• 제목/요약/키워드: Medical model

검색결과 5,556건 처리시간 0.029초

보건의료정보관리 실습교육을 위한 실습모델 연구 (A Study on the Practice Model for Practical Education for Health and Medical Information Management)

  • 최준영
    • 보건의료생명과학 논문지
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    • 제8권2호
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    • pp.83-93
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    • 2020
  • 본 연구에서는 보건의료정보관리사 양성을 위한 대학에서 EMR 교육시스템을 활용하여 교육할 수 있는 실습모델을 연구하였다. 현재 보건의료정보관리사의 직무역량 강화를 위해 도입된 보건의료정보관리교육 평가·인증 기준에 보건의료정보관리를 위한 실습과정이 제시되어 있지 않다. 이에 따라서 보건의료 정보관리교육 평가·인증 편람에서 교육환경으로 제시한 실습프로그램을 EMR 교육시스템에서 실습할 수 있도록 프로그램을 구성였다. 또한 프로그램별로 보건의료정보관리 현장실습지침서에 따라 수행할 수 있는 실습모델을 연구하였다. 보건의료정보관리 교육용 EMR시스템을 이용하여 마스터데이터관리, 환자 등록, 의사처방, 진료비 수납, 건강보험청구관리, 서식관리, 퇴원등록, 암등록, 미비기록관리, 보건의료데이터관리, 보건의료통계, 정보보호/보안관리에 대한 실습을 수행할 수 있도록 실습모델을 연구하였다. 대학에서 체계적이고 표준화된 보건의료정보관리 실습과정을 수행하여 보건의료정보관리 교육의 질적 수준을 높임으로써 보건의료정보관리 전문가로써 역할을 수행할 수 있을 것이다. 이에 따라서 보건의료정보관리사의 실습교육을 통하여 의료데이터 분석을 기반으로 의료서비스를 개발하고 관리하는 보건의료 정보관리 전문가를 양성할 수 있도록 해야 할 것이다.

편평세포폐암에서 CT 영상 소견을 이용한 PD-L1 발현 예측 (Predictions of PD-L1 Expression Based on CT Imaging Features in Lung Squamous Cell Carcinoma)

  • 여성희;윤현정;김인중;김여진;이영;차윤기;박소현
    • 대한영상의학회지
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    • 제85권2호
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    • pp.394-408
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    • 2024
  • 목적 CT 영상 소견을 이용하여 편평세포폐암에서 programmed death ligand 1 (이하 PD-L1)의 발현을 예측하는 모델을 구축해 보고자 하였다. 대상과 방법 PD-L1 발현검사 결과를 포함하고 있는 97명의 편평세포폐암 환자를 포함하였고 종양 치료 전 시행한 CT 영상 소견을 분석하였다. 전체 환자군과 40명의 진행성(≥ stage IIIB) 병기 환자군에 대하여 PD-L1 발현 예측을 위한 다중 로지스틱 회귀 분석 모델 구축을 시행하였다. 각각의 환자군에 대하여 곡선 아래 면적(areas under the receiver operating characteristic curves; 이하 AUCs)을 분석하여 예측력을 평가하였다. 결과 전체 환자군에서 '전체 유의인자 모델'(종양병기, 종양크기, 흉막결절, 폐전이)의 AUC 값은 0.652이며, '선택 유의인자 모델'(흉막결절)은 0.556이었다. 진행성 병기 환자군에서 '선택 유의인자 모델'(종양크기, 흉막결절, 폐소수전이, 간질성폐렴의 부재)의 AUC 값은 0.897이었다. 이러한 인자들 중 흉막결절과 폐소수전이는 높은 오즈비를 보였다(각각, 8.78과 16.35). 결론 본 연구에서의 모델은 편평세포폐암의 PD-L1 발현예측의 가능성을 보여주었으며 흉막결절과 폐소수전이는 PD-L1 발현을 예측하는데 중요한 CT 예측인자였다.

