Woojae Kim

Differential diagnosis in idiopathic granulomatous mastitis and tuberculous mastitis.

Purpose Idiopathic granulomatous mastitis (IGM) is a rare chronic inflammatory disease of unknown etiology. The diagnosis of IGM requires that other granulomatous lesions in the breast be excluded. Tuberculous mastitis (TM) is also an uncommon disease that is often difficult to differentiate from IGM. The purpose of this study is to develop a new algorithm for the differential diagnosis and treatment of IGM and TM. Methods Medical records of 68 patients (58 with IGM and 10 with TM) between July 1999 and February 2009 were retrospectively reviewed. Results The mean age of the patients was 33.5 (IGM) and 40 (TM) years (p=0.018). The median follow-up was 84 months. Of the total 10 patients with TM, 5 patients had a history of pulmonary tuberculosis. The most common symptoms of the diseases were breast lump and pain. However, axillary lymphadenopathy was more seen in TM (50%) compared to IGM (20.6%) (p=0.048). TM showed more cancer-mimicking findings on radiologic study (p=0.028). In IGM, 48 patients (82.7%) underwent surgical wide excision and 21 patients (36.2%) were managed with corticosteroid therapy and antibiotics. All of the TM patients received anti-tuberculosis medications and 9 patients (90%) underwent wide excision. The mean treatment duration was 2.8 months in IGM and 8.4 months in TM. Recurrence developed in 5 patients (8.6%) in IGM and 1 patient (10%) in TM. Conclusion This study shows different characteristics between IGM and TM. The IGM patients were younger and had more mastalgia symptoms than the TM patients. Axillary lymphadenopathy was seen more often in TM patients. Half of the TM patients had pulmonary tuberculosis or tuberculosis lymphadenitis. Surgical wide excision might be both therapeutic and useful for providing an exact diagnosis.

A novel algorithm for detection of adverse drug reaction signals using a hospital electronic medical record database

Quantitative analytic methods are being increasingly used in postmarketing surveillance. However, currently existing methods are limited to spontaneous reporting data and are inapplicable to hospital electronic medical record (EMR) data. The principal objectives of this study were to propose a novel algorithm for detecting the signals of adverse drug reactions using EMR data focused on laboratory abnormalities after treatment with medication, and to evaluate the potential use of this method as a signal detection tool.

A Comparison of Intensive Care Unit Mortality Prediction Models through the Use of Data Mining Techniques

Objectives The intensive care environment generates a wealth of critical care data suited to developing a well-calibrated prediction tool. This study was done to develop an intensive care unit (ICU) mortality prediction model built on University of Kentucky Hospital (UKH)s data and to assess whether the performance of various data mining techniques, such as the artificial neural network (ANN), support vector machine (SVM) and decision trees (DT), outperform the conventional logistic regression (LR) statistical model. Methods The models were built on ICU data collected regarding 38,474 admissions to the UKH between January 1998 and September 2007. The first 24 hours of the ICU admission data were used, including patient demographics, admission information, physiology data, chronic health items, and outcome information. Results Only 15 study variables were identified as significant for inclusion in the model development. The DT algorithm slightly outperformed (AUC, 0.892) the other data mining techniques, followed by the ANN (AUC, 0.874), and SVM (AUC, 0.876), compared to that of the APACHE III performance (AUC, 0.871). Conclusions With fewer variables needed, the machine learning algorithms that we developed were proven to be as good as the conventional APACHE III prediction.

Effect of sunlight exposure on serum 25-hydroxyvitamin d concentration in women with vitamin d deficiency: using ambulatory lux meter and sunlight exposure questionnaire.

Background Vitamin D is an important factor in human health. Yet, vitamin D deficiency is very common. We aimed to confirm serum 25-hydroxyvitamin D (25OHD) concentration change after sunlight exposure and to elucidate the relationship between the amount of sunlight exposure and serum 25OHD level change by ambulatory lux meter and sunlight exposure questionnaire. Methods Twenty healthy young women were enrolled. They were educated to obtain 20 minutes of sunlight exposure during weekdays from October to November, 2010, during which they were to wear an ambulatory lux meter on an arm. All subjects completed a one-week recall sunlight exposure questionnaire at the end of the study. Before and after sunlight exposure, serum 25OHD level was measured. Results Mean pre-exposure serum 25OHD concentration was 11.01 ng/mL. The mean change of pre- and post-exposure 25OHD level was -0.62 ng/mL, but it was not statistically significant. The mean personal sunlight exposure recorded by ambulatory lux meter, 292.6 lux/s, showed no significant relationship with average change of 25OHD and average weekly sunlight exposure score, 11.9, calculated by the sunlight exposure questionnaire. However, the mean change of serum 25OHD level and weekly sunlight exposure score showed significant negative correlation (r = -0.469, P = 0.037). Conclusion Change of serum 25OHD concentration after four weeks of sunlight exposure was not statistically significant in women with vitamin D deficiency. However, serum 25OHD concentration change was significantly negatively correlated with the sunlight exposure score by the questionnaire.

