• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.49 no.3
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• pp.135-141
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• 2023

Objectives: This study aimed to develop and validate machine learning (ML) models using H₂O-AutoML, an automated ML program, for predicting medication-related osteonecrosis of the jaw (MRONJ) in patients with osteoporosis undergoing tooth extraction or implantation. Patients and Methods: We conducted a retrospective chart review of 340 patients who visited Dankook University Dental Hospital between January 2019 and June 2022 who met the following inclusion criteria: female, age ≥55 years, osteoporosis treated with antiresorptive therapy, and recent dental extraction or implantation. We considered medication administration and duration, demographics, and systemic factors (age and medical history). Local factors, such as surgical method, number of operated teeth, and operation area, were also included. Six algorithms were used to generate the MRONJ prediction model. Results: Gradient boosting demonstrated the best diagnostic accuracy, with an area under the receiver operating characteristic curve (AUC) of 0.8283. Validation with the test dataset yielded a stable AUC of 0.7526. Variable importance analysis identified duration of medication as the most important variable, followed by age, number of teeth operated, and operation site. Conclusion: ML models can help predict MRONJ occurrence in patients with osteoporosis undergoing tooth extraction or implantation based on questionnaire data acquired at the first visit.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.48 no.3
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• pp.167-173
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• 2022

Objectives: The purpose of this study was to estimate the minimal clinically important difference (MCID) of mouth opening (MO) and patient satisfaction in surgically treated oral submucous fibrosis (OSMF) patients. Materials and Methods: The status of MO was collected preoperatively (T0), postoperatively at 3 months (T1), and at a minimum of 6 months postoperatively (T2). MCID was determined through the anchor-based approach with the change difference method, mean change method, and receiver operator characteristic curve (ROC) method. Results: In this study, 35 patients enrolled and completed postoperative follow-up (T2) averaging a duration of 18.1 months. At T1, using the change difference method, MO was 14.89 mm and the ROC curve exhibited a 11.5 gain in MO (sensitivity 81.8% and specificity 100%, area under the curve [AUC] of 0.902) and was classified as MCID as reported by patients. At T2, MCID of MO was 9.75 mm using the change difference method and 11.75 mm by the mean change method. The ROC curve revealed that the MCID of MO at T2 was 10.5 mm with 73.9% sensitivity and 83.3% specificity (AUC of 0.873). The kappa value was 0.91, confirming reliability of the data. Conclusion: This study demonstrated MCID values that indicate the clinical relevance of surgical treatment of OSMF if the minimum possible gain in MO is approximately 10 mm.

• Bulletin of the Korean Chemical Society
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• v.35 no.2
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• pp.557-561
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• 2014

The aim of the present study was to improve dosing of lacosamide, a functionalized amino acid used as an antiepileptic agent, from twice daily to once daily for the convenience of patients. A sustained-release lacosamide tablet was developed and dissolution testing was employed to determine in vitro release behavior using water or buffer solutions at pH 1.2, 4.0, or 6.8. Lacosamide was released for 12 h from the sustainedrelease (SR) tablet, as compared to complete release within 1 h from an immediate-release $Vimpat^{(R)}$ tablet. Each formulation (100 mg) was orally administered to six beagle dogs and six mini-pigs under fasted conditions, and pharmacokinetic parameters such as the area under the concentration time curve ($AUC_t$), the maximum plasma concentration ($C_{max}$), and the time at which this occurred ($T_{max}$) were calculated. These results showed similar values for $AUC_t$, $C_{max}$, and $T_{max}$ following oral administration of immediate-release ($Vimpat^{(R)}$) and SR lacosamide tablets.

• Journal of Veterinary Clinics
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• v.23 no.4
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• pp.416-420
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• 2006

Compare to testing sera individually, pooled-serum testing has considered as a cost-effective method, particularly on a large population-based seroprevalence studies. This study was to determine the relationship between individual sera and pooled sera titers for detection of avian infectious bronchitis virus (IBV) and to evaluate suitability of pooled sera by comparing prevalences estimated from both samples. A total of 5,000 individual samples were collected from 500 flocks in Chungcheong, Gyunsgi, and Kangwon provinces between January 2005 and February 2006. Ten samples were randomly selected from each flock. Five-hundred pooled sera were prepared by mixing equal amount of each 10 individual serum from the original samples. IBV antibody titers were measured by hemagglutination inhibition (HI) test. The least squares regression analysis was performed to construct equation between pooled and mean individual titers. To determine whether the flock is infected 4 arbitrary criteria were used: detection of at least 1 chicken with HI titer ${\ge}$ 9 (criterion 1), detection of at least 2 samples with HI titer ${\ge}$9 (criterion 2), detection of at least 1 sample with HI titer ${\ge}$ 10 (criterion 3), and filially detection of at least 1 sample with HI titer ${\ge}$ 11 (criterion 4). The receiver operating characteristic (ROC) curve was used to examine the cut-off points of pooled titers showing optimal diagnostic accuracy. The area under the curve (AUC), sensitivities (Se), specificities (Sp), and positive (PPV) and negative (NPV) predictive values were calculated. The regression equation between pooled titers (pool) and mean individual titers (mean) was: $pool= 1.2498+0.8952{\times}mean$, with coefficient of determination of 87% (p< 0.0001). The optimal cut-off points of pooled titers were titer 8 for criterion 1 (AUC=0.975, Se=0.883, Sp=0.959, PPV=0.985, NPV=0.728), titer 8 for criterion 2 (AUC=0.969, Se=0.954, Sp=0.855, PPV=0.926, NPV=0.907), titer 9 for criterion 3 (AUC=0.970, Se=0.836, Sp=0.967, PPV=0.978, NPV=0.772), and titer 9 for criterion 4 (AUC= 0.946, Se=0.928, Sp=0.843, PPV=0.857, NPV=0.921). The difference of 'prevalence estimated by individual and pooled sample showed a minimum of 2% for criteria 2 and a maximum of 9.1:% for criteria 3. These results indicate that the use of pooled sera in HI test for screening IBV infection in laying hen flocks is considered as a cost-effective method of testing large numbers of samples with high diagnostic accuracy.

