• 산업경영시스템학회지
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• 제46권3호
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• pp.161-169
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• 2023

Degenerative arthritis is a common joint disease that affects many elderly people and is typically diagnosed through radiography. However, the need for remote diagnosis is increasing because knee pain and walking disorders caused by degenerative arthritis make face-to-face treatment difficult. This study collects three-dimensional joint coordinates in real time using Azure Kinect DK and calculates 6 gait features through visualization and one-way ANOVA verification. The random forest classifier, trained with these characteristics, classified degenerative arthritis with an accuracy of 97.52%, and the model's basis for classification was identified through classification algorithm by features. Overall, this study not only compensated for the shortcomings of existing diagnostic methods, but also constructed a high-accuracy prediction model using statistically verified gait features and provided detailed prediction results.

• 간호행정학회지
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• 제23권2호
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• pp.211-222
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• 2017

Purpose: To develop staffing levels for nursing personnel (registered nurses and nursing assistants) to provide inpatients with integrated nursing care that includes, in addition to professional nursing care, personal care previously provided by patients' families or private caregivers. Methods: A time & motion study was conducted to observe nursing care activities and the time spent by nursing personnel, families, and private caregivers in 10 medical-surgical units. The Korean Patient Classification System-1 (KPCS-1) was used for the nurse manager survey conducted to measure staffing levels and patient needs for nursing care. Results: Current nurse to patient ratios from the time-motion study and the survey study were 1:10 and 1:11, respectively. Time spent in direct patient care by nursing personnel and family/private caregivers was 51 and 130 minutes per day, respectively. Direct nursing care hours correlated with KPCS-1 scores. Nursing personnel to patient ratio required to provide integrated inpatient care ranged from 1:3.9 to 1:6.1 in tertiary hospitals and from 1:4.4 to 1:6.0 in general hospitals. The functional nursing care delivery system had been implemented in 38.5% of the nursing units. Conclusion: Findings indicate that appropriate nurse staffing and efficient nursing care delivery systems are required to provide integrated inpatient nursing care.

• 한국보건간호학회지
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• 제1권1호
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• pp.45-61
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• 1987

This Study was an attempt to estimate the optimum numbers of Operating Room Nursing Manpower by measuring the amount of service hours required by the patients in Operating Room in relation to the service amount actually provided by the nurses. The major concern of this study was placed on the measurement of Nursing Service Requirements by using the Operating Room (O. R) Patient Acuity System recently developed by M. M. Hart to classify the O. R. patients into four groups according to the degree of the complexity of operative procedure and some other elements which increase nursing activities in respect of patient care; Acuity IV group is the one requires nursing services most, on the other hand Acuity I requires least. nu sing The objectives of this study were as follows; 1. To analyze functions of the nursing personnel in O. R. by time unit and to estimate the average time a nurse can activate for productive functions. 2. To measure the actual amount of nursing times provided by nurses to the surgical patients. 3. To develop a patient classification system in order to measure the amount of Nursing services required by the patients. 4. To calculate an appropriate number of nursing manpower to meet the needs of the patients. In order to conduct the research both selected nurses and patients in 'S' University Hospital were Studied by utilizing the O. R. Patient Acuity System as well as the Classification Chart developed by Association of Operating Room Nurses (A. O. R. N) as a means of classifying functions of O. R. nurses. That is; Functions of the 10 selected O. R. nurses observed during the period of June 30 to July 4, 1986, whereas the amount of nursing services required by or provided to the 974 patients who had received surgeries during the period of June 9 to July 4, 1986. The results of this study were as follows; 1) The actual working hours per a nurse averaged 6.7 hours a day. 2) Each nurse's daily routine schedule consists of $71.4\%$ for Technical Functions, $16.1\%$ for Nonprodective Functions, $6.6\%$ for Assessment and Evaluation, $3.9\%$ for Overseeing and Supervision and the rest $2.0\%$ for Patient Preparation respectively. 3) Preoperative waiting time per a patient was 24.1 minutes on the average; for the first case was 10.7 minutes, whereas for the following cases was 32.0 minutes. 4) Total Operation time for the 974 patients during the period of observation for this study amounted to 2759.6 hours, weekly hour was equivalent to 689.9 hours, Whereas daily operation time averaged 130 hours. Meanwhile the average operation time per patient was 2.8 hours ; for the case of Acuity IV was 5.6 hours, 5. 1 hours for the case of Acuity III, 2.3 hours for Acuity II and 1.1 hours for Acuity I. 5) According to the O. R. Patient Acuity System, $64.5\%$ of the whole patients belonged to Acuity II, $23.7\%$ to Acuity III, 11. $3\%$ to Acuity IV and $0.7\%$ to Acuity I respectively. 6) Required amount of nursing times based on the preoperative waiting time and operation time was 7167.8 person hours, which showed that $5.5\%$ of them needed for preoperative nursing care, whereas the rest $94.5\%$ for intraoperative nursing care. In terms of the O. R. Patient Acuity System, $49.7\%$ of total nursing service requirements was needed for Acuity II patients, $27.4\%$ for Acuity III patients, $17.2\%$ for Acuity IV patients and $0.2\%$ for Acuity I patients. 7) The rate of the nursing services provided against the required nursing times was about $81.4\%$ on the average; some departments, like those of Plastic Surgery, Otolaryngology and Ophthalmology whose patients mostly belonged to Acuity II recorded hegher provision rate than average, whereas other departments of Thoracic Surgery. Neurosurgery and Orthopedic Surgery whose patients belonged to Acuity III and Acuity IV as well as Acuity II recorded lower provision rate than average. 8) Subsequently, required numbers of nursing manpower was 10.7 nurses additionally. Based on the above findings the following recommendations will be made; 1) this study recommends, develops. and adopts an accurate and realistic O. R. Patient Acuity System which can help measure the nursing service requirements objectively to elicit the rationales of allocation of nursing personnels. 2) this study proposes storongly place nurses who take the role of preoperative nursing care exclusively for the waiting patients in O. R. and shortening their waiting time by close communication between the designated O. R. and the ward.

