, SAPS, and MPM. Theses tools rely crucially on the evaluation of patients' general clinical status on the first date of their admission to ICU. Nursing activities are one of the most crucial factors influencing on the quality of treatment that patients receive and one of the contributing factors for their prognosis and safety. The purpose of this study was to identify the goodness-of-fit of CPSCS of critical patient severity classification system(CPSCS) and Glasgow coma scale(GCS) and the clinical usefulness of its death rate prediction. Data were collected from the medical records of 187 neurological patients who were admitted to the ICU of C University Hospital. The data were analyzed through $x^2$ test, t-test, Mann-Whitney, Kruskal-Wallis, goodness-of-fit test, and ROC curve. In accordance with patients' general and clinical characteristics, patient mortality turned out to be statistically different depending on ICU stay, endotracheal intubation, central venous catheter, and severity by CPSCS. Homer-Lemeshow goodness-of-fit tests were CPSCS and GCS and the results of the discrimination test using the ROC curve were $CPSCS_0$,.734, $GCS_0$,.583, $CPSCS_{24}$,.734, $GCS_{24}$,.612, $CPSCS_{48}$,.591, $GCS_{48}$,.646, $CPSCS_{72}$,.622, and $GCS_{72}$,.623. Logistic regression analysis showed that each point on the CPSCS score signifies1.034 higher likelihood of dying. Applied to neurologically ill patients, early CPSCS scores can be regarded as a useful tool.

• Health Policy and Management
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• v.26 no.4
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• pp.359-372
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• 2016

Background: The purpose of this study was to develop risk-adjustment models for acute stroke mortality that were based on data from Health Insurance Review and Assessment Service (HIRA) dataset and to evaluate the validity of these models for comparing hospital performance. Methods: We identified prognostic factors of acute stroke mortality through literature review. On the basis of the avaliable data, the following factors was included in risk adjustment models: age, sex, stroke subtype, stroke severity, and comorbid conditions. Survey data in 2014 was used for development and 2012 dataset was analysed for validation. Prediction models of acute stroke mortality by stroke type were developed using logistic regression. Model performance was evaluated using C-statistics, $R^2$ values, and Hosmer-Lemeshow goodness-of-fit statistics. Results: We excluded some of the clinical factors such as mental status, vital sign, and lab finding from risk adjustment model because there is no avaliable data. The ischemic stroke model with age, sex, and stroke severity (categorical) showed good performance (C-statistic=0.881, Hosmer-Lemeshow test p=0.371). The hemorrhagic stroke model with age, sex, stroke subtype, and stroke severity (categorical) also showed good performance (C-statistic=0.867, Hosmer-Lemeshow test p=0.850). Conclusion: Among risk adjustment models we recommend the model including age, sex, stroke severity, and stroke subtype for HIRA assessment. However, this model may be inappropriate for comparing hospital performance due to several methodological weaknesses such as lack of clinical information, variations across hospitals in the coding of comorbidities, inability to discriminate between comorbidity and complication, missing of stroke severity, and small case number of hospitals. Therefore, further studies are needed to enhance the validity of the risk adjustment model of acute stroke mortality.

• Journal of Korean Society of Environmental Engineers
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• v.27 no.1
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• pp.67-74
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• 2005

Toxicity tests were performed to evaluate the feasibility of application with prediction models to 10 mixture chemicals (chloroneb, butylbenzylphthalate, pendimethaline, di-n-butylphthalate, di-iso-butylphthalate, diazinon, isofenphos, 2-chlorophenol, 2,4,6-trichlorophenol and p-octylphenol) detected in effluents from wastewater treatment plants (WWTPs). Ten chemicals were selected in the basis of their toxicities to Daphnia magna and the concentrations in effluents measured by GC/MS. Three models including concentration addition (CA), independent action (IA) and effect summation (ES) were employed for the comparison of the predicted and the observed mortality of D. magna exposed to 10 mixture chemicals for 48 hours. With a comparative study it was ineffective to predict the mortality through the CA and the ES prediction model, while the IA prediction model showed a high correlation($r^2\;=\;0.85$). Moreover, the ES model over-estimated the toxicity observed by bioassay experiments about five-fold. Consequently, IA model is a reasonable tool to predict the mixture toxicity of the discharging water from WWTPs.