• Journal of Korean Society for Quality Management
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• v.42 no.2
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• pp.153-163
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• 2014

Purpose: This research aims to find out clinical information protection behavior within a medical institution in mandatory circumstance based on health psychology theories Methods: This research has developed the survey based on the variables from ealth psychology theories; and conducted the survey during the whole month in April 2013. In the end, 256 samples have been used for this research's analysis. Results: First of all, Empirical results has proved that perceived benefits, self-efficacy, and cues to action have an positive influence on clinical information protection behavior. Perceived barriers has an negative influence. Finally, it has proven from the research that perceived severity and perceived susceptibility do not have an impact on clinical inf ormation protection behavior Conclusion: These findings provide an enriched understanding about medical institution workers information protection behavior on patient's clinical information.

• Journal of Internet Computing and Services
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• v.18 no.6
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• pp.75-84
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• 2017

As there has been growing interests in PHR-based personalized health management project, various institutions recently explore safe methods of recording personal medical and health information. In particular, innovative medical solution can be realized when medical researchers and medical service institutes can generally get access to patient data. As EMR data is extremely sensitive, there has been no progress in clinical information exchange. Moreover, patients cannot get access to their own health data and exchange it with researchers or service institutions. It can be operated in terms of technology, yet policy environment are affected by state laws as well as Privacy and Security Policy. Blockchain technology-independent, in transaction, and under test-is introduced in the medical industry in order to settle these problems. In other words, medical organizations can grant preliminary approval on patient information exchange by using the safely encrypted and distributed Blockchain ledger and can be managed independently and completely by individuals. More apparently, medical researchers can gain access to information, thereby contributing to the scientific advance in rare diseases or minor groups in the world. In this paper, we focused on how to manage personal medical information and its protective use and proposes medical treatment exchange system for patients based on a permissioned Blockchain network for the safe PHR operation. Trusted Model for Sharing Medical Data (TMSMD), that is proposed model, is based on exchanging information as patients rely on hospitals as well as among hospitals. And introduce medical treatment exchange system for patients based on a permissioned Blockchain network. This system is a model that encrypts and records patients' medical information by using this permissioned Blockchain and further enhances the security due to its restricted counterfeit. This provides service to share medical information uploaded on the permissioned Blockchain to approved users through role-based access control. In addition, this paper presents methods with smart contracts if medical institutions request patient information complying with domestic laws by using the distributed Blockchain ledger and eventually granting preliminary approval for sharing information. This service will provide an independent information transaction and the Blockchain technology under test will be adopted in the medical industry.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.4
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• pp.268-275
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• 2014

In future, hospitals are expected to automatically issue remote transcriptions. Many general hospitals are planning to encrypt their medical database to secure personal information as mandated by law. The electronic medical record system, picture archiving communication system, and the clinical data warehouse, amongst others, are the preferred targets for which stronger security is planned. In the near future, medical systems can be assumed to be automated and connected to remote locations, such as rural areas, and islands. Connecting patients who are in remote locations to medical complexes that are usually based in larger cities requires not only automatic processing, but also a certain amount of security in terms of medical data that is of a sensitive and critical nature. Unauthorized access to patients' transcription data could result in the data being modified, with possible lethal results. Hence, personal and sensitive data on telemedicine and medical information systems should be encrypted to protect patients from these risks. Login passwords, personal identification information, and biological information should similarly be protected in a systematic way. This paper proposes the use of electronic acupuncture with a built-in multi-pad, which has the advantage of being able to establish a patient's physical condition, while simultaneously treating the patient with acupuncture. This system implements a sensing pad, amplifier, a small signal drive circuit, and a digital signal processing system, while the use of a built-in fuzzy technique and a control algorithm have been proposed for performing analyses.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.171-180
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• 2021

