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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...
As the field of healthcare has changed, new diseases and disorders have developed. It is impossible for one doctor to know how to recognize and treat every disease in the world. With evidence-based guidelines, they can come close. These guidelines may not have a perfect success rate, but they can make diagnosing illness easier.
The model utilizes a scientific and hypothetical deduction to assist the medical diagnosis reasoning. Nurses use decision trees to numerically assess potential outcomes. Moreover, analytical decision making is also employed to help practitioners in diagnostic reasoning. The physician’s thought process should follow rational logic subject to study until a decision can be made. During such decision-making process, the physician’s experience as well as their ability to recognize situations that impact the process are taken into
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.
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
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.
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).
Data mining is the computer-assisted process that digs through and analyzes massive sets of data, and then extracts the meaning of the figures. Data mining has empowered companies to find new opportunities for growth, decisions and the ability to identify customers, trends and purchasing decisions (Rygielski, Wang, and Yen 2002). The data mining results allows companies to save customers prior to departure to a competitor. Second indicator is that the Target’s company strategy includes the phrase “predictive analytics”, which is defined as “Predictive analytics is the branch of the advanced analytics which is used to make predictions about unknown future events. Predictive analytics uses many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions about future.” (http://www.predictiveanalyticstoday.com). Target utilizes three primary sets of guest expectations, understanding their needs, deliver relevant messages and offers and contact guest with the right vehicle (Pole 2010). Target purchases the demographics, spending habits and neighborhood data just to start the process of towards those three sets of guest
Since there are several names and abbreviations in many of biomedical existences, it is so good that an automatic mean facilitates text mining themes for collecting these synonym words and abbreviations. If all the words and abbreviations for one existence could be written as a single sentence in the context, it will be a field work in Information Extraction issue, synonym words of a name decryption of gene and abbreviations of biomedical sentences[7]. Abbreviations and summary usually are used for illnesses and etcetera in biomedical contexts for names of gene. Since the changes of abbreviations-definitions are dependent to the context, they can cause ambiguity[8]. The ability to detect and extract abbreviations and writing them on an optimized definition for data extracting field could be
The purpose of this post is to compare and contrast informatics and clinical informatics. I will then provide examples of clinical informatics, as well as examples of how a nurse manager can use data management as a strategy to improve patient care on their unit. Finally, I will discuss the mandate by President Bush to implement electronic health records by 2014. According to the American Medical Informatics Association (2016), informatics combines the sciences of computers, management, information, and decision, with cognitive science, and organizational theory to manage information and knowledge regarding biomedical research, clinical care, and public health.
Data mining has emerged as an important method to discover useful information, hidden patterns or rules from different types of datasets. Association rule mining is one of the dominating data mining technologies. Association rule mining is a process for finding associations or relations between data items or attributes in large datasets. Association rule is one of the most popular techniques and an important research issue in the area of data mining and knowledge discovery for many different purposes such as data analysis, decision support, patterns or correlations discovery on different types of datasets. Association rule mining has been proven to be a successful technique for extracting useful information from large datasets. Various algorithms or models were developed many of which have been applied in various application domains that include telecommunication networks, market analysis, risk management, inventory control and many others
What are the roles of a public health informatician in building and enhancing the public health infrastructure
Introduction Clinical Decision Support Systems: Decision support systems use a software containing knowledge and theories from various fields to support complex decision-making and problem-solving. A working definition; "Clinical Decision Support systems link health observations with health knowledge to influence health choices by clinicians for improved health care". (Proposed by Dr. Robert Hayward of the Centre for Health Evidence) It allows decision makers to build and look for the implications of their judgments.
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
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