• 한국약용작물학회지
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• 제7권1호
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• pp.27-30
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• 1999

작약 수확후 건조과정중 양질의 약재 생산을 위한 기초자료를 얻고자 뿌리약초 박피기를 이용하여 껍질제거시 박피시간을 달리하여 시험한 결과를 요약하면 다음과 같다. 작약근의 기계박피시 표피 부분만 제거하고 피층부분의 수량손실과 paeoniflirin 함량의 손실을 방지하기 위한 적정 기계박피시간은 1회에 $10{\sim}30$분이 었다. 작약근 부위별 paeoniflorin 함량은 박피시간이 길어질수록 감소하였고, 상위부>하위부>중위부 순으로 노두부분과 인접한 상위부가 가장 높았다. 건조 작약 절단 전에 수처리시 박피 및 수침시간이 길어질수록 paeoniflorin의 유실량이 많았고, 색도 변화는 박피시간이 길어짐에 따라 적갈색의 표피 부분이 제거되어 명도가 증가하고 총 색도가 감소하였다.

• 대한의용생체공학회:의공학회지
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• 제42권4호
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• pp.175-185
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• 2021

Purpose: Vital sign are used to help assess the general physical health of a person, give clues to possible diseases, and show progress toward recovery. Researchers are using vital sign data and AI(artificial intelligence) to manage a variety of diseases and predict mortality. In order to analyze vital sign data using AI, it is important to select and extract vital sign data suitable for research purposes. Methods: We developed a method to visualize vital sign and early warning scores by processing retrospective vital sign data collected from EMR(electronic medical records) and patient monitoring devices. The vital sign data used for development were obtained using the open EMR big data MIMIC-III and the wearable patient monitoring device(CareTaker). Data processing and visualization were developed using Python. We used the development results with machine learning to process the prediction of mortality in ICU patients. Results: We calculated NEWS(National Early Warning Score) to understand the patient's condition. Vital sign data with different measurement times and frequencies were sampled at equal time intervals, and missing data were interpolated to reconstruct data. The normal and abnormal states of vital sign were visualized as color-coded graphs. Mortality prediction result with processed data and machine learning was AUC of 0.892. Conclusion: This visualization method will help researchers to easily understand a patient's vital sign status over time and extract the necessary data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권8호
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• pp.2016-2029
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• 2023

Accurate prediction of critical illness is significant for ensuring the lives and health of patients. The selection of indicators affects the real-time capability and accuracy of the prediction for critical illness. However, the diversity and complexity of these indicators make it difficult to find potential connections between them and critical illnesses. For the first time, this study proposes an indicator analysis model to extract key indicators from the preoperative and intraoperative clinical indicators and laboratory results of critical illnesses. In this study, preoperative and intraoperative data of heart failure and respiratory failure are used to verify the model. The proposed model processes the datum and extracts key indicators through four parts. To test the effectiveness of the proposed model, the key indicators are used to predict the two critical illnesses. The classifiers used in the prediction are light gradient boosting machine (LightGBM) and eXtreme Gradient Boosting (XGBoost). The predictive performance using key indicators is better than that using all indicators. In the prediction of heart failure, LightGBM and XGBoost have sensitivities of 0.889 and 0.892, and specificities of 0.939 and 0.937, respectively. For respiratory failure, LightGBM and XGBoost have sensitivities of 0.709 and 0.689, and specificity of 0.936 and 0.940, respectively. The proposed model can effectively analyze the correlation between indicators and postoperative critical illness. The analytical results make it possible to find the key indicators for postoperative critical illnesses. This model is meaningful to assist doctors in extracting key indicators in time and improving the reliability and efficiency of prediction.

• 터널과지하공간
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• 제29권1호
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• pp.1-11
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• 2019

본 연구에서는 국내 의료, 제조, 금융, 자동차, 도시 분야와 해외 광업 분야에서 머신러닝 기술이 활용된 사례를 조사하였다. 문헌 조사를 통해 머신러닝 기술이 의학영상 정보시스템 개발, 실시간 모니터링 및 이상 진단 시스템 개발, 정보시스템의 보안 수준 개선, 자율주행차 개발, 도시 통합관리 시스템 개발 등에 광범위하게 활용되어왔음을 알 수 있었다. 현재까지 국내 광업 분야에서는 머신러닝 기술의 활용사례를 찾을 수 없었으나, 해외에서는 광상 탐사나 광산 개발의 생산성 및 안전성을 개선을 위해 머신러닝 기술을 도입한 프로젝트들을 찾을 수 있었다. 향후 머신러닝 기술의 광업 분야 도입은 점차 확산될 것으로 예상된다.

