• Investigative Magnetic Resonance Imaging
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• 제24권3호
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• pp.132-140
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• 2020

Purpose: To retrospectively determine whether specialized magnetic resonance imaging (MRI) reading performed by an experienced radiologist affected the successful assessment of extraprostatic extension (EPE) in the presence of biopsy-related hemorrhage after prostate biopsy. Materials and Methods: Two hundred consecutive patients with biopsy-proven prostate cancer underwent MRI. General radiologist and subspecialized radiologist readings were unpaired and reviewed in random order by a radiologist who was blinded to patients' clinical details and histopathologic data. The extent of hemorrhage was assessed on T1-weighted (T1W) MRI using a 1-4 scale, and the likelihood of EPE was assessed for each of the four categories. Histopathologic specimens served as the reference standard. The area under the curve (AUC) of the standard reading was compared to that of the specialized reading. Results: Post-biopsy hemorrhage was subjectively graded as ≥ 3 in 101 patients (50.5%) by standard reading, and in 100 patients (50.0%) by specialized reading. The standard and specialized readings disagreed for 40 (20.7%) of the patients (kappa [κ] = 0.35; 95% CI, 0.14-0.48). Of these, specialized reading was the correct interpretation for 21 patients (52.5%). The sensitivity (75% vs. 44%; P = 0.002) and area under the receiver operating characteristics (AUROC) (0.83 vs. 0.67; P = 0.008) of the specialized readings were significantly higher than those of the standard readings, while there was no significant difference in specificity (84% vs. 87%; P = 0.434). Conclusion: The reinterpretation of MRI by experienced radiologists significantly improves the diagnosis of EPE in prostate cancer in the presence of post-biopsy hemorrhage.

• International Journal of Computer Science & Network Security
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• 제21권6호
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• pp.319-328
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• 2021

Parallel administration of numerous drugs increases Drug-Drug Interaction (DDI) because one drug might affect the activity of other drugs. DDI causes negative or positive impacts on therapeutic output. So there is a need to discover DDI to enhance the safety of consuming drugs. Though there are several DDI system exist to predict an interaction but nowadays it becomes impossible to maintain with a large number of biomedical texts which is getting increased rapidly. Mostly the existing DDI system address classification issues, and especially rely on handcrafted features, and some features which are based on particular domain tools. The objective of this paper to predict DDI in a way to avoid adverse effects caused by the consumed drugs, to predict similarities among the drug, Drug pair similarity calculation is performed. The best optimal weight is obtained with the support of KHA. LSTM function with weight obtained from KHA and makes bets prediction of DDI. Our methodology depends on (LSTM-KHA) for the detection of DDI. Similarities among the drugs are measured with the help of drug pair similarity calculation. KHA is used to find the best optimal weight which is used by LSTM to predict DDI. The experimental result was conducted on three kinds of dataset DS1 (CYP), DS2 (NCYP), and DS3 taken from the DrugBank database. To evaluate the performance of proposed work in terms of performance metrics like accuracy, recall, precision, F-measures, AUPR, AUC, and AUROC. Experimental results express that the proposed method outperforms other existing methods for predicting DDI. LSTMKHA produces reasonable performance metrics when compared to the existing DDI prediction model.

• 한국멀티미디어학회논문지
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• 제25권8호
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• pp.1224-1232
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• 2022

Whole body bone scan is the most frequently performed nuclear medicine imaging to evaluate bone metastasis in cancer patients. We evaluated the performance of a VGG16-based transfer learning classifier for bone scan images in which metastatic bone lesion was present. A total of 1,000 bone scans in 1,000 cancer patients (500 patients with bone metastasis, 500 patients without bone metastasis) were evaluated. Bone scans were labeled with abnormal/normal for bone metastasis using medical reports and image review. Subsequently, gradient-weighted class activation maps (Grad-CAMs) were generated for explainable AI. The proposed model showed AUROC 0.96 and F1-Score 0.90, indicating that it outperforms to VGG16, ResNet50, Xception, DenseNet121 and InceptionV3. Grad-CAM visualized that the proposed model focuses on hot uptakes, which are indicating active bone lesions, for classification of whole body bone scan images with bone metastases.