• Analytical Science and Technology
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• v.34 no.2
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• pp.78-86
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• 2021

The objective of this study was to investigate urinary cut-off concentrations of quetiapine and risperidone for distinction between normal and abnormal/non-takers who were being placed on probation. Liquid chromatography-tandem mass spectrometric (LC-MS/MS) method was employed for determination of antipsychotic drugs in urine from mentally disordered probationers. The optimal cut-off values of antipsychotic drugs were calculated using receiver operating characteristic (ROC) curve analysis. The sensitivity and specificity of the method for the detection of antipsychotic drugs in urine were subsequently evaluated. The area under the ROC curve (AUC) was 0.927 for norquetiapine and 0.791 for 9-hydroxyrisperidone, respectively. These antipsychotic drugs are classified readily in the ROC curve analysis. The cut-off values for distinguishing regular and irregular/non-takers were 39.1 ng/mL for norquetiapine and 67.9 ng/mL for 9-hydroxyrisperidone, respectively. The results of this study suggest the cut-off values of quetiapine and risperidone were highly useful to distinguish regular takers from irregular/non-takers.

• Atmosphere
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• v.28 no.2
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• pp.223-232
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• 2018

The Global-Korean aviation Turbulence Guidance (G-KTG) system is developed using the operational Global Data Assimilation and Prediction System of Korea Meteorological Administration with 17-km horizontal grid spacing. The G-KTG system provides an integrated solution of various clear-air turbulence (CAT) diagnostics and mountain-wave induced turbulence (MWT) diagnostics for low [below 10 kft (3.05 km)], middle [10 kft (3.05 km) - 20 kft (6.10 km)], and upper [20 kft (6.10 km) - 50 kft (15.24 km)] levels. Individual CAT and MWT diagnostics in the G-KTG are converted to a 1/3 power of energy dissipation rate (EDR). 12-h forecast of the G-KTG is evaluated using 6-month period (2016.06~2016.11) of in-situ EDR observation data. The forecast skill is calculated by area under curve (AUC) where the curve is drawn by pairs of probabilities of detection of "yes" for moderate-or-greater-level turbulence events and "no" for null-level turbulence events. The AUCs of G-KTG for the upper, middle, and lower levels are 0.79, 0.69, and 0.63, respectively. Comparison of the upper-level G-KTG with the regional-KTG in East Asia reveals that the forecast skill of the G-KTG (AUC = 0.77) is similar to that of the regional-KTG (AUC = 0.79) using the Regional Data Assimilation and Prediction System with 12-km horizontal grid spacing.

• Korean Circulation Journal
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• v.53 no.11
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• pp.758-771
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• 2023

Background and Objectives: Paroxysmal atrial fibrillation (AF) is a major potential cause of embolic stroke of undetermined source (ESUS). However, identifying AF remains challenging because it occurs sporadically. Deep learning could be used to identify hidden AF based on the sinus rhythm (SR) electrocardiogram (ECG). We combined known AF risk factors and developed a deep learning algorithm (DLA) for predicting AF to optimize diagnostic performance in ESUS patients. Methods: A DLA was developed to identify AF using SR 12-lead ECG with the database consisting of AF patients and non-AF patients. The accuracy of the DLA was validated in 221 ESUS patients who underwent insertable cardiac monitor (ICM) insertion to identify AF. Results: A total of 44,085 ECGs from 12,666 patient were used for developing the DLA. The internal validation of the DLA revealed 0.862 (95% confidence interval, 0.850-0.873) area under the curve (AUC) in the receiver operating curve analysis. In external validation data from 221 ESUS patients, the diagnostic accuracy of DLA and AUC were 0.811 and 0.827, respectively, and DLA outperformed conventional predictive models, including CHARGE-AF, C2HEST, and HATCH. The combined model, comprising atrial ectopic burden, left atrial diameter and the DLA, showed excellent performance in AF prediction with AUC of 0.906. Conclusions: The DLA accurately identified paroxysmal AF using 12-lead SR ECG in patients with ESUS and outperformed the conventional models. The DLA model along with the traditional AF risk factors could be a useful tool to identify paroxysmal AF in ESUS patients.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.21 no.4
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• pp.278-288
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• 2018