Currently, hospitals and medical practices have a large amount of unstructured information, gathered in time at each ward or practice by physicians in a wide range of medical branches. The data requires processing in order to be able to extract relevant information, which can be used to improve the medical system. It is useful for a physician to have access to a patient's entire medical history when he or she is in an emergency situation, as relevant information can be found about the patient's problems such as: allergies to various medications, personal history, or hereditary collateral conditions etc. If the information exists in a structured form, the detection of diseases based on specific symptoms is much easier, faster and with a higher degree of accuracy. Thus, physicians may investigate certain pathological profiles and conduct cohort clinical trials, including comparing the profile of a particular patient with other similar profiles that already have a confirmed diagnosis. Involving information technology in this field will change so the time which the physicians should spend in front of the computer into a much more beneficial one, providing them with the possibility for more interaction with the patient while listening to the patient's needs. The expert system, described in the paper, is an application for medical diagnostic of the most frequently met conditions, based on logical programming and on the theory of probabilities. The system rationale is a search item in the field basic knowledge on the condition. The web application described in the paper is implemented for the ward of pathological anatomy of a hospital in Romania. It aims to ease the healthcare staff's work, to create a connection of communication at one click between the necessary wards and to reduce the time lost with bureaucratic proceedings. The software (made in PHP programming language, by writing directly in the source code) is developed in order to ease the healthcare staff's activity, being created in a simpler and as elegant way as possible.

• Proceedings of the KOSOMBE Conference
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• v.1989 no.05
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• pp.75-76
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• 1989

The blood bank is a field of clinical pathology which requires the most accuratemaintenanceofrecording. Because the mistake in it is directly related to a patient's life. So, the computerization of the blood bank is urgent to maintain a log blook arid to compare the patient's current data with past result. We developed the blood bank management program using 32 bit minicomputer. This is composed of 4 parts; a management of routine test result, special test result, the blood issue and statistics. The management of routine test result handles the patient's information and blood typing and compares above results with the past one of same patient. The management of special test result are for special immunohematologic tests like an irregular antibody, Coombs' test, and etc. Blood issue part records the type of the blood bag, component, and the name of issuer. Statistic part are made to get statistics of each day and each month by the blood type, and the type the blood component. The program is secured by the maintenance of operator's operation history and thu provision of the security code to each operator, without which no one can enter the system and after the content. So the stability and reliability of the data is obtained. This program will be upgraded for bar-code using system in the near future.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.225-234
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• 2023

In this era of information and communication technology (ICT), tremendous improvements have been witnessed in our daily lives. The impact of these technologies is subjective and negative or positive. For instance, ICT has brought a lot of ease and versatility in our lifestyles, on the other hand, its excessive use brings around issues related to physical and mental health etc. In this study, we are bridging these both aspects by proposing the idea of AI based mental healthcare (AIMS). In this regard, we aim to provide a platform where the patient can register to the system and take consultancy by providing their assessment by means of a chatbot. The chatbot will send the gathered information to the machine learning block. The machine learning model is already trained and predicts whether the patient needs a treatment by classifying him/her based on the assessment. This information is provided to the mental health practitioner (doctor, psychologist, psychiatrist, or therapist) as clinical decision support. Eventually, the practitioner will provide his/her suggestions to the patient via the proposed system. Additionally, the proposed system prioritizes care, support, privacy, and patient autonomy, all while using a friendly chatbot interface. By using technology like natural language processing and machine learning, the system can predict a patient's condition and recommend the right professional for further help, including in-person appointments if necessary. This not only raises awareness about mental health but also makes it easier for patients to start therapy.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.17-25
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• 2023

The alarming global prevalence of Type 2 Diabetes Mellitus (T2DM) has catalyzed an urgent need for robust, early diagnostic methodologies. This study unveils a pioneering approach to predicting T2DM, employing the Extreme Gradient Boosting (XGBoost) algorithm, renowned for its predictive accuracy and computational efficiency. The investigation harnesses a meticulously curated dataset of 4303 samples, extracted from a comprehensive Chinese research study, scrupulously aligned with the World Health Organization's indicators and standards. The dataset encapsulates a multifaceted spectrum of clinical, demographic, and lifestyle attributes. Through an intricate process of hyperparameter optimization, the XGBoost model exhibited an unparalleled best score, elucidating a distinctive combination of parameters such as a learning rate of 0.1, max depth of 3, 150 estimators, and specific colsample strategies. The model's validation accuracy of 0.957, coupled with a sensitivity of 0.9898 and specificity of 0.8897, underlines its robustness in classifying T2DM. A detailed analysis of the confusion matrix further substantiated the model's diagnostic prowess, with an F1-score of 0.9308, illustrating its balanced performance in true positive and negative classifications. The precision and recall metrics provided nuanced insights into the model's ability to minimize false predictions, thereby enhancing its clinical applicability. The research findings not only underline the remarkable efficacy of XGBoost in T2DM prediction but also contribute to the burgeoning field of machine learning applications in personalized healthcare. By elucidating a novel paradigm that accentuates the synergistic integration of multifaceted clinical parameters, this study fosters a promising avenue for precise early detection, risk stratification, and patient-centric intervention in diabetes care. The research serves as a beacon, inspiring further exploration and innovation in leveraging advanced analytical techniques for transformative impacts on predictive diagnostics and chronic disease management.