Self-Care Education Programs Based on a Trans-Theoretical Model in Women Referring to Health Centers: Breast Self-Examination Behavior in Iran

  • Ghahremani, Leila;Mousavi, Zakiyeh;Kaveh, Mohammad Hossein;Ghaem, Haleh
    • Asian Pacific Journal of Cancer Prevention
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    • 제17권12호
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    • pp.5133-5138
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    • 2016
  • Background: Breast cancer is one of the most common cancers and a major public health problem in developing countries. However, early detection and treatment may be achieved by breast self-examination (BSE). Despite the importance of BSE in reducing the incidence of breast cancer and esultant deaths, the disease continues to be the most common cause of cancer death among women in Iran.This study aimed to determine the effects of self-care education on performance of BSE among women referring to health centers in our country. Materials and Methods: This quasi-experimental interventional study with pretest/posttest control group design was conducted on 168 women referred to health centers. The data were collected using a validated researcher-made questionnaire including demographic variables and trans-theoretical model constructs as well as a checklist assessing BSE behavior. The instruments were administered to groups with and without self-care education before, a week after, and 10 weeks after the intervention. Then, the data were entered into the SPSS statistical software (version 19) and analyzed using independent sample t-tests, paired sample t-test, repeated measures ANOVA, Chi-square, and Friedman tests (p<0.05). Results: The results showed an increase in the intervention group's mean scores of trans-theoretical model constructs (stages of change, self-efficacy, decisional balance, and processes of change) and BSE behavior compared to the control group (p<0.001). Conclusion: The study confirmed the effectiveness of aneducational intervention based ona trans-theoretical model in performing BSE. Therefore, designing educational interventions based on this model is recommended to improve women's health and reduce deaths due to breast cancer.

순환신경망을 이용한 질병발생건수 예측 (Predicting the number of disease occurrence using recurrent neural network)

  • 이승현;여인권
    • 응용통계연구
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    • 제33권5호
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    • pp.627-637
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    • 2020
  • 본 논문에서는 건강보험심사평가원에서 제공한 약 120만명의 2014년 고령환자의료자료(HIRA-APS-2014-0053)과 기상자료를 일반화추정방정식(generalized estimating equation; GEE) 모형과 long short term memory (LSTM) 기반 순환신경망(recurrent neural network; RNN) 모형으로 분석하여 기상 조건에 따른 주요 주상병의 발생 빈도를 예측한다. 이를 위해 환자가 의료 서비스를 받은 기관의 지역을 이용하여 환자의 거주지를 추정하고 해당 지역의 주별 기상 관측소 자료와 의료자료를 병합하였다. 질병 발생 상태를 세 개의 범주(질병에 걸리지 않음, 관심 주상병 발생, 다른 질병 방생)로 나누었으며 각 범주에 속할 확률을 GEE 모형과 RNN 모형으로 추정하였다. 각 범주별 발생 건수는 해당 범주의 속할 추정확률의 합으로 계산하였으며 비교분석결과 RNN을 이용한 예측이 GEE를 이용한 예측보다 정확도가 높은 것으로 나타났다.

Establishment and evaluation of the VX2 orthotopic lung cancer rabbit model: a ultra-minimal invasive percutaneous puncture inoculation method

  • Wang, Lijuan;Che, Keke;Liu, Zhonghong;Huang, Xianlong;Xiang, Shifeng;Zhu, Fei;Yu, Yu
    • The Korean Journal of Physiology and Pharmacology
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    • 제22권3호
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    • pp.291-300
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    • 2018
  • The purpose of the present work is to establish an ultra-minimal invasive percutaneous puncture inoculation method for a VX2 orthotopic lung cancer rabbit model with fewer technical difficulties, lower mortality of rabbits, a higher success rate and a shorter operation time, to evaluate the growth, metastasis and apoptosis of tumor by CT scans, necropsy, histological examination, flow cytometry and immunohistochemistry. The average inoculation time was 10-15 min per rabbit. The tumorbearing rate was 100%. More than 90% of the tumor-bearing rabbits showed local solitary tumor with 2-10 mm diameters after two weeks post-inoculation, and the rate of chest seeding was only 8.3% (2/24). The tumors diameters increased to 4-16 mm, and irregularly short thorns were observed 3 weeks after inoculation. Five weeks post-inoculation, the liquefaction necrosis and a cavity developed, and the size of tumor grew further. Before natural death, the CT images showed that the tumors spread to the chest. The flow cytometry and immunohistochemistry indicated that there was less apoptosis in VX2 orthotopic lung cancer rabbit model compared to chemotherapy drug treatment group. Minimal invasive percutaneous puncture inoculation is an easy, fast and accurate method to establish the VX2 orthotopic lung cancer rabbit model, an ideal in situ tumor model similar to human malignant tumor growth.