• 한국임상약학회지
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• 제34권1호
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• pp.21-29
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• 2024

Background: Daytime sleepiness, a common phenomenon among adolescents focused on academics, has negative effects on aspects such as growth and overall learning. However, research on various drugs and diseases affecting daytime sleepiness is lacking in the reality. Therefore, this study aims to investigate the factors influencing daytime sleepiness in adolescents with daytime sleepiness. Methods: This study was conducted through a survey of 2,432 middle and high school students, aged 14 to 19. The questionnaire consisted of information on socio-demographic characteristics, overall health status, and sleep patterns. The Pediatric Daytime Sleepiness Scale (PDSS), translated into Korean, was used to assess daytime sleepiness. Daytime sleepiness was measured by calculating the total score for each item of the PDSS, and divided into two groups based on the cutoff value of 19, which was the upper quartile. Results: We analyzed a total of 1,770 students including 799 boys and 971 girls. Students with a PDSS score of 19 or higher made up 33.3% of boys and 66.7% of girls. In multivariate analyses, females, smoking, poor self-reported health level, sleep after 12 am, not feeling refreshed in the morning, headache, muscle pain, and scoliosis increased the risk of daytime sleepiness significantly. The AUROC of PDSS, including significant factors in multivariate analyses, was 0.751 (95% CI 0.725~0.776). Conclusions: Daytime sleepiness in adolescents affects growth, academic performance, and emotional stability. Therefore, it is important to manage medications, diseases, and other factors that affect daytime sleepiness on a social level.

• Journal of Ginseng Research
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• 제48권4호
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• pp.373-383
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• 2024

Background: Network pharmacology has emerged as a powerful tool to understand the therapeutic effects and mechanisms of natural products. However, there is a lack of comprehensive evaluations of network-based approaches for natural products on identifying therapeutic effects and key mechanisms. Purpose: We systematically explore the capabilities of network-based approaches on natural products, using Panax ginseng as a case study. P. ginseng is a widely used herb with a variety of therapeutic benefits, but its active ingredients and mechanisms of action on chronic diseases are not yet fully understood. Methods: Our study compiled and constructed a network focusing on P. ginseng by collecting and integrating data on ingredients, protein targets, and known indications. We then evaluated the performance of different network-based methods for summarizing known and unknown disease associations. The predicted results were validated in the hepatic stellate cell model. Results: We find that our multiscale interaction-based approach achieved an AUROC of 0.697 and an AUPR of 0.026, which outperforms other network-based approaches. As a case study, we further tested the ability of multiscale interactome-based approaches to identify active ingredients and their plausible mechanisms for breast cancer and liver cirrhosis. We also validated the beneficial effects of unreported and top-predicted ingredients, in cases of liver cirrhosis and gastrointestinal neoplasms. Conclusion: our study provides a promising framework to systematically explore the therapeutic effects and key mechanisms of natural products, and highlights the potential of network-based approaches in natural product research.

• Korean Circulation Journal
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• 제53권10호
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• pp.677-689
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• 2023

Background and Objectives: There is limited evidence regarding machine-learning prediction for the recurrence of atrial fibrillation (AF) after electrical cardioversion (ECV). This study aimed to predict the recurrence of AF after ECV using machine learning of clinical features and electrocardiograms (ECGs) in persistent AF patients. Methods: We analyzed patients who underwent successful ECV for persistent AF. Machine learning was designed to predict patients with 1-month recurrence. Individual 12-lead ECGs were collected before and after ECV. Various clinical features were collected and trained the extreme gradient boost (XGBoost)-based model. Ten-fold cross-validation was used to evaluate the performance of the model. The performance was compared to the C-statistics of the selected clinical features. Results: Among 718 patients (mean age 63.5±9.3 years, men 78.8%), AF recurred in 435 (60.6%) patients after 1 month. With the XGBoost-based model, the areas under the receiver operating characteristic curves (AUROCs) were 0.57, 0.60, and 0.63 if the model was trained by clinical features, ECGs, and both (the final model), respectively. For the final model, the sensitivity, specificity, and F1-score were 84.7%, 28.2%, and 0.73, respectively. Although the AF duration showed the best predictive performance (AUROC, 0.58) among the clinical features, it was significantly lower than that of the final machine-learning model (p<0.001). Additional training of extended monitoring data of 15-minute single-lead ECG and photoplethysmography in available patients (n=261) did not significantly improve the model's performance. Conclusions: Machine learning showed modest performance in predicting AF recurrence after ECV in persistent AF patients, warranting further validation studies.

