• Title/Summary/Keyword: medical intelligence system

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A Study on the Knowledge-Based T.P.N. System (1) (지식 구조화 경정맥 완전 영양공급 시스템의 개발에 관한 연구 (I))

  • Jeon, Gye-Rok;Choe, Sam-Gil;Byeon, Geon-Sik
    • Journal of Biomedical Engineering Research
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    • v.11 no.2
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    • pp.305-314
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    • 1990
  • In this paper we have implemented and tested TPN which is system to supply sufficent nutrition to nutritionally deficient patient by means of ES (expert system) a kind of A.1 (artificial intelligence) . This system affords to evaluation of nutritional state of patient which is essential to physi- cian. who performs TPN, decision of performing TPN and management of patient-data & calculation of information needing to making TPN fluid. The features were as follolv 1. we input data, take ideal weight of patient and 24hr's creatlnln In urine according to chart in system compare TSF (triceps skin fold), MAC (mid-arm circumference), AMC (arm muscle circumference) to 5th, 15th, 50th percentile and evaluate the nutritional state of patient. 2. Calculation of protein & nonprotein calorie needing to treament of patient can be made exactly by stress factor, activity factor and body temperature. 3. patient's personal recording needing to management of patient date name of chief doc- tor, name of department of admission, chart number, history can by taken very easily. 4. The way of system operating is pull-down Menu one, It can be processing very efficiently. 5. Date processing in system, we can manage memory volume of computer verlr efficiently using of dynamic allocation variables. 6. We can make it very easy to edit & revise the input data, processed data is saved to diskette in 2 files (TDF, THF) , these are semipermanent preservation.

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An Intelligent Self Health Diagnosis System using FCM Algorithm and Fuzzy Membership Degree (FCM 알고리즘과 퍼지 소속도를 이용한 지능형 자가 진단 시스템)

  • Kim, Kwang-Baek;Kim, Ju-Sung
    • Journal of Intelligence and Information Systems
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    • v.13 no.1
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    • pp.81-90
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    • 2007
  • This paper shows an intelligent disease diagnosis system for public. Our system deals with 30 diseases and their typical symptoms selected based on the report from Ministry of Health and Welfare, Korea. Technically, the system uses a modified FCM algorithm for clustering diseases and the input vector consists of the result of user-selected questionnaires. The modified FCM algorithm improves the quality of clusters by applying symmetrically measure based on the fuzzy theory so that the clusters are relatively sensitive to the shape of the pattern distribution. Furthermore, we extract the highest 5 diseases only related to the user-selected questionnaires based on the fuzzy membership function between questionnaires and diseases in order to avoid diagnosing unrelated disease.

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Implementation of Intelligence Pulse Wave Detection System (지능형 맥진기 구현)

  • Hong, Y.S.;Yu, J.S.;Chang, S.J.;Sun, S.H.;Lee, W.B.;Nam, D.H.;Yu, M.S.;Choi, M.B.;Lee, S.S.
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.13 no.2
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    • pp.245-254
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    • 2013
  • In oriental medicine, it is possible to classify and treat many diseases using the pulse wave detection system. Other problems may arise. As it is a very subjective way to analyze the pulse wave. One problem of the conventional pulse wave detection system is that the arterial pulse sensor is not located correctly at the radial artery. Threrefore measurement results can differ depending on the measurement position and the measurement procedure. This is mostly due to it's sensitivity to high reproducibility. In order to solve this problem this paper proposes an algorithm to analyze the weak pulse wave symptom and strong pulse wave symptom. It uses the portable pulse wave detection system which includes a Hall Sensor. As a final result, it analyzed the weak pulse wave symptom and strong pulse wave symptom by the SPSS statistics technique. It proves that N time (notch point time) and S Amp (rise waveform size) mean values are significantly different in 95% confidence interval.