• Convergence Security Journal
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• v.17 no.5
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• pp.121-132
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• 2017

This study is a narrative research study to understand the degree of awareness and practice of nursing college students' patients' health information protection and to grasp the relationship between them.. The subjects of this study were 122 nursing college students who experienced clinical practice in two nursing colleges in GwangJu city. Data collection was done from October 13 to 28, 2017. Using SPSS/WIN 21.0 Program, descriptive statistics, t-test, one-way ANOVA and Pearson correlation analysis were performed. As a result of the study, the degree of awareness and practicability of patient' health information protection were 4.44(SD=0.44) and 4.28(SD=0.62), respectively. There was a difference in the awareness of health information protection when they were educated about patients' health information protection at school (t=5.094, p<.001) and hospital (t=2.028, p=.045) in the case of having experience in patients' health information protection in hospitals(t=2.551, p=.012). There was a significant positive(+)correlation between patient's health information protection perception and practicing degree, and the degree of health information protection practitioner's communication domain (r=.420, p<.001). There was a significant positive correlation with the domain (r=.368, p<.001) and the referral domain (r=.304, p=.001). Based on these results, we sought to protect the personal information of patients and to provide necessary basic data to develop for standardized education program.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.19-30
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• 2021

Diabetes Mellitus (DM) is one of common chronic diseases leading to severe health complications that may cause death. The disease influences individuals, community, and the government due to the continuous monitoring, lifelong commitment, and the cost of treatment. The World Health Organization (WHO) considers Saudi Arabia as one of the top 10 countries in diabetes prevalence across the world. Since most of the medical services are provided by the government, the cost of the treatment in terms of hospitals and clinical visits and lab tests represents a real burden due to the large scale of the disease. The ability to predict the diabetic status of a patient without the laboratory tests by performing screening based on some personal features can lessen the health and economic burden caused by diabetes alone. The goal of this paper is to investigate the prediction of diabetic and prediabetic patients by considering factors other than the laboratory tests, as required by physicians in general. With the data obtained from local hospitals, medical records were processed to obtain a dataset that classified patients into three classes: diabetic, prediabetic, and non-diabetic. After applying three machine learning algorithms, we established good performance for accuracy, precision, and recall of the models on the dataset. Further analysis was performed on the data to identify important non-laboratory variables related to the patients for diabetes classification. The importance of five variables (gender, physical activity level, hypertension, BMI, and age) from the person's basic health data were investigated to find their contribution to the state of a patient being diabetic, prediabetic or normal. Our analysis presented great agreement with the risk factors of diabetes and prediabetes stated by the American Diabetes Association (ADA) and other health institutions worldwide. We conclude that by performing class-specific analysis of the disease, important factors specific to Saudi population can be identified, whose management can result in controlling the disease. We also provide some recommendations learnt from this research.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.12
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• pp.6533-6537
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• 2012

Cancers are significant contributors to the mortality and health care expenditures. Cancer can be reduced and monitored by new information technology. Radio frequency identification or RFID is a wireless identification technology. The use of this technology can be employed for identifying and tracking clinical staff, patients, supplies, medications and equipments. RFID can trace and manage chemotherapy drugs. There are different types of RFID. Implantable RFID allowing a chip to be embedded under the skin and that store the cancer patient's identifier. These are concerns about applications of RFID. Privacy, security and legal issues are key problems. This paper describes capabilities, benefits and confidentiality aspects in radio frequency identification systems and solutions for overcoming challenges.