Fuzzy Based Automated System For Predicting Viral Infections

Logistic regression is used in this study to compare the non-linear neural network approach to come up with the best influenza vaccination model to be used for prediction purpose in the medical practice of primary health care physicians; where the vaccine is normally dispensed. In this study, logistic regression has been used widely to analyze

Referring to a study conducted by IBM, over $30 billion are spent in US each year on unnecessary hospital admissions. Clinical and behaviour data can be used to identify patients at high-risk and lead healthcare providers to develop strategies to care for patients before it is too late. The prediction of patient’s problems can reducing the number of unnecessary hospitalizations and the related costs.

The ability to predict patient’s condition is very important in hospital settings. Patients’ condition is based upon diverse factors like personal, psychological and other health problems. Different data mining algorithms are used to achieve the goal of