A Study of the Leader's Traits on the heirarchy of Nurse managers (간호관리자의 계층에 따른 지도자 특성에 관한 연구)

  • Hwang, Sung-Woo
    • Journal of Korean Academy of Nursing Administration
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    • v.4 no.1
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    • pp.5-17
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    • 1998
  • The Purpose of this study is to find out differences among the leader's traits on the hierarchy of Nurse managers in Nurse system of the hospital. In this study 152 managers over head nurse working in 6 University hospitals and 5 general hospitals were selected and the questionary paper answered by them was collected from 1st to 30th in September in 1997. The measuring instrument used in this study is the one integrated and classified by Stogdill (1981), which nurse professor and 2 students of the master's course translated and modified with myself. And its validity was verified through making a test on 130 nurses. The measuring instrument used in this study is made up of 4 items about physical characteristics, 3 items about social background, 4 items about intelligence and ability, 17 items about personaity, 6 items about task-related characteristics, 9 items about social characteristics and 8 items about general background in the triats of leader. And this instrument is made to be marked using five point Likert type. It's reliability is Cronbach's Alpha =.93. The data for study were analyzed through SPSS/PC+ The result of this study are as follows: 1. The order in importantly perceptible degree of the leader's traits showed like these: the intelligence and ability (M=4.683), the task-related characteristics (M=4.605), the personality (M=4.39), the social characteristics (M=4.327), the social back-ground (M=4.056), the physical characteristics (M=3.601). 2. The order in degree to percept the importance of 44 detailed items of the leader's traits showed like these: the judgement and decisiveness (M=4.967), the sense of responsibility (M=4.904), the activity and energy (M=4.796), the self-confidence (M=4.776), the creativity (M=4.748), the intelligence (M=4.743), the responsibility in the pursuit of objectives (M=4.743), the enthusiasm (M=4.717), the objectivity (M=4.704), the moral sense and ethical conduct (M=4.704), the ability to enlist cooperation (M=4.694), the strength of conviction (M =4.678), the enterprise (M=4.691), the administrative ability (M=4.678) and the cooperativeness (M=4.638) 3. As the result of analyzing the leader's trait differences on the hierarchy of nurse managers in six factors of the leader's traits, the social background showed the meaningful differences(F=4.983, P=0.008). 4. As the result of analyzing the leader's traits defferences made from the upper first to 15th rank among the detailed items of the leader's trait factors on the heirarchy of nurse managers, the meaningful defferences appeared in the following items: the objectivity(F=3.413, P=0.033), the creativity (F=3.550, P=0.031), the sense of responsibility(F=3.345, P=0.049), and the administrative ability (F=3.363, P=0.037). 5. As the result of analyzing the leader's trait factors in general background, only the social background of 6 leader's trait factors showed the meaningful differences according to the working place (F=4.057, P=0.008). The study shows that we should consider the above leader's trait factors in selecting nurse managers and that we should develop the educational program for hierarchy of nurse managers urgently.

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Conventional Versus Artificial Intelligence-Assisted Interpretation of Chest Radiographs in Patients With Acute Respiratory Symptoms in Emergency Department: A Pragmatic Randomized Clinical Trial

  • Eui Jin Hwang;Jin Mo Goo;Ju Gang Nam;Chang Min Park;Ki Jeong Hong;Ki Hong Kim
    • Korean Journal of Radiology
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    • v.24 no.3
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    • pp.259-270
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    • 2023
  • Objective: It is unknown whether artificial intelligence-based computer-aided detection (AI-CAD) can enhance the accuracy of chest radiograph (CR) interpretation in real-world clinical practice. We aimed to compare the accuracy of CR interpretation assisted by AI-CAD to that of conventional interpretation in patients who presented to the emergency department (ED) with acute respiratory symptoms using a pragmatic randomized controlled trial. Materials and Methods: Patients who underwent CRs for acute respiratory symptoms at the ED of a tertiary referral institution were randomly assigned to intervention group (with assistance from an AI-CAD for CR interpretation) or control group (without AI assistance). Using a commercial AI-CAD system (Lunit INSIGHT CXR, version 2.0.2.0; Lunit Inc.). Other clinical practices were consistent with standard procedures. Sensitivity and false-positive rates of CR interpretation by duty trainee radiologists for identifying acute thoracic diseases were the primary and secondary outcomes, respectively. The reference standards for acute thoracic disease were established based on a review of the patient's medical record at least 30 days after the ED visit. Results: We randomly assigned 3576 participants to either the intervention group (1761 participants; mean age ± standard deviation, 65 ± 17 years; 978 males; acute thoracic disease in 472 participants) or the control group (1815 participants; 64 ± 17 years; 988 males; acute thoracic disease in 491 participants). The sensitivity (67.2% [317/472] in the intervention group vs. 66.0% [324/491] in the control group; odds ratio, 1.02 [95% confidence interval, 0.70-1.49]; P = 0.917) and false-positive rate (19.3% [249/1289] vs. 18.5% [245/1324]; odds ratio, 1.00 [95% confidence interval, 0.79-1.26]; P = 0.985) of CR interpretation by duty radiologists were not associated with the use of AI-CAD. Conclusion: AI-CAD did not improve the sensitivity and false-positive rate of CR interpretation for diagnosing acute thoracic disease in patients with acute respiratory symptoms who presented to the ED.

