• Journal of Institute of Control, Robotics and Systems
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• v.20 no.3
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• pp.364-371
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• 2014

The purpose of this paper is to review recent developments in lower limb exoskeletons. The exoskeleton system is a human-robot cooperation system that enhances the performance of the wearer in various environments while the human operator is in charge of the position control, contextual perception, and motion signal generation through the robot's artificial intelligence. This system is in the form of a mechanical structure that is combined to the exterior of a human body to improve the muscular power of the wearer. This paper is followed by an overview of the development history of exoskeleton systems and their three main applications in military/industrial field, medical/rehabilitation field and social welfare field. Besides the key technologies in exoskeleton systems, the research is presented from several viewpoints of the exoskeleton mechanism, human-robot interface and human-robot cooperation control.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.2
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• pp.213-218
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• 2011

In this paper, we have developed a tongue diagnosis system using fuzzy rules. A healthy person's tongue is red in color and has less tongue coating. However, when a person suffers from a disease, the color of their tongue changes from red to white, blue, or black. Therefore, it can analyze patient's health if analyze color and coated tongue of tongue. Medical diagnosis system can automatically determines the symptoms of the disease of a patient and their and calculate the optimal acupuncture time on the basis of the patient's physical conditions, illness conditions, and age from any place and at any time. The computer simulation results have shown that electro-acupuncture administered by using the medical diagnosis system developed in this study is more effective than the conventional method.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.11-23
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• 2024

Triage is a practice of accurately prioritizing patients in emergency department (ED) based on their medical condition to provide them with proper treatment service. The variation in triage assessment among medical staff can cause mis-triage which affect the patients negatively. Developing ED triage system based on machine learning (ML) techniques can lead to accurate and efficient triage outcomes. This study aspires to develop a triage system using machine learning techniques to predict ED triage levels using patients' information. We conducted a retrospective study using Security Forces Hospital ED data, from 2021 through 2023 during Hajj period in Saudia Arabi. Using demographics, vital signs, and chief complaints as predictors, two machine learning models were investigated, naming gradient boosted decision tree (XGB) and deep neural network (DNN). The models were trained to predict ED triage levels and their predictive performance was evaluated using area under the receiver operating characteristic curve (AUC) and confusion matrix. A total of 11,584 ED visits were collected and used in this study. XGB and DNN models exhibit high abilities in the predicting performance with AUC-ROC scores 0.85 and 0.82, respectively. Compared to the traditional approach, our proposed system demonstrated better performance and can be implemented in real-world clinical settings. Utilizing ML applications can power the triage decision-making, clinical care, and resource utilization.

• Progress in Medical Physics
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• v.31 no.3
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• pp.111-123
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• 2020

Deep learning (DL) is a subset of machine learning and artificial intelligence that has a deep neural network with a structure similar to the human neural system and has been trained using big data. DL narrows the gap between data acquisition and meaningful interpretation without explicit programming. It has so far outperformed most classification and regression methods and can automatically learn data representations for specific tasks. The application areas of DL in radiation oncology include classification, semantic segmentation, object detection, image translation and generation, and image captioning. This article tries to understand what is the potential role of DL and what can be more achieved by utilizing it in radiation oncology. With the advances in DL, various studies contributing to the development of radiation oncology were investigated comprehensively. In this article, the radiation treatment process was divided into six consecutive stages as follows: patient assessment, simulation, target and organs-at-risk segmentation, treatment planning, quality assurance, and beam delivery in terms of workflow. Studies using DL were classified and organized according to each radiation treatment process. State-of-the-art studies were identified, and the clinical utilities of those researches were examined. The DL model could provide faster and more accurate solutions to problems faced by oncologists. While the effect of a data-driven approach on improving the quality of care for cancer patients is evidently clear, implementing these methods will require cultural changes at both the professional and institutional levels. We believe this paper will serve as a guide for both clinicians and medical physicists on issues that need to be addressed in time.

• Journal of Preventive Medicine and Public Health
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• v.22 no.1 s.25
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• pp.57-64
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• 1989

A decision support system (DSS) was developed to support doctor's decision-making in diagnosing hearing loss. The final diagnosis encompassed 41 diseases with the problem of hearing loss. The system was developed by integrating model-oriented DSS technique and artificial intelligence technology. The system can be used as both diagnosis tool and teaching tool for medical students. Furthermore, the AI technology obtained from this study may also be used in developing DSS for hospital management.

• 대한상한금궤의학회지
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• v.14 no.1
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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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.199-208
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• 2014

At present, the effect of oriental treatment system is proved in the west and using the data of tongue and pulse of body, the doctor can decide the patient's body state without Xray and CT data of large machines. In this paper, the patient's medical data is transmitted to the doctor and the real time decision algorithm is developed and so the doctor can decide the medical treatments. Using the mobile phone, the pulse data and bio data can be sent to the doctor and therefore the patients, who can't care in real time, can be treated in real time in the impossible medical treatment areas. Therefore in this paper, the oriental medical treatment system algorithm and artificial intelligence electrical needle simulation are processed for real time and checked and treated, so anyone can decide patient's state using mobile phone.

• The Journal of the Acoustical Society of Korea
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• v.41 no.5
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• pp.534-544
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• 2022

Medical ultrasound system has been widely used to visualize the lesion for diagnostics in most medical service site including hospitals and clinics thanks to its advantages such as real time operation, ease of use, safety. Among many signal processing blocks of the system, one of the most important part that governs the image quality is the beamformer, and technologies for this part has been continuously developed in long time. The synthetic aperture imaging method, that is one of the major technologies of beamforming, was introduced to maximize utilizing the information delivered from the patient's body through the probe, and contributed to breakthrough of the image quality since it was introduced in around 1990's, and evolved continuously in decades. This paper reviews and surveys the process of development of this technology and expects future evolution.

• Journal of Information Technology Services
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• v.18 no.2
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• pp.123-141
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• 2019

The goal of this study is to review the major research trend on the convergence studies of AI and healthcare technologies. For the study, 15,260 English articles on AI and healthcare related topics were collected from Scopus for 55 years from 1963, and text mining techniques were conducted. As a result, seven key research topics were defined : "AI for Clinical Decision Support System (CDSS)", "AI for Medical Image", "Internet of Healthcare Things (IoHT)", "Big Data Analytics in Healthcare", "Medical Robotics", "Blockchain in Healthcare", and "Evidence Based Medicine (EBM)". The result of this study can be utilized to set up and develop the appropriate healthcare R&D strategies for the researchers and government. In this study, text mining techniques such as Text Analysis, Frequency Analysis, Topic Modeling on LDA (Latent Dirichlet Allocation), Word Cloud, and Ego Network Analysis were conducted.

• Annual Conference of KIPS
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• 2023.05a
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• pp.292-294
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• 2023

본 논문에서는 국내 사망 원인 1위 질환인 암 중 가장 큰 비중을 차지하는 폐암의 암 오진율 감소 및 정밀 진단을 위해 폐암세포를 검출 및 계수 할 수 있는 시스템을 구현하였다. 사용자가 관심 영역을 지정하면 H&E 염색 방식을 사용한 폐암세포 전처리 과정을 거쳐 검출 및 계수 할 수 있다. 본 시스템을 통해 병리학자가 단 시간에 폐암세포 검출 및 계수하여 객관적 진단 도구로 활용할 수 있으며, 디지털 기술과 융합하여 정밀 의료에 크게 기여할 수 있을 것으로 기대된다.