• 대한물리치료과학회지
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• 제20권1호
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• pp.1-7
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• 2013

Purpose : Investigate the effects of Horse-back riding Simulation Machine training on the Balance ability in Patients with Stroke. Method : The patients were divided to control group(n=18) with conventional rehabilitation conventional rehabilitation 60min/day and experimental group(n=17) with hippotherapy simulator 15 min/day after conventional rehabilitation 45min/day, 5 time/week for 4 weeks. Balance ability of both groups was assessed using Timed Up and Go(TUG), Berg balabce scale(BBS) and Center of pressure area(COPA). In the present result, there was a no significant(P>0.05) Results : The results of this study showed that Horse-back riding Simulation Machine training, after training, had meaningful difference of TUG, BBS and COPA. Conclusion : This study showed that Horse-back riding Simulation Machine training increased balance ability that resulted in enhancement of motor performance.

• 대한상한금궤의학회지
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• 제14권1호
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• pp.41-50
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• 2022

Objective : The purpose of this study is to explore the most suitable machine learning model algorithm for Shanghanlun diagnostic system classification using natural language processing (NLP). Methods : A total of 201 data items were collected from 『Shanghanlun』 and 『Clinical Shanghanlun』, 'Taeyangbyeong-gyeolhyung' and 'Eumyangyeokchahunobokbyeong' were excluded to prevent oversampling or undersampling. Data were pretreated using a twitter Korean tokenizer and trained by logistic regression, ridge regression, lasso regression, naive bayes classifier, decision tree, and random forest algorithms. The accuracy of the models were compared. Results : As a result of machine learning, ridge regression and naive Bayes classifier showed an accuracy of 0.843, logistic regression and random forest showed an accuracy of 0.804, and decision tree showed an accuracy of 0.745, while lasso regression showed an accuracy of 0.608. Conclusions : Ridge regression and naive Bayes classifier are suitable NLP machine learning models for the Shanghanlun diagnostic system classification.

• 한국인공지능학회지
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• 제7권2호
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• pp.19-24
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• 2019

Lung cancer is a chronic disease which ranks fourth in cancer incidence with 11 percent of the total cancer incidence in Korea. To deal with such issues, there is an active study on the usefulness and utilization of the Clinical Decision Support System (CDSS) which utilizes machine learning. Thus, this study reviews existing studies on artificial intelligence technology that can be used in determining the lung cancer, and conducted a study on the applicability of machine learning in determination of the lung cancer by comparison and analysis using Azure ML provided by Microsoft. The results of this study show different predictions yielded by three algorithms: Support Vector Machine (SVM), Two-Class Support Decision Jungle and Multiclass Decision Jungle. This study has its limitations in the size of the Big data used in Machine Learning. Although the data provided by Kaggle is the most suitable one for this study, it is assumed that there is a limit in learning the data fully due to the lack of absolute figures. Therefore, it is claimed that if the agency's cooperation in the subsequent research is used to compare and analyze various kinds of algorithms other than those used in this study, a more accurate screening machine for lung cancer could be created.

• 한국정보전자통신기술학회논문지
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• 제14권5호
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• pp.413-420
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• 2021

본 연구는 수집 파라미터 중 하나가 부족할 경우 다른 파라미터를 통해 부족한 파라미터를 예측하기 위한 기계학습 모델에 대한 연구로서, 실내 환경 데이터 수집 장치를 통해 시간에 따른 온도·습도·CO2농도·광량에 대한 데이터를 수집하고, 수집한 데이터를 Matlab내 기계학습 회귀분석 기능을 통해 시간·온도·습도·CO2·광량 데이터를 예측하는 회귀모델을 만들었다. 또한 각 파라미터별로 RMSE 값이 가장 적은 3가지 모델을 선정하였으며 이에 대한 검증을 진행했다. 검증을 위해 각 파라미터로 도출된 예측모델에 테스트 데이터를 적용하여 예측치를 구했으며, 실측치와 구해진 예측치 간의 상관계수와 오차 평균을 구한 후 이를 비교하였다.

• Korean Journal of Radiology
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• 제24권12호
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• pp.1303-1305
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• 2023

• Korean Circulation Journal
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• 제53권10호
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• pp.690-692
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• 2023