Use of Artificial Intelligence for Reducing Unnecessary Recalls at Screening Mammography: A Simulation Study

  • Yeon Soo Kim;Myoung-jin Jang;Su Hyun Lee;Soo-Yeon Kim;Su Min Ha;Bo Ra Kwon;Woo Kyung Moon;Jung Min Chang
    • Korean Journal of Radiology
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    • v.23 no.12
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    • pp.1241-1250
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    • 2022
  • Objective: To conduct a simulation study to determine whether artificial intelligence (AI)-aided mammography reading can reduce unnecessary recalls while maintaining cancer detection ability in women recalled after mammography screening. Materials and Methods: A retrospective reader study was performed by screening mammographies of 793 women (mean age ± standard deviation, 50 ± 9 years) recalled to obtain supplemental mammographic views regarding screening mammography-detected abnormalities between January 2016 and December 2019 at two screening centers. Initial screening mammography examinations were interpreted by three dedicated breast radiologists sequentially, case by case, with and without AI aid, in a single session. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and recall rate for breast cancer diagnosis were obtained and compared between the two reading modes. Results: Fifty-four mammograms with cancer (35 invasive cancers and 19 ductal carcinomas in situ) and 739 mammograms with benign or negative findings were included. The reader-averaged AUC improved after AI aid, from 0.79 (95% confidence interval [CI], 0.74-0.85) to 0.89 (95% CI, 0.85-0.94) (p < 0.001). The reader-averaged specificities before and after AI aid were 41.9% (95% CI, 39.3%-44.5%) and 53.9% (95% CI, 50.9%-56.9%), respectively (p < 0.001). The reader-averaged sensitivity was not statistically different between AI-unaided and AI-aided readings: 89.5% (95% CI, 83.1%-95.9%) vs. 92.6% (95% CI, 86.2%-99.0%) (p = 0.053), although the sensitivities of the least experienced radiologists before and after AI aid were 79.6% (43 of 54 [95% CI, 66.5%-89.4%]) and 90.7% (49 of 54 [95% CI, 79.7%-96.9%]), respectively (p = 0.031). With AI aid, the reader-averaged recall rate decreased by from 60.4% (95% CI, 57.8%-62.9%) to 49.5% (95% CI, 46.5%-52.4%) (p < 0.001). Conclusion: AI-aided reading reduced the number of recalls and improved the diagnostic performance in our simulation using women initially recalled for supplemental mammographic views after mammography screening.

Study on a Methodology for Developing Shanghanlun Ontology (상한론(傷寒論)온톨로지 구축 방법론 연구)

  • Jung, Tae-Young;Kim, Hee-Yeol;Park, Jong-Hyun
    • Journal of Physiology & Pathology in Korean Medicine
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    • v.25 no.5
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    • pp.765-772
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    • 2011
  • Knowledge which is represented by formal logic are widely used in many domains such like artificial intelligence, information retrieval, e-commerce and so on. And for medical field, medical documentary records retrieval, information systems in hospitals, medical data sharing, remote treatment and expert systems need knowledge representation technology. To retrieve information intellectually and provide advanced information services, systematically controlled mechanism is needed to represent and share knowledge. Importantly, medical expert's knowledge should be represented in a form that is understandable to computers and also to humans to be applied to the medical information system supporting decision making. And it should have a suitable and efficient structure for its own purposes including reasoning, extendability of knowledge, management of data, accuracy of expressions, diversity, and so on. we call it ontology which can be processed with machines. We can use the ontology to represent traditional medicine knowledge in structured and systematic way with visualization, then also it can also be used education materials. Hence, the authors developed an Shanghanlun ontology by way of showing an example, so that we suggested a methodology for ontology development and also a model to structure the traditional medical knowledge. And this result can be used for student to learn Shanghanlun by graphical representation of it's knowledge. We analyzed the text of Shanghanlun to construct relational database including it's original text, symptoms and herb formulars. And then we classified the terms following some criterion, confirmed the structure of the ontology to describe semantic relations between the terms, especially we developed the ontology considering visual representation. The ontology developed in this study provides database showing fomulas, herbs, symptoms, the name of diseases and the text written in Shanghanlun. It's easy to retrieve contents by their semantic relations so that it is convenient to search knowledge of Shanghanlun and to learn it. It can display the related concepts by searching terms and provides expanded information with a simple click. It has some limitations such as standardization problems, short coverage of pattern(證), and error in chinese characters input. But we believe this research can be used for basic foundation to make traditional medicine more structural and systematic, to develop application softwares, and also to applied it in Shanghanlun educations.