• 지능정보연구
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• 제29권1호
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• pp.121-142
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• 2023

수입식품의 수입 건수와 수입 중량이 꾸준히 증가함에 따라 식품안전사고 방지를 위한 수입식품의 안전관리가 더욱 중요해지고 있다. 식품의약품안전처는 통관단계의 수입검사와 더불어 통관 전 단계인 해외제조업소에 대한 현지실사를 시행하고 있지만 시간과 비용이 많이 소요되고 한정된 자원 등의 제약으로 데이터 기반의 수입식품 안전관리 방안이 필요한 실정이다. 본 연구에서는 현지실사 전 부적합이 예상되는 업체를 사전에 선별하는 기계학습 예측 모형을 마련하여 현지실사의 효율성을 높이고자 하였다. 이를 위해 통합식품안전정보망에 수집된 총 303,272건의 해외제조가공업소 기본정보와 2019년도부터 2022년 4월까지의 현지실사 점검정보 데이터 1,689건을 수집하였다. 해외제조가공업소의 데이터 전처리 후 해외 제조업소_코드를 활용하여 현지실사 대상 데이터만 추출하였고, 총 1,689건의 데이터와 103개의 변수로 구성되었다. 103개의 변수를 테일유(Theil-U) 지표를 기준으로 '0'인 변수들을 제거하였고, 다중대응분석(Multiple Correspondence Analysis)을 적용해 축소 후 최종적으로 49개의 특성변수를 도출하였다. 서로 다른 8개의 모델을 생성하고, 모델 학습 과정에서는 5겹 교차검증으로 과적합을 방지하고, 하이퍼파라미터를 조정하여 비교 평가하였다. 현지실사 대상업체 선별의 연구목적은 부적합 업체를 부적합이라고 판정하는 확률인 검측률(recall)을 최대화하는 것이다. 머신러닝의 다양한 알고리즘을 적용한 결과 Recall_macro, AUROC, Average PR, F1-score, 균형정확도(Balanced Accuracy)가 가장 높은 랜덤포레스트(Random Forest)모델이 가장 우수한 모형으로 평가되었다. 마지막으로 모델에 의해서 평가된 개별 인스턴스의 부적합 업체 선정 근거를 제시하기 위해 SHAP(Shapley Additive exPlanations)을 적용하고 현지실사 업체 선정 시스템에의 적용 가능성을 제시하였다. 본 연구결과를 바탕으로 데이터에 기반한 과학적 위험관리 모델을 통해 수입식품 관리체계의 구축으로 인력·예산 등 한정된 자원의 효율적 운영방안 마련에 기여하길 기대한다.

• 한국콘텐츠학회논문지
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• 제16권6호
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• pp.225-235
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• 2016

본 연구의 목적은 우리나라의 65세 이상 노인 집단에 총점으로 사용할 수 있는 단축형 회상기능척도(RFS-S)를 개발하고 정상노인 집단과 경도인지장애 집단에게 RFS-S를 시행하여 RFS-S의 내적신뢰도 및 준거타당도 검증과 인구학적 특성이 회상기능에 영향을 미치는 요인을 알아보고자 하였다. 연구 대상자는 65세 이상 정상대조군 60명, 경도인지자애군 58명으로 두 집단 간 일반적 특성은 유의한 차이가 없었다. 문항 간 내적신뢰도는 0.63이었고, RFS-S와 이야기회상검사, 치매임상평가척도 박스총점과의 수렴타당도는 각 0.20(p<.05), -0.25(p<.001)로 모두 유의한 상관관계를 보였다. ROC 곡선 아래의 면적을 분석한 준거타당도 결과 0.68(p<.001)로 덜 정확한(less accurate) 검사로 최적 절단점은 17점이었고 이에 따른 민감도는 0.59, 특이도는 0.72였다. 인구학적 변인에 따른 회상기능은 모두 유의한 차이를 보이지 않았다. RFS-S는 정상과 경도인지장애 차이를 판별하는 신뢰도와 타당도가 높은 진단 보조 도구이자 회상치료의 효과를 평가하는 유용한 도구임을 나타낸다.

