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Electronic medical record (EMR) system has become increasingly more important in developed countries due to its convenience and efficiency in medical information storage, management and analysis. However, one of the main limitations of EMR lies in that the clinical data for patients cannot be exchanged among different medical institutions. Recently, the cloud-based clinic system, featuring in lower...
The main purpose of outlier detection algorithms is to find a new feature that is distinct from the other features of the vector in the analyzed data set. This paper concerns outlier detection in medical databases, and the supervised and unsupervised methods used in detection of outliers in medical data are discussed. Moreover, the author's original method for detecting outliers based on linguistic...
The diagnostic errors are a controversial but real-life topic considered a leading cause of death and unintended harm due to medical-care procedures. We proposed a formula to assess the possibility to identify the diagnostic errors using a database analysis of patients admitted and released from the Clinic of Pulmonary Diseases. We built a database from the admittances and releases during a 21 months...
In this paper, we describe a cloud platform for health care tourism of Hainan province, China. The relevant business process is designed in a way that the characteristics of health care are taken into account. A scheme that combines medical tourism with a self-health care system and a nursing system is proposed. The data storage structure of the health care system is given, together with the specific...
Medical and health data are collected and stored in various document types and databases including clinical treatment records, medical examinations, prognosis health information, health billing and claims, health surveys, and community health settings. Such huge amount, wide range, and unstructured dataset characteristics imply a big challenge for researchers and government officers to analyze and...
This work focuses on the issue of diseases diagnosis based on data classification approaches. We consider mainly the diagnosis of heart diseases, diabetes, hepatitis and fetal risks. To do so, we employ a modified version of the SVDD algorithm, endowed with efficient tools to manage the multi-classification problems. Some other conventional algorithms such as SVM and RBF are, likewise, used to take...
PrefixSpan is a pattern-growth method for mining sequential patterns, and it is employed in this research for identifying disease trajectory patterns based on frequent subsequence analysis. One of the most beneficial features of this algorithm is the maintainable characteristics of original data order, especially for effectively and efficiently searching sequential patterns within a huge database...
Data mining is an important area of research and is pragmatically used in different domains like finance, clinical research, education, healthcare etc. Further, the scope of data mining have thoroughly been reviewed and surveyed by many researchers pertaining to the domain of healthcare which is an active interdisciplinary area of research. In fact, the task of knowledge extraction from the medical...
Computer based analysis of Electronic Health Records (EHRs) has the potential to provide major novel insights of benefit both to specific individuals in the context of personalized medicine, as well as on the level of population-wide health care and policy. The present paper introduces a novel algorithm that uses machine learning for the discovery of longitudinal patterns in the diagnoses of diseases...
The main objective of this research is to develop a framework to classify the medical data. In order to achieve promising results in medical data classification, we have planned to utilize orthogonal local preserving projection and classifier. Initially, the pre-processing will be applied to extract useful data and to convert suitable sample from raw medical datasets. Here, input dataset will be as...
Designing medication recommendation system is a need for the fast growing world. In this fast growing world, the need for the application which recommend a medication led to a doctor friendly and hospital free atmosphere for all users all over the world. In this paper an unified extraction system with stanford parser is used for extraction of medical terms. Then K-means clustering algorithm clusters...
The paper introduce, explains and presents the results obtained with the children prediction tool available through the specialized social network www.emama.mk. The inspiration behind the development of this system is to extend knowledge about the children diseases and their symptoms in a very simple and understandable way. The whole research presents summary of half decade successful work in which...
Healthcare related queries are a treasure trove of information about the information needs of domain users, be they patients or doctors. However, unlike general queries, in order to make the most out of the information therein, such queries have to be processed within a medical terminology annotation pipeline. We show how this has been done in the context of the KConnect project and demonstrate an...
The paper presents a new scheduling method of medical appointments for chronic patients. Taking into account the current situation, in which, especially due to the growing number of chronic patients, both hospitals and primary care units have to cope with a permanently increasing number of appointments, one of the main goals of the proposed method is to balance the workload of the medical staff. A...
Paralinguistic sounds such as laughter, cry and cough etc. communicate different messages as well, apart from the linguistic content in speech. These non-verbal speech sounds may possibly communicate some emotion, gesture or physiological condition of a human being. Cough sounds mostly indicate symptoms of a disease and are used sometimes as a gesture to draw attention. Analysing cough sounds using...
Chronic diseases may cause heavy burden on health care resources and disturb the quality of life. Chronic Obstructive Pulmonary Disease (COPD) is an important chronic disease, which takes a long period of time to progress and hard to detect in early stage. In this work, we propose a novel approach for early assessment on COPD by mining COPD-related sequential risk patterns from diagnostic clinical...
With the development of information technology, data acquisition, data storage and management means is increasingly perfect, data mining discipline emerge as the times require At present, the application of the technology in the field of medicine is still in its infancy, and expounds its theoretical framework and its specific application in the medical field and the current application situation in...
The article presents the design of an application interface for associated medical data visualization and management for neurologists in a stroke clustering and prediction system called Stroke MD. The goal of the system is to facilitate efficient visual data introduction and knowledge extraction based on a predictive model implementation. Aspects such as quality of the visualized data, type of the...
Objective: Atopic dermatitis, also known as the "ectopic dermatitis", which is a kind of intense itching, chronic and recurrent skin disease. In recent years, numerous clinical studies have reported that acupuncture is effective for Atopic dermatitis. This paper aims to assess the clinical efficacy and safety of acupuncture in the treatment of Atopic dermatitis by a systematic review. Methods:...
Named entity recognition (NER) is one of the fundamental tasks in natural language processing, with a high utilization value in the medical domain. The electronic discharge summary is a comprehensive clinical document, with the important legal effect especially in medical disputes, which contains patients' relevant information during hospitalization. Current main writing mode of electronic discharge...
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