• Clinical Endoscopy
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• 제57권4호
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• pp.476-485
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• 2024

Background/Aims: Sedation has become a standard practice for patients undergoing gastrointestinal (GI) endoscopy. However, considering the serious cardiopulmonary adverse events associated with sedatives, it is important to identify patients at high risk. Machine learning can generate reasonable prediction for a wide range of medical conditions. This study aimed to evaluate the risk factors associated with sedation during GI endoscopy and develop a predictive model for hypoxia during endoscopy under sedation. Methods: This prospective observational study enrolled 446 patients who underwent sedative endoscopy at the Korea University Ansan Hospital. Clinical data were used as predictor variables to construct predictive models using the random forest method that is a machine learning algorithm. Results: Seventy-two of the 446 patients (16.1%) experienced life-threatening hypoxia requiring immediate medical intervention. Patients who developed hypoxia had higher body weight, body mass index (BMI), neck circumference, and Mallampati scores. Propofol alone and higher initial and total dose of propofol were significantly associated with hypoxia during sedative endoscopy. Among these variables, high BMI, neck circumference, and Mallampati score were independent risk factors for hypoxia. The area under the receiver operating characteristic curve for the random forest-based predictive model for hypoxia during sedative endoscopy was 0.82 (95% confidence interval, 0.79-0.86) and displayed a moderate discriminatory power. Conclusions: High BMI, neck circumference, and Mallampati score were independently associated with hypoxia during sedative endoscopy. We constructed a model with acceptable performance for predicting hypoxia during sedative endoscopy.

• 유통과학연구
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• 제13권1호
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• pp.7-18
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• 2015

Purpose - This study integrates a corporate competition system with a bird predation system to examine how organizational strategic adjustment capacity influences firm performance. By proving the prominent effects on performance, a financial vector is constructed to represent corporate strategic adjustment results, and an operation capacity vector is constructed, which can be categorized as a parameter for locating birds. All these works help us to propose a new method of investment, the portfolio decision model based on the strategic adjustment capacity. Research design, data, and methodology - Strategic adjustment capacity can be decomposed into three aspects: the organizational learning capacity from the top firms, the extent to which firms maintainor rely on the best operational capacity vector in history, and the ability to eliminate the disadvantages or retain the advantages of the operation capacity vector from the previous year. The method of solving cyclic equations is designed to evaluate strategic adjustment. Firms manufacturing specialized equipment are chosen to test the effects of the strategic adjustment capacity on three aspects of firm performance. Results - There is a positive correlation between the capacity to learn from the best firms and performance improvement. The relationship between the dependence or maintenance of a firm's advantages and performance improvement is a U-shape curve, and there is no significant effect of inertial control on performance improvement. Conclusions - A firm's competition system is a sophisticated adaptation, and competitive advantage and performance can be investigated based on the principles of competition in nature.

• 대한의생명과학회지
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• 제25권1호
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• pp.99-106
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• 2019

Amyloid positron emission tomography (PET) allows early and accurate diagnosis in suspected cases of Alzheimer's disease (AD) and contributes to future treatment plans. In the present study, a method of implementing a diagnostic system to distinguish ${\beta}$-Amyloid ($A{\beta}$) positive from $A{\beta}$ negative with objectiveness and accuracy was proposed using a machine learning approach, such as the Principal Component Analysis (PCA) and Support Vector Machine (SVM). $^{18}F$-Florbetaben (FBB) brain PET images were arranged in control and patients (total n = 176) with mild cognitive impairment and AD. An SVM was used to classify the slices of registered PET image using PET template, and a system was created to diagnose patients comprehensively from the output of the trained model. To compare the per-slice classification, the PCA-SVM model observing the whole brain (WB) region showed the highest performance (accuracy 92.38, specificity 92.87, sensitivity 92.87), followed by SVM with gray matter masking (GMM) (accuracy 92.22, specificity 92.13, sensitivity 92.28) for $A{\beta}$ positivity. To compare according to per-subject classification, the PCA-SVM with WB also showed the highest performance (accuracy 89.21, specificity 71.67, sensitivity 98.28), followed by PCA-SVM with GMM (accuracy 85.80, specificity 61.67, sensitivity 98.28) for $A{\beta}$ positivity. When comparing the area under curve (AUC), PCA-SVM with WB was the highest for per-slice classifiers (0.992), and the models except for SVM with WM were highest for the per-subject classifier (1.000). We can classify $^{18}F$-Florbetaben amyloid brain PET image for $A{\beta}$ positivity using PCA-SVM model, with no additional effects on GMM.

• 한국컴퓨터정보학회논문지
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• 제28권10호
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• pp.133-153
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• 2023

본 논문에서 우리는 뇌 신호 측정 기술 중 하나인 뇌전도를 활용한 새로운 접근방식을 제안한다. 전통적으로 연구자들은 감정 상태의 분류성능을 향상시키기 위해 뇌전도 신호와 생체신호를 결합해왔다. 우리의 목표는 뇌전도와 결합된 생체신호의 상호작용 효과를 탐구하고, 뇌전도+생체신호의 조합이 뇌전도 단독사용 또는 임의로 생성된 의사 무작위 신호와 결합한 경우에 비해 감정 상태의 분류 정확도를 향상시킬 수 있는지를 확인한다. 네 가지 특징추출 방법을 사용하여 두 개의 공개 데이터셋에서 얻은 데이터 기반의 뇌전도, 뇌전도+생체신호, 뇌전도+생체신호+무작위신호, 및 뇌전도+무작위신호의 네 가지 조합을 조사했다. 감정 상태 (작업 대 휴식 상태)는 서포트 벡터 머신과 장단기 기억망 분류기를 사용하여 분류했다. 우리의 결과는 가장 높은 정확도를 가진 서포트 벡터 머신과 고속 퓨리에 변환을 사용할 때 뇌전도+생체신호의 평균 오류율이 뇌전도+무작위신호와 뇌전도 단독 신호만을 사용한 경우에 비해 각각 4.7% 및 6.5% 높았음을 보여주었다. 우리는 또한 다양한 무작위 신호를 결합하여 뇌전도+생체신호의 오류율을 철저하게 분석했다. 뇌전도+생체신호+무작위신호의 오류율 패턴은 초기에는 깊은 이중 감소 현상으로 인해 감소하다가 차원의 저주로 인해 증가하는 V자 모양을 나타냈다. 결과적으로, 우리의 연구 결과는 뇌파와 생체신호의 결합이 항상 유망한 분류성능을 보장할 수 없음을 시사한다.