• 한국융합학회논문지
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• 제8권5호
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• pp.79-85
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• 2017

본 연구는 만성 B형 바이러스성 감염 환자의 간 기능 혈액 검사 수치와 간 섬유화 스캔 검사(FibroScan(R))의 비교 분석을 통한 간 섬유화 스캔 검사의 임상적, 기기적 융합 유용성을 평가하고자 하였다. 2015년 7월 1일부터 2016년 2월 28일까지 대전시 B내과에 내원한 만성 B형 바이러스성 감염 환자 75명의 간 섬유화 스캔 검사 결과와 간 기능 혈액검사 수치를 분석 하고 ROC곡선을 작성하였다. 알라닌 아미노전이요소, 아스파르테이트 아미노전이요소 수치가 각각 0.572, 0.502로 섬유화 수치와 가장 높은 상관관계를 보였다(p<0.000). 감마지티피, 총 빌리루빈, 알칼리성 인산분해효소 수치도 비교적 유의한 상관관계를 보였으나 알파태아단백과 총 단백정량은 통계적으로 유의하지 않았다. 또한 알부민(-0.449)과 혈소판치(-0.373)도 섬유화 수치와 상관관계가 없었다(p<0.000). 간 섬유화 정도가 높은 등급일수록 ROC 곡선의 정확도가 증가하였으며 F4의 간 경변 단계에서 가장 큰 AUROC값을 나타냈다. 따라서 간 섬유화 스캔 검사는 만성 간질환 환자의 간 기능 수치인 알라닌 아미노전이요소, 아스파르테이트 아미노전이요소 수치와 유의한 상관관계를 보여 간의 염증 검사 및 만성 간질환 진단에 매우 유용한 것으로 판단된다.

• Korean Journal of Radiology
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• 제22권6호
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• pp.959-969
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• 2021

Objective: This study aimed to evaluate the role of preoperative two-dimensional (2D) shear wave elastography (SWE) in assessing the stages of liver fibrosis in patients with suspected biliary atresia (BA) and compared its diagnostic performance with those of serum fibrosis biomarkers. Materials and Methods: This study was approved by the ethical committee, and written informed parental consent was obtained. Two hundred and sixteen patients were prospectively enrolled between January 2012 and October 2018. The 2D SWE measurements of 69 patients have been previously reported. 2D SWE measurements, serum fibrosis biomarkers, including fibrotic markers and biochemical test results, and liver histology parameters were obtained. 2D SWE values, serum biomarkers including, aspartate aminotransferase to platelet ratio index (APRi), and other serum fibrotic markers were correlated with the stages of liver fibrosis by METAVIR. Receiver operating characteristic (ROC) curves and area under the ROC (AUROC) curve analyses were used. Results: The correlation coefficient of 2D SWE value in correlation with the stages of liver fibrosis was 0.789 (p < 0.001). The cut-off values of 2D SWE were calculated as 9.1 kPa for F1, 11.6 kPa for F2, 13.0 kPa for F3, and 15.7 kPa for F4. The AUROCs of 2D SWE in the determination of the stages of liver fibrosis ranged from 0.869 to 0.941. The sensitivity and negative predictive value of 2D SWE in the diagnosis of ≥ F3 was 93.4% and 96.0%, respectively. The diagnostic performance of 2D SWE was superior to that of APRi and other serum fibrotic markers in predicting severe fibrosis and cirrhosis (all p < 0.005) and other serum biomarkers. Multivariate analysis showed that the 2D SWE value was the only statistically significant parameter for predicting liver fibrosis. Conclusion: 2D SWE is a more effective non-invasive tool for predicting the stage of liver fibrosis in patients with suspected BA, compared with serum fibrosis biomarkers.