• 대한간호학회지
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• 제34권2호
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• pp.278-289
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• 2004

Purpose: The purpose of this study was to classify the elderly in long-term care facilities using the Resource Utilization Group(RUG-III) and to examine the feasibility of a payment method based on the RUG-III classification system in Korea. Method: This study measured resident characteristics using a Resident Assessment Instrument-Minimum Data Set(RAI-MDS) and staff time. Data was collected from 530 elderly residents over sixty, residing in long-term care facilities. Resource use for individual patients was measured by a wage-weighted sum of staff time and the total time spent with the patient by nurses, aides, and physiotherapists. Result: The subjects were classified into 4 groups out of 7 major groups. The group of Clinically Complex was the largest (46.3%), and then Reduced Physical Function(27.2%), Behavior Problems (17.0%), and Impaired Cognition (9.4%) followed. Homogeneity of the RUG-III groups was examined by total coefficient of variation of resource use. The results showed homogeneity of resource use within RUG-III groups. Also, the difference in resource use among RUG major groups was statistically significant (p<0.001), and it also showed a hierarchy pattern as resource use increases in the same RUG group with an increase of severity levels(ADL). Conclusion: The results of this study showed that the RUG-Ill classification system differentiates resources provided to elderly in long-term care facilities in Korea.

• Journal of Korean Neurosurgical Society
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• 제58권5호
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• pp.448-453
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• 2015

Objective : Recently, the survival of patients with hepatocellular carcinoma (HCC) has been prolonged with improvements in various diagnostic tools and medical treatment modalities. Consequently, spine metastases from HCC are being diagnosed more frequently. The accurate prediction of prognosis plays a critical role in determining a patient's treatment plan, including surgery for patients with spinal metastases of HCC. We investigated the clinical features, surgical outcomes, and prognostic factors of HCC presenting with spine metastases, in patients who underwent surgery. Methods : A retrospective review was conducted on 33 HCC patients who underwent 36 operations (three patients underwent surgical treatment twice) from February 2006 to December 2013. The median age of the patients was 56 years old (range, 28 to 71; male : female=30 : 3). Results : Overall survival was not correlated with age, sex, level of metastases, preoperative Child-Pugh classification, preoperative ambulatory function, preoperative radiotherapy, type of operation, administration of Sorafenib, or the Tokuhashi scoring system. Only the Tomita scoring system was shown to be an independent prognostic factor for overall survival. Comparing the Child-Pugh classification and ambulatory ability, there were no statistically differences between patients pre- and post-operatively. Conclusion : The Tomita scoring system represents a practicable and highly predictive prognostic tool. Even though surgical intervention may not restore ambulatory function, it should be considered to prevent deterioration of the patient's overall condition. Additionally, aggressive management may be needed if there is any ambulatory ability remaining.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.84.4-84
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• 2001

In this paper, we present a method to classify electromyogram (EMG) signals which are utilized to be control signals for patient-responsive walker-supported system for paraplegics. Patterns of EMG signals for dierent walking motions are classied via adequate filtering, real EMG signal extraction, AR-modeling, and modified self-organizing feature map (MSOFM). More efficient signal processing is done via a data-reducing extraction algorithm. Moreover, MSOFM classifies and determines the classified results are presented for validation.

