• Communications for Statistical Applications and Methods
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• v.25 no.3
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• pp.307-319
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• 2018

High-throughput technologies enable the simultaneous evaluation of thousands of genes that could discriminate different subclasses of complex diseases. Ranking genes according to differential expression is an important screening step for follow-up analysis. Many statistical measures have been proposed for this purpose. A good ranked list should provide a stable rank (at least for top-ranked gene), and the top ranked genes should have a high power in differentiating different disease status. However, there is a lack of emphasis in the literature on ranking genes based on these two criteria simultaneously. To achieve the above two criteria simultaneously, we proposed to apply a previously reported metric, the modified area under the receiver operating characteristic cure, to gene ranking. The proposed ranking method is found to be promising in leading to a stable ranking list and good prediction performances of top ranked genes. The findings are illustrated through studies on both synthesized data and real microarray gene expression data. The proposed method is recommended for ranking genes or other biomarkers for high-dimensional omics studies.

• Journal of Trauma and Injury
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• v.18 no.2
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• pp.141-147
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• 2005

Purpose: Recently, The new Injury Severity Score (NISS) has become a more accurate predictor of mortality than the traditional Injury Severity Score (ISS) in the trauma population. Trauma Score Injury Severity Score (TRISS) method, regarded as the gold standard for mortality prediction in trauma patients, still contains the ISS as an essential factor within its formula. The purpose of this study was to determine whether a simple modification of the TRISS by replacing the ISS with the NISS would improve the prediction of in-hospital mortality in a trauma population with blunt head and neck trauma. Objects and Methods: The study population consisted of 641 patients from a regional emergency medical center in Kyoungsangnam-do. Demographic data, clinical information, the final diagnosis, and the outcome for each patient were collected in a retrospective manner. the ISS, NISS, TRISS, and modified TRISS were calculated for each patients. The discrimination and the calibration of the ISS, NISS, modified TRISS and conventional TRISS models were compared using receiver operator characteristic (ROC) curves, areas under the ROC curve (AUC) and Hosmer-Lemeshow statistics. Results: The AUC of the ISS, NISS, modified TRISS, and conventional TRISS were 0.885, 0.941, 0.971, and 0.918 respectively. Statistical differences were found between the ISS and the NISS (p=0.008) and between the modified TRISS and the conventional TRISS (p=0.009). Hosmer-Lemeshow chi square values were 13.2, 2.3, 50.1, and 13.8, respectively; only the conventional TRISS failed to achieve the level of and an excellent calibration model (p<0.001). Conclusion: The modified TRISS is a more accurate predictor of in-hospital mortality than the conventional TRISS in a trauma population of blunt head and neck trauma.

• Dementia and Neurocognitive Disorders
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• v.23 no.4
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• pp.236-244
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• 2024

Background and Purpose: Since the onset of the coronavirus disease 2019 pandemic, the Telephone-Montreal Cognitive Assessment (T-MoCA) has gained popularity as a remote cognitive screening tool. T-MoCA includes items from the original MoCA (MoCA-30), excluding those requiring visual stimuli, resulting in a maximum score of 22 points. This study aimed to assess whether the T-MoCA items (MoCA-22) demonstrate comparable discriminatory power to MoCA-30 and Mini-Mental State Examination (MMSE) in screening for mild cognitive impairment (MCI) and dementia. Methods: Participants included 233 cognitively normal (CN) individuals, 175 with MCI, and 166 with dementia. All completed the Korean-MoCA-30 (K-MoCA-30) and Korean-MMSE (K-MMSE), with the Korean-MoCA-22 (K-MoCA-22) scores derived from the K-MoCA-30 responses. A receiver operating characteristic (ROC) curve analysis was conducted. Results: K-MoCA-22 showed a strong correlation with K-MoCA-30 and a moderate correlation with K-MMSE. Scores decreased progressively from CN to MCI and dementia, with significant differences between groups, consistent with K-MoCA-30 and K-MMSE. The study also explored modified K-MoCA-22 index scores across 5 cognitive domains. ROC curve analysis revealed that the area under the curve (AUC) for K-MoCA-22 was significantly smaller than that for K-MoCA-30 in distinguishing both MCI and dementia from CN. However, no significant difference in AUC was found between K-MoCA-22 and K-MMSE, indicating similar discriminatory power. Additionally, the discriminability of K-MoCA-22 varied by education level. Conclusions: These results indicate that K-MoCA-22, although slightly less effective than K-MoCA-30, still shows good to excellent discriminatory power and is comparable to K-MMSE in screening for MCI and dementia.

• Journal of Biomedical Engineering Research
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• v.38 no.1
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• pp.25-31
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• 2017

Testing TB in chest X-ray images is a typical method to diagnose presence and magnitude of PTB lesion. However, the method has limitation due to inter-reader variability. Therefore, it is essential to overcome this drawback with automatic interpretation. In this study, we propose a novel method for detection of PTB using SegNet, which is a deep learning architecture for semantic pixel wise image labelling. SegNet is composed of a stack of encoders followed by a corresponding decoder stack which feeds into a soft-max classification layer. We modified parameters of SegNet to change the number of classes from 12 to 2 (TB or none-TB) and applied the architecture to automatically interpret chest radiographs. 552 chest X-ray images, provided by The Korean Institute of Tuberculosis, used for training and test and we constructed a receiver operating characteristic (ROC) curve. As a consequence, the area under the curve (AUC) was 90.4% (95% CI:[85.1, 95.7]) with a classification accuracy of 84.3%. A sensitivity was 85.7% and specificity was 82.8% on 431 training images (TB 172, none-TB 259) and 121 test images (TB 63, none-TB 58). This results show that detecting PTB using SegNet is comparable to other PTB detection methods.

• Journal of Veterinary Clinics
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• v.40 no.1
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• pp.31-37
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• 2023

Vertebral left atrial size (VLAS) is an important indicator to predict myxomatous mitral valve degeneration (MMVD) in dogs. When the caudal margin of cardiac silhouette and the dorsal margin of caudal vena cava (CdVC) could not be seen exactly, another way to evaluate VLAS is needed. The objective of this study was to assess whether a new modified VLAS (m-VLAS) could be used as an indicator to predict MMVD in 57 small breed dogs with MMVD. The m-VLAS was also used to classify American College of Veterinary Internal Medicine staging groups and left heart enlargement confirmed with echocardiograph (EchoLHE) groups. The m-VLAS was measured as the distance from the ventral aspect of the carina to the dorsal aspect of the intersection of the cardiac silhouette and the farthest LA caudal margin, not the CdVC, followed by drawing the same line beginning at the cranial edge of T4. Based on VLAS values and m-VLAS values measured for dogs with MMVD, correlations between these values and left heart enlargement groups were then evaluated. There were significant differences in both the VLAS and the m-VLAS between EchoLHE groups. The AUC of the ROC curve of the m-VLAS to detect EchoLHE was higher than that of the VLAS. The optimal cutoff value for the m-VLAS was >2.7, which had a higher specificity (86.84%) than the VLAS specificity (71.05%). This study reveals that a new m-VLAS is a more specific indicator than the VLAS for predicting left side heart enlargement in small breed dogs. Therefore, the m-VLAS can be used as a clinically useful radiographic measurement alternative to or better than the VLAS.