• Perspectives in Nursing Science
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• v.5 no.1
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• pp.1-15
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• 2008

Background: Recent changes in the medical environment have increased the need for the home health care nursing in Korea. Even though the number of home health care patients is increasing, the major nursing problems have not been identified due to lack of a standardized nursing diagnosis. Aim: An investigative study was conducted to determine the frequency and appropriateness of nursing problems in hospital-based home health care patients in Korea using two internationally standardized nursing diagnosis classification systems. Methods: Nursing records of 249 hospital-based home health care patients were reviewed and nursing problems were identified using the North American Nursing Diagnosis Association Nursing Diagnosis Taxonomy I (NANDA) and the Clinical Care Classification of Nursing Diagnoses (CCC). Findings: Out of 463 nursing problems. 403 nursing problems were described using the NANDA whereas 427 nursing problems were described using the CCC. Nursing diagnoses not captured by the NANDA classification include nausea/vomiting, anorexia, risk for nutrition deficit, decreased blood pressure, dying process, blood sugar impairment. infection unspecified, and disuse syndrome. Nursing diagnoses not captured by the CCC include nausea/vomiting and anorexia. Conclusions: In describing nursing problems of home health care patients, it was found that the CCC was able to represent more diagnoses than the NANDA.

• The Journal of the Korea Contents Association
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• v.10 no.10
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• pp.54-67
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• 2010

Applying the conventional machine-learning method which has been frequently used in health-care area has several fundamental problems for modern U-health service analysis. First of all, we are still lack of application examples of the traditional method for our modern U-health environment because of its short term history of U-health study. Second, it is difficult to apply the machine-learning method to our U-health service environment which requires real-time management of disease because the method spends a lot of time in the process of learning. Third, we cannot implement a personalized U-health diagnosis system using the conventional method because there is no way to assign weights on the disease-related variables although various kinds of machine-learning schemes have been proposed. In this paper, a novel diagnosis scheme PCADP is proposed to overcome the problems mentioned above. PCADP scheme is a personalized diagnosis method and it makes the bio-data analysis just a 'process' in the U-health service system. In addition, we offer a semantics modeling of the U-health ontology framework in order to describe U-health data and service specifications as meaningful representations based on this PCADP. The PCADP scheme is a kind of statistical diagnosis method which has characteristics of flexible structure, real-time processing, continuous improvement, and easy monitoring of decision process. Upto the best of authors' knowledge, the PCADP scheme and ontology framework proposed in this paper reveals one of the best characteristics of flexible structure, real-time processing, continuous improvement, and easy monitoring among recently developed U-health schemes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.538-541
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• 2009

Increasing of number of people who suffered from long term chronic diseases which required frequent daily health monitoring and body check up in conjunction with the trendy uses of mobile phones and Personal Digital Assistants (PDAs) in various ubiquitous computing had make portable healthcare system a well known application today. A mobile phone based portable healthcare monitoring system with multiple vital signals monitoring ability at real time in WSN and CDMA network is developed. This system carries out real time monitoring and local data analysis process in the mobile phone. Any detection of abnormal health condition and diagnosis at earlier stage will reduce the risk of patient's life. As an extension to the existing model, a pre-diagnosis management system (PDMS) is designed to minimize the time consuming in pre-diagnosis process in the hospital or healthcare center. An alert is sent to the web server at the healthcare center when the patient detects his health is at critical state where the immediate diagnosis is needed. Preparation of diagnosis equipments and arrangement of doctor and nurses at the hospital side can be done earlier before the arrival of patient at the hospital with the help of PDMS. An efficient pre-diagnosis management increases the chances of diseases recovery rate as well.

• Smart Structures and Systems
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• v.17 no.6
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• pp.1031-1053
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• 2016

The health conditions of in-service civil infrastructures can be evaluated by employing structural health monitoring technology. A reliable health evaluation result depends heavily on the quality of the data collected from the structural monitoring sensor network. Hence, the problem of sensor fault diagnosis has gained considerable attention in recent years. In this paper, an innovative sensor fault diagnosis method that focuses on fault detection and isolation stages has been proposed. The dynamic or auto-regressive characteristic is firstly utilized to build a multivariable statistical model that measures the correlations of the currently collected structural responses and the future possible ones in combination with the canonical correlation analysis. Two different fault detection statistics are then defined based on the above multivariable statistical model for deciding whether a fault or failure occurred in the sensor network. After that, two corresponding fault isolation indices are deduced through the contribution analysis methodology to identify the faulty sensor. Case studies, using a benchmark structure developed for bridge health monitoring, are considered in the research and demonstrate the superiority of the new proposed sensor fault diagnosis method over the traditional principal component analysis-based and the dynamic principal component analysis-based methods.