• Tuberculosis and Respiratory Diseases
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• 제78권3호
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• pp.180-189
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• 2015

Accurate lymph node staging of lung cancer is crucial in determining optimal treatment plans and predicting patient outcome. Currently used lymph node maps have been reconciled to the internationally accepted International Association for the Study of Lung Cancer (IASLC) map published in the seventh edition of TNM classification system of malignant tumours. This article provides computed tomographic illustrations of the IASLC nodal map, to facilitate its application in day-to-day clinical practice in order to increase the appropriate classification in lung cancer staging.

• Genomics & Informatics
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• 제17권3호
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• pp.30.1-30.4
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• 2019

Neuroblastoma is a major cause of cancer death in early childhood, and its timely and correct diagnosis is critical. Gene expression datasets have recently been considered as a powerful tool for cancer diagnosis and subtype classification. However, no attempts have yet been made to apply deep learning using gene expression to neuroblastoma classification, although deep learning has been applied to cancer diagnosis using image data. Taking the International Neuroblastoma Staging System stages as multiple classes, we designed a deep neural network using the gene expression patterns and stages of neuroblastoma patients. Despite a small patient population (n = 280), stage 1 and 4 patients were well distinguished. If it is possible to replicate this approach in a larger population, deep learning could play an important role in neuroblastoma staging.

• 한국산학기술학회논문지
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• 제20권11호
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• pp.324-334
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• 2019

본 연구는 일개 요양병원 입원 환자들의 의무기록 정보를 바탕으로 환자분류군에 따른 입원 환자 특성을 파악하고, 각 요인 간의 상관 관계를 분석하여 요양병원 환자분류 체계 개선에 필요한 기초 자료를 제공하고자 시행하였다. 연구 대상은 2016년 1월부터 12월까지 1년간 전북 지역 일개 요양병원에서 퇴원한 환자들의 의무기록 정보 총 213건으로 선정하였다. 재원일수와 상병 개수의 상관계수는 양의 상관 관계를 보여 환자가 가지고 있는 상병이 많을수록 재원일수가 길어지고 있음을 알 수 있었다. 이와 같은 연구 결과를 기반으로 환자분류군을 결정하는 환자평가표의 항목들을 실제로 환자에게 제공되는 의학적인 노력이 반영될 수 있도록 수정·보완하는 것이 필요하다. 또한 각 환자분류군별로 중점적으로 관리해야 할 문제점을 파악하여 각 분류군에 적합한 케어 서비스 체계를 수립하는 것이 효율적인 요양병원 운영을 위한 필수 요소이자 나아가 국가적 차원에서도 노인 인구의 건강을 관리하는 데 중요한 과제임을 알 수 있다.

• 대한의용생체공학회:의공학회지
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• 제40권2호
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• pp.62-67
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• 2019

The purpose of this study was to evaluate the performance of deep neural network model in order to determine whether there is a risk factor for coronary artery disease based on the cardiac variation parameter. The study used unidentifiable 297 data to evaluate the performance of the model. Input data consists of heart rate parameters, which are SDNN (standard deviation of the N-N intervals), PSI (physical stress index), TP (total power), VLF (very low frequency), LF (low frequency), HF (high frequency), RMSSD (root mean square of successive difference) APEN (approximate entropy) and SRD (successive R-R interval difference), the age group and sex. Output data are divided into normal and patient groups, and the patient group consists of those diagnosed with diabetes, high blood pressure, and hyperlipidemia among the various risk factors that can cause coronary artery disease. Based on this, a binary classification model was applied using Deep Neural Network of deep learning techniques to classify normal and patient groups efficiently. To evaluate the effectiveness of the model used in this study, Kernel SVM (support vector machine), one of the classification models in machine learning, was compared and evaluated using same data. The results showed that the accuracy of the proposed deep neural network was train set 91.79% and test set 85.56% and the specificity was 87.04% and the sensitivity was 83.33% from the point of diagnosis. These results suggest that deep learning is more efficient when classifying these medical data because the train set accuracy in the deep neural network was 7.73% higher than the comparative model Kernel SVM.