Purpose: Dehydration is a paediatric medical emergency but there is no single standard parameter to evaluate it at the emergency department. Our aim was to evaluate the reliability and validity of capillary refilling time as a triage parameter to assess dehydration in children. Methods: This was a prospective pilot cohort study of children who presented to two paediatric emergency departments in Italy, with symptoms of dehydration. Reliability was assessed by comparing the triage nurse's measurements with those obtained by the physician. Validity was demonstrated by using 6 parameters suggestive of dehydration. Comparison between refilling time (RT) and a validated Clinical Dehydration Score (CDS) was also considered. The scale's discriminative ability was evaluated for the outcome of starting intravenous rehydration therapy by using a receiver operating characteristic (ROC) curve. Results: Participants were 242 children. All nurses found easy to elicit the RT after being trained. Interobserver reliability was fair, with a Cohen's kappa of 0.56 (95% confidence interval [CI], 0.41 to 0.70). There was a significant correlation between RT and weight loss percentage (r-squared=-0.27; 95% CI, -0.47 to -0.04). The scale's discriminative ability yielded an area under the ROC curve (AUC) of 0.65 (95% CI, 0.57 to 0.73). We found a similarity between RT AUC and CDS-scale AUC matching the two ROC curves. Conclusion: The study showed that RT represents a fast and handy tool to recognize dehydrated children who need a prompt rehydration and may be introduced in the triage line-up.

• Journal of The Korean Society of Emergency Medicine
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• v.29 no.5
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• pp.455-464
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• 2018

Objective: Machine learning is not yet widely used in the medical field. Therefore, this study was conducted to compare the performance of preexisting severity prediction models and machine learning based models (random forest [RF], gradient boosting [GB]) for mortality prediction in pneumonia patients. Methods: We retrospectively collected data from patients who visited the emergency department of a tertiary training hospital in Seoul, Korea from January to March of 2015. The Pneumonia Severity Index (PSI) and Sequential Organ Failure Assessment (SOFA) scores were calculated for both groups and the area under the curve (AUC) for mortality prediction was computed. For the RF and GB models, data were divided into a test set and a validation set by the random split method. The training set was learned in RF and GB models and the AUC was obtained from the validation set. The mean AUC was compared with the other two AUCs. Results: Of the 536 investigated patients, 395 were enrolled and 41 of them died. The AUC values of PSI and SOFA scores were 0.799 (0.737-0.862) and 0.865 (0.811-0.918), respectively. The mean AUC values obtained by the RF and GB models were 0.928 (0.899-0.957) and 0.919 (0.886-0.952), respectively. There were significant differences between preexisting severity prediction models and machine learning based models (P<0.001). Conclusion: Classification through machine learning may help predict the mortality of pneumonia patients visiting the emergency department.

• Archives of Pharmacal Research
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• v.26 no.7
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• pp.564-568
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• 2003

Pharmacokinetic and pharmacodynamic properties of gliclazide were studied after an oral administration of gliclazide tablets in healthy volunteers. After an overnight fasting, gliclazide tablet was orally administered to 11 volunteers; Additional 10 volunteers were used as a control group (i.e., no gliclazide administration). Blood samples were collected, and the concentration determined for gliclazide and glucose up to 24 after the administration. Standard pharmacokinetic analysis was carried out for gliclazide. Pharmacodynamic activity of the drug was expressed by increase of glucose concentration ($\Delta$PG), by area under the increase of glucose concentration-time curve ($AUC_{$\Delta$PG}$) or by the difference in increase of glucose concentration ($D_{$\Delta$PG}$) at each time between groups with and without gliclazide administration. Pharmacokinetic analysis revealed that $C_{max}, T_{max}$, CL/F (apparent clearance), V/F (apparent volume of distribution) and half-life of gliclazide were $4.69\pm1.38 mg/L, 3.45\pm1.11 h, 1.26\pm0.35 L/h, 17.78\pm5.27 L, and 9.99\pm2.15 h$, respectively. When compared with the no drug administration group, gliclazide decreased significantly the $AUC_{$\Delta$PG}$ s at 1, 1.5, 2, 2.5, 3 and 4 h (p＜0.05). The $\Delta$PGs were positively correlated with $AUC_{gliclazide}$ at 1 and 1.5 h (p＜0.05), and the correlation coefficient was maximum at 1 h (r = 0.642) and gradually decreased at 4 h after the administration. The $AUC_{$\Delta$PG}$s were positively correlated with $AUC_{gliclazide}$ at 1, 2, 3 and 4 h (p＜0.05), and the maximum correlation coefficient was obtained at 2 h (r=0.642) after the administration. The $D_{$\Delta$PG}$ reached the maximum at 1 h, remained constant from 1 h to 3 h, and decreased afterwards. Therefore, these observations indicated that maximum hypoglycemic effect of gliclazide was reached at approximately at 1.5 h after the administration and the effect decreased, probably because of the homeostasis mechanism, in health volunteers.