An Automated Measurement of Ciliary Beating Frequency using a Combined Optical Flow and Peak Detection

Objectives The mucociliary transport system is a major defense mechanism of the respiratory tract. The performance of mucous transportation in the nasal cavity can be represented by a ciliary beating frequency (CBF). This study proposes a novel method to measure CBF by using optical flow. Methods To obtain objective estimates of CBF from video images, an automated computer-based image processing technique is developed. This study proposes a new method based on optical flow for image processing and peak detection for signal processing. We compare the measuring accuracy of the method in various combinations of image processing (optical flow versus difference image) and signal processing (fast Fourier transform [FFT] vs. peak detection [PD]). The digital high-speed video method with a manual count of CBF in slow motion video play, is the gold-standard in CBF measurement. We obtained a total of fifty recorded ciliated sinonasal epithelium images to measure CBF from the Department of Otolaryngology. The ciliated sinonasal epithelium images were recorded at 50-100 frames per second using a charge coupled device camera with an inverted microscope at a magnification of ×1,000. Results The mean square errors and variance for each method were 1.24, 0.84 Hz; 11.8, 2.63 Hz; 3.22, 1.46 Hz; and 3.82, 1.53 Hz for optical flow (OF) + PD, OF + FFT, difference image [DI] + PD, and DI + FFT, respectively. Of the four methods, PD using optical flow showed the best performance for measuring the CBF of nasal mucosa. Conclusions The proposed method was able to measure CBF more objectively and efficiently than what is currently possible.

The effects of a 1.8 GHz continuous electromagnetic fields on mucociliary transport of human nasal mucosa

The aim of this study was to investigate the effects of a 1.8 GHz continuous electromagnetic fields (EMF) on human nasal mucociliary transport, and to determine the pathophysiology of ciliary beat frequency (CBF) during an EMF‐induced change.

Analysis of Relationship between Levofloxacin and Corrected QT Prolongation Using a Clinical Data Warehouse

Objective The aim of this study was to examine whether or not levofloxacin has any relationship with QT prolongation in a real clinical setting by analyzing a clinical data warehouse of data collected from different hospital information systems. Methods Electronic prescription data and medical charts from 3 different hospitals spanning the past 9 years were reviewed, and a clinical data warehouse was constructed. Patients who were both administrated levofloxacin and given electrocardiograms (ECG) were selected. The correlations between various patient characteristics, concomitant drugs, corrected QT (QTc) prolongation, and the interval difference in QTc before and after levofloxacin administration were analyzed. Results A total of 2,176 patients from 3 different hospitals were included in the study. QTc prolongation was found in 364 patients (16.7%). The study revealed that age (OR 1.026, p < 0.001), gender (OR 0.676, p = 0.007), body temperature (OR 1.267, p = 0.024), and cigarette smoking (OR 1.641, p = 0.022) were related with QTc prolongation. After adjusting for related factors, 12 drugs concomitant with levofloxacin were associated with QTc prolongation. For patients who took ECGs before and after administration of levofloxacin during their hospitalization (n = 112), there was no significant difference in QTc prolongation. Conclusions The age, gender, body temperature, cigarette smoking and various concomitant drugs might be related with QTc prolongation. However, there was no definite causal relationship or interaction between levofloxacin and QTc prolongation. Alternative surveillance methods utilizing the massive accumulation of electronic medical data seem to be essential to adverse drug reaction surveillance in future.

Knowledge Integration and Use-Case Analysis for a Customized Drug-Drug Interaction CDS Service

Clinical decision support systems (CDSSs) are thought to reduce adverse drug events (ADEs) by monitoring drug-drug interactions(DDIs). However, clinically improper or excessive alerts can result in high alert overrides. A tailored CDS service, which is appropriate for clinicians and their ordering situations, is required to increase alert acceptance. In this study, we conducted a 12-week pilot project adopting a tailed CDSS at an emergency department. The new CDSS was conducted via a stepwise integration of additional new rules. The alert status with changes in acceptance rate was analyzed. The most frequent DDI alerts were related to prescriptions of anti-inflammatory drugs. The percentages of alert overrides for each stage were 98.0%, 96.0%, 96.9%, and 98.1%, respectively. 91.5% of overridden alerts were related to discharge medications. To reduce the potential hazards of ADEs, the development of an effective customized DDI CDSS is required, via in-depth analysis on alert patterns and overridden reasons.

No-reference perceptual sharpness assessment for ultra-high-definition images

Since ultra-high-definition (UHD) display has larger resolution and various display size, it is necessary to measure image sharpness considering variation in visual resolution caused by diverse viewing geometry. In this paper, we propose a no-reference perceptual sharpness assessment model of UHD images. The proposed model analyzes viewing geometry in terms of display resolution and viewing environment. Then, we measure the local adaptive sharpness score in accordance with the textural motion blur, texture, and edge. In addition, we propose a spatial pooling method associated with foveal regions, which is caused by nonuniform distribution of the photoreceptors on a human retina. Through the rigorous experiments, we demonstrate that the proposed model can measure the sharpness of UHD images more accurately than other image sharpness assessment methods.

Video sharpness prediction based on motion blur analysis

For high bit rate video, it is important to acquire the video contents with high resolution, the quality of which may be degraded due to the motion blur from the movement of an object(s) or the camera. However, conventional sharpness assessments are designed to find focal blur caused either by defocusing or by compression distortion targeted for low bit rates. To overcome this limitation, we present a no-reference framework of a visual sharpness assessment (VSA) for high-resolution video based on the motion and scene classification. In the proposed framework, the accuracy of the sharpness estimation can be improved via pooling weighted by the visual perception from the object and camera movements and by the strong influence from the region with the highest sharpness. Based on the motion blur characteristics, the variance and the contrast over the spectral domain are used to quantify the perceived sharpness. Moreover, for the VSA, we extract the highly influential sharper regions and emphasize them by utilizing the scene adaptive pooling.