• Biomedical Science Letters
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• v.28 no.1
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• pp.1-8
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• 2022

Total joint arthroplasty is a successful joint replacement treatment that improves joint function and overall quality of life and provides pain relief. However, the prevalence of periprosthetic joint infection (PJI) has become prevalent with the rise in the incidence of arthroplasty surgery. PJI occurs rarely following arthroplasty however presents with serious complications, including high morbidity. The identification of causative microorganisms is essential for the treatment of PJI. Managing PJI requires complex treatment strategies, including long-term antibacterial treatment, and significant medical costs can be incurred. The American Academy of Orthopedic Surgeons, the Centers for Disease Control and Prevention, and Surgical Care Improvement Project guidelines recommend that prophylactic antibiotics such as first-generation cephalosporins be infused completely 1 hour before surgical incision. However, these preventative antibiotics are very limited, therefore risk factors must be identified to diagnosis and treat patients effectively. Moreover, determining antimicrobial susceptibility during artificial joint surgery and choosing the most appropriate treatment strategy following an accurate diagnosis of microbial infections are essential. In the present review, we describe the management, including the etiology, diagnosis, and classification of PJI, and approaches to its diagnosis using the available novel molecular diagnostic methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.284-310
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• 2024

Identifying clinical pathways for disease diagnosis and treatment process recommendations are seriously decision-intensive tasks for health care practitioners. It requires them to rely on their expertise and experience to analyze various categories of health parameters from a health record to arrive at a decision in order to provide an accurate diagnosis and treatment recommendations to the end user (patient). Technological adaptation in the area of medical diagnosis using AI is dispensable; using expert systems to assist health care practitioners in decision-making is becoming increasingly popular. Our work architects a novel knowledge-based recommender system model, an expert system that can bring adaptability and transparency in usage, provide in-depth analysis of a patient's medical record, and prescribe diagnostic results and treatment process recommendations to them. The proposed system uses a set of parallel discrete fuzzy rule-based classifier systems, with each of them providing recommended sub-outcomes of discrete medical conditions. A novel knowledge-based combiner unit extracts significant relationships between the sub-outcomes of discrete fuzzy rule-based classifier systems to provide holistic outcomes and solutions for clinical decision support. The work establishes a model to address disease diagnosis and treatment recommendations for primary lung disease issues. In this paper, we provide some samples to demonstrate the usage of the system, and the results from the system show excellent correlation with expert assessments.

• Parasites, Hosts and Diseases
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• v.58 no.5
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• pp.551-558
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• 2020

The flaviviruses are small single-stranded RNA viruses that are typically transmitted by mosquitoes or tick vectors and are etiological agents of acute zoonotic infections. The viruses are found around the world and account for significant cases of human diseases. We investigated population of culicine mosquitoes in central region of Korean Peninsula, Incheon Metropolitan City and Hwaseong-si. Aedes vexans nipponii was the most frequently collected mosquitoes (56.5%), followed by Ochlerotatus dorsalis (23.6%), Anopheles spp. (10.9%), and Culex pipiens complex (5.9%). In rural regions of Hwaseong, Aedes vexans nipponii was the highest population (62.9%), followed by Ochlerotatus dorsalis (23.9%) and Anopheles spp. (12.0%). In another rural region of Incheon (habitat of migratory birds), Culex pipiens complex was the highest population (31.4%), followed by Ochlerotatus dorsalis (30.5%), and Aedes vexans vexans (27.5%). Culex pipiens complex was the predominant species in the urban region (84.7%). Culicine mosquitoes were identified at the species level, pooled up to 30 mosquitoes each, and tested for flaviviral RNA using the SYBR Green-based RT-PCR and confirmed by cDNA sequencing. Three of the assayed 2,683 pools (989 pools without Anopheles spp.) were positive for Culex flaviviruses, an insect-specific virus, from Culex pipiens pallens collected at the habitats for migratory birds in Incheon. The maximum likelihood estimation (the estimated number) for Culex pipiens pallens positive for Culex flavivirus was 25. Although viruses responsible for mosquito-borne diseases were not identified, we encourage intensified monitoring and long-term surveillance of both vector and viruses in the interest of global public health.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.1
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• pp.39-44
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• 2014