Intelligent Robust Base-Station Research in Harsh Outdoor Wilderness Environments for Wildsense

  • Ahn, Junho;Mysore, Akshay;Zybko, Kati;Krumm, Caroline;Lee, Dohyeon;Kim, Dahyeon;Han, Richard;Mishra, Shivakant;Hobbs, Thompson
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.15 no.3
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    • pp.814-836
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    • 2021
  • Wildlife ecologists and biologists recapture deer to collect tracking data from deer collars or wait for a drop-off of a deer collar construction that is automatically detached and disconnected. The research teams need to manage a base camp with medical trailers, helicopters, and airplanes to capture deer or wait for several months until the deer collar drops off of the deer's neck. We propose an intelligent robust base-station research with a low-cost and time saving method to obtain recording sensor data from their collars to a listener node, and readings are obtained without opening the weatherproof deer collar. We successfully designed the and implemented a robust base station system for automatically collecting data of the collars and listener motes in harsh wilderness environments. Intelligent solutions were also analyzed for improved data collections and pattern predictions with drone-based detection and tracking algorithms.

A Perspective on Surgical Robotics and Its Future Directions for the Post-COVID-19 Era (포스트 코로나 시대 수술 로봇의 역할 및 발전 방향에 관한 전망)

  • Jang, Haneul;Song, Chaehee;Ryu, Seok Chang
    • The Journal of Korea Robotics Society
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    • v.16 no.2
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    • pp.172-178
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    • 2021
  • The COVID-19 pandemic has been reshaping the world by accelerating non-contact services and technologies in various domains. Hospitals as a healthcare system lie at the center of the dramatic change because of their fundamental roles: medical diagnosis and treatments. Leading experts in health, science, and technologies have predicted that robotics and artificial intelligence (AI) can drive such a hospital transformation. Accordingly, several government-led projects have been developed and started toward smarter hospitals, where robots and AI replace or support healthcare personnel, particularly in the diagnosis and non-surgical treatment procedures. This article inspects the remaining element of healthcare services, i.e., surgical treatment, focusing on evaluating whether or not currently available laparoscopic surgical robotic systems are sufficiently preparing for the era of post-COVID-19 when contactless is the new normal. Challenges and future directions towards an effective, fully non-contact surgery are identified and summarized, including remote surgery assistance, domain-expansion of robotic surgery, and seamless integration with smart operating rooms, followed by emphasis on robot tranining for surgical staff.

Data Augmentation Techniques of Power Facilities for Improve Deep Learning Performance

  • Jang, Seungmin;Son, Seungwoo;Kim, Bongsuck
    • KEPCO Journal on Electric Power and Energy
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    • v.7 no.2
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    • pp.323-328
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    • 2021
  • Diagnostic models are required. Data augmentation is one of the best ways to improve deep learning performance. Traditional augmentation techniques that modify image brightness or spatial information are difficult to achieve great results. To overcome this, a generative adversarial network (GAN) technology that generates virtual data to increase deep learning performance has emerged. GAN can create realistic-looking fake images by competitive learning two networks, a generator that creates fakes and a discriminator that determines whether images are real or fake made by the generator. GAN is being used in computer vision, IT solutions, and medical imaging fields. It is essential to secure additional learning data to advance deep learning-based fault diagnosis solutions in the power industry where facilities are strictly maintained more than other industries. In this paper, we propose a method for generating power facility images using GAN and a strategy for improving performance when only used a small amount of data. Finally, we analyze the performance of the augmented image to see if it could be utilized for the deep learning-based diagnosis system or not.