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Introduction to electronic health records
Introduction to electronic medical records
Electronic medical records research paper
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Mining frequent disease from medical data using association rule mining technique
Abstract
Health care industry today generates large amounts of complex data for patients, hospitals facilities, diseases, disease diagnostic methods, electronic patients records, etc .The data mining techniques are very useful to make medicinal decisions, specially to analyze the information about frequently occurring disease from large dataset obtained from hospital so that in future healthcare administrator will able to improve the quality of service. Until now some works done on this where they had collected medical data from particular area and improve the accuracy of classification, increase in the prediction of various diseases. But here in the proposed technique work will be done on mining the frequent disease of patients in different geographical area at given time period.
Frequent diseases are those diseases which are occurring large number of times in the dataset. The data collection regarding these sorts of diseases can be done through association rule. Apriori Algorithm of the Association rule is used for the mining of frequent diseases.
Introduction
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The information can be transformed into knowledge about historical patterns and future trends. Data mining perform a significant role in the field of information technology. Health care industry today produce large amounts of complex data about patients, hospitals facilities, diseases, disease diagnosis methods, electronic patients records, etc, techniques of data mining are very essential for making medicinal decisions in curing diseases. The healthcare industry collects large amount of healthcare data which, unfortunately, are not “mined” to discover hidden information for effective decision making. The discovered knowledge can be analyzed by the healthcare administrators to improve the quality of
Computers have totally proliferated the world of medicine. They are used to monitor vital signs, to operate artificial hearts and to compile and store medical histories. Though not directly related to our well being, the last use is of utmost importance. Today, the use of medical databases and computer...
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In this work we proposed and implemented text classification (naïve bayes) and text clustering (K means) algorithm trained on dbGaP study text to identify heart,lung and blood studies. Classifiers performance compared with keyword based search result of dbGaP.It was determined that text classifiers are always best complement to document retrieval system of dbGaP.
Mathematical models and computer simulations are important tool to investigate spread and control of infectious diseases. These two jointly build and test theories that are involved with complex biological systems related disease, getting quantitative conjectures, determining parameter sensitivities due to change and estimating parameters from data. It is important to state that modeling is very crucial in epidemiology since in most cases we cannot do experiments. Modelling gives better idea in e-epidemiology when the system is simulated with various parameters because conducting experiments in e-epidemiology is critical.
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As you exit the bus, another passenger next to you starts to cough, and then you hold the handrail as you exit the bus. Since you’re late getting home, you take a shortcut through a field to get home quicker. These three simple acts just exposed you to bacteria, viruses, and insects that could cause illness or even death. Infectious diseases, also known as communicable disease, are spread by germs. Germs are living things that are found in the air, in the soil, and in water. You can be exposed to germs in many ways, including touching, eating, drinking or breathing something that contains a germ. Animal and insect bites can also spread germs.1
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Emerging Infectious Diseases (EIDS) are a disease of infectious origin whose incidence in humans has increased within the recent past threatens to increase in the near future. Over 30 new infectious agents have been detected worldwide in the last three decades; 60% of these are of zoonotic origin, and more than 2-3rds of these have originated in the wildlife (Dikid et al., 2013).
Health Information Management is directly responsible for how information is handled, the procedure for acquiring, analyzing, and the method of digital documentation of protecting protected health information. These processes are vital in providing safe and secure patient care. (“Gartee, R.,” 2011) Their mission is solely to improve healthcare by promoting extensive health information management techniques. They plan to achieve this goal by improving healthcare through data analytics and are known by their four core values: respect, excellence, leadership, and integrity.(“AHIMA,” n.d.)The American Health Information Management Association has an extensive role in Health Information Management.
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After understanding the possible outcomes and usages of Big Data Mining and Analytics, the study of the process is necessary to identify the real possibilities behind this techniques and how this can improve a business performance. To do this; we should comprehend the basics about data mining and the process that leads from pure data to insights.
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When a company implements the use of data analytics, they are clearly looking to only improve the way the company functions on a daily basis by pin pointing possible solutions and verifying or dismissing certain ideas that may exist. The whole point of data analytics is that they give the company the opportunity to input raw data and data analytics makes sense of this raw data so that the company can use it to guide their choices. Data analytics makes decision making much easier and straighter forward than traditional accounting and business methods. It also provides the upper level employees of the company more exposure to the exact events that are occurring within the company. So, when it comes to the idea of having more information with
What are the roles of a public health informatician in building and enhancing the public health infrastructure
To the "Hospital medicine" in the past, it uses a cross-sectional nosographic technique to be classified different types of patients according to the internal lesion for example: they will distinguish the heart disease patient and high blood pressure disease patient and distinguish which cause it and prescribe the right medicine for an illness. This technique can let the doctor be more focus on their professional and more expert on those lesions. Also the "hospital medicine" will according to the symptom and disease to conflate an infinite chain of risk which found out the root cause of illness for example: A headache may be a risk for high blood pressure, but high blood pressure may also