Background: This study aimed to provide an overview of lifestyle changes after breast cancer diagnosis and to examine the relationship between dietary and physical activity changes with weight changes in breast cancer patients. Women with breast carcinomas (n=368) were recruited from eight hospitals and four breast cancer support groups in peninsular Malaysia. Dietary and physical activity changes were measured from a year preceding breast cancer diagnosis to study entry. Mean duration since diagnosis was $4.86{\pm}3.46$ years. Dietary changes showed that majority of the respondents had decreased their intake of high fat foods (18.8-65.5%), added fat foods (28.3-48.9%), low fat foods (46.8-80.7%), red meat (39.7%), pork and poultry (20.1-39.7%) and high sugar foods (42.1-60.9%) but increased their intake of fish (42.7%), fruits and vegetables (62.8%) and whole grains (28.5%). Intake of other food groups remained unchanged. Only a small percentage of the women (22.6%) had increased their physical activity since diagnosis where most of them (16.0%) had increased recreational activities. Age at diagnosis (${\beta}$= -0.20, p= 0.001), and change in whole grain (${\beta}$= -0.15, p= 0.003) and fish intakes (${\beta}$= 0.13, p= 0.013) were associated with weight changes after breast cancer diagnosis. In summary, the majority of the women with breast cancer had changed their diets to a healthier one. However, many did not increase their physical activity levels which could improve their health and lower risk of breast cancer recurrence.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.7
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• pp.3159-3163
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• 2012

Background: For cervical cancer the epidemiological profile is poorly known in Morocco and no data is available concerning the direct medical costs. The purpose of this work is to estimate the direct cost of medical management of invasive cervical cancer during the first year after diagnosis in Morocco. Methods: The estimation of direct costs of medical management of invasive cervical cancer during the first year after diagnosis in Morocco is based on the estimation of individual cost in each stage which covers diagnosis, treatment and follow-up during first year. The cost was estimated per patient and whole cycle-set using the costs for each drug and procedure as indicated by the Moroccan National Agency for Health Insurance. Extrapolation of the results to the whole country was used to calculate the total annual cost of cervical cancer treatments in Morocco. Results: Overall approximately 1,978 new cases of cervical cancer occur each year in Morocco. The majority (82.96%) of these cases were diagnosed at a late stage (stageII or more). The cost of one case of cervical cancer depends on stage of diagnosis, the lowest cost is $382 for stageCis followed by the cost of stageIA1 for young women (< 40 years) which is $2,952. The highest cost is for stageIV, which is $7,827. The total cost of cervical cancer care for one year after diagnosis is estimated at $13,589,360. The share allocated to treatment is the most important part of the global care budget with an annual sum of $13,027,609 whereas other cost components are represented as follows: $435,694 for annual follow-up activity and $126,057 for diagnosis and preclinical staging. Conclusion: This study provides health decision-makers with a first estimate of costs and the opportunity to achieve the optimal use of available data to estimate the needs of health facilities in Morocco.

• Journal of Physiology & Pathology in Korean Medicine
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• v.25 no.4
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• pp.755-758
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• 2011

Sasang Constitutional Medicine (SCM) suggests the concept of health condition which is owned of different properties by each Sasang Constitution. But SCM theory has concentrate to explaining different symptoms that distinguish health condition, not much concerned with practical methods like face features, pulse diagnosis, skin surface properties and voice features. Physical properties of practical diagnosis methods from literature finding have been researched. As a result, complexion and face moisture were described to be physical properties related to health State. In scope of pulse diagnosis, depth, rate, contour, width and pressure are related. Skin surfaces' elasticity, moisture, fraction properties were also suggested as factors of health State of Sasang Constitution. We assume that this study would contribute further studies for objectifying and quantifying diagnosis methods to evaluate health state grades of Sasang Constitution.