한의표준임상경로에 기반한 치매 안심 한의주치의 모형 개발 연구 (A Study on the Development of a Korean Medicine Clinical Pathway for Primary Care of Patients with Dementia Based on Clinical Pathway Methodology)

  • 권도영;권기태;허영진;김동수;조성훈
    • 동의신경정신과학회지
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    • 제34권4호
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    • pp.359-368
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    • 2023
  • Objectives: This study aims to establish a Korean medicine doctor's range of services in the dementia relief primary care system based on the previously developed dementia clinical practice guidelines (CPGs). Developing a dementia relief primary care Clinical Pathway (CP) can aid clinically when the Korean medicine primary care doctor conducts treatment. Methods: We analyzed Dementia Korean Medicine Primary Care Model Data and then applied CP Methodology to develop the configuration of the Korean Medicine Primary Care Model. For patients with Alzheimer's dementia (AD), vascular dementia (VD), and mild cognitive impairment (MCI), the Korean Medicine Primary Care Model focuses on improving cognitive function, everyday living abilities and easing symptoms through interventions described in CPGs. The contents of the draft model later include references to already-existing CPs. Results: The study sites were chosen as Korean medical clinics connected to primary care physicians in the dementia-friendly model. The CP used a time task matrix version to arrange the clinical chronology, which included all examinations, diagnoses, and treatment procedures, from the initial appointment to follow-ups and the end of therapy. Conclusions: It anticipates that Korean primary care doctors familiar with dementia can use the offered therapies for the first time by creating the dementia Korean medicine primary care model in this study. This is expected to maximize the range of medical services provided by Korean medicine and improve the standard of medical treatment.

Combination of 18F-Fluorodeoxyglucose PET/CT Radiomics and Clinical Features for Predicting Epidermal Growth Factor Receptor Mutations in Lung Adenocarcinoma

  • Shen Li;Yadi Li;Min Zhao;Pengyuan Wang;Jun Xin
    • Korean Journal of Radiology
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    • 제23권9호
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    • pp.921-930
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    • 2022
  • Objective: To identify epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma based on 18F-fluorodeoxyglucose (FDG) PET/CT radiomics and clinical features and to distinguish EGFR exon 19 deletion (19 del) and exon 21 L858R missense (21 L858R) mutations using FDG PET/CT radiomics. Materials and Methods: We retrospectively analyzed 179 patients with lung adenocarcinoma. They were randomly assigned to training (n = 125) and testing (n = 54) cohorts in a 7:3 ratio. A total of 2632 radiomics features were extracted from the tumor region of interest from the PET (1316) and CT (1316) images. Six PET/CT radiomics features that remained after the feature selection step were used to calculate the radiomics model score (rad-score). Subsequently, a combined clinical and radiomics model was constructed based on sex, smoking history, tumor diameter, and rad-score. The performance of the combined model in identifying EGFR mutations was assessed using a receiver operating characteristic (ROC) curve. Furthermore, in a subsample of 99 patients, a PET/CT radiomics model for distinguishing 19 del and 21 L858R EGFR mutational subtypes was established, and its performance was evaluated. Results: The area under the ROC curve (AUROC) and accuracy of the combined clinical and PET/CT radiomics models were 0.882 and 81.6%, respectively, in the training cohort and 0.837 and 74.1%, respectively, in the testing cohort. The AUROC and accuracy of the radiomics model for distinguishing between 19 del and 21 L858R EGFR mutational subtypes were 0.708 and 66.7%, respectively, in the training cohort and 0.652 and 56.7%, respectively, in the testing cohort. Conclusion: The combined clinical and PET/CT radiomics model could identify the EGFR mutational status in lung adenocarcinoma with moderate accuracy. However, distinguishing between EGFR 19 del and 21 L858R mutational subtypes was more challenging using PET/CT radiomics.

호흡곤란환자의 입-퇴원 분석을 위한 규칙가중치 기반 퍼지 분류모델 (Rule Weight-Based Fuzzy Classification Model for Analyzing Admission-Discharge of Dyspnea Patients)

  • 손창식;신아미;이영동;박형섭;박희준;김윤년
    • 대한의용생체공학회:의공학회지
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    • 제31권1호
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    • pp.40-49
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    • 2010
  • A rule weight -based fuzzy classification model is proposed to analyze the patterns of admission-discharge of patients as a previous research for differential diagnosis of dyspnea. The proposed model is automatically generated from a labeled data set, supervised learning strategy, using three procedure methodology: i) select fuzzy partition regions from spatial distribution of data; ii) generate fuzzy membership functions from the selected partition regions; and iii) extract a set of candidate rules and resolve a conflict problem among the candidate rules. The effectiveness of the proposed fuzzy classification model was demonstrated by comparing the experimental results for the dyspnea patients' data set with 11 features selected from 55 features by clinicians with those obtained using the conventional classification methods, such as standard fuzzy classifier without rule weights, C4.5, QDA, kNN, and SVMs.

Capsaicin으로 유도된 아토피 피부염 rat model에서 선태의 효과 (The effect of Periostracum Cicadae on capsaicin-induced model of atopic dermatitis in rats)

  • 장유진;정달림;홍승욱
    • 한방안이비인후피부과학회지
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    • 제28권4호
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    • pp.41-50
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    • 2015
  • Objectives : 선태는 아토피 피부염에서 소양증 완화를 위해 사용되고 있다. 본 연구에서는 면역계 및 신경계 손상을 일으킨 rat model에서 선태 추출물이 소양증 완화에 효과가 있는지 알아보고자 한다.Methods : 출생 48시간 이내의 rat을 대상으로, capsaicin(50 mg/kg)을 피하 투여하였다. 임의로 선정된 12마리의 실험군에 3주 동안 선태 추출물(0.5g/kg)을 매일 경구 투여하였다. 이후 scratching behavior 와 dermatitis score를 측정하였다.Results : 선태 투여군과 대조군에서 scratching number 와 dermatitis score의 차이가 없었다.Conclusions : 위의 결과로부터 capsaicin으로 유발한 아토피 피부염 rat model에서 선태의 소양증 완화 효과가 없다는 것을 알 수 있었다. 아토피 피부염의 효과적인 치료를 위해 면역계 뿐만 아니라 신경계 손상 회복시키는 약물을 찾기 위한 더 많은 연구가 필요할 것으로 생각된다.

Comparison of Pre-processed Brain Tumor MR Images Using Deep Learning Detection Algorithms

  • Kwon, Hee Jae;Lee, Gi Pyo;Kim, Young Jae;Kim, Kwang Gi
    • Journal of Multimedia Information System
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    • 제8권2호
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    • pp.79-84
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    • 2021
  • Detecting brain tumors of different sizes is a challenging task. This study aimed to identify brain tumors using detection algorithms. Most studies in this area use segmentation; however, we utilized detection owing to its advantages. Data were obtained from 64 patients and 11,200 MR images. The deep learning model used was RetinaNet, which is based on ResNet152. The model learned three different types of pre-processing images: normal, general histogram equalization, and contrast-limited adaptive histogram equalization (CLAHE). The three types of images were compared to determine the pre-processing technique that exhibits the best performance in the deep learning algorithms. During pre-processing, we converted the MR images from DICOM to JPG format. Additionally, we regulated the window level and width. The model compared the pre-processed images to determine which images showed adequate performance; CLAHE showed the best performance, with a sensitivity of 81.79%. The RetinaNet model for detecting brain tumors through deep learning algorithms demonstrated satisfactory performance in finding lesions. In future, we plan to develop a new model for improving the detection performance using well-processed data. This study lays the groundwork for future detection technologies that can help doctors find lesions more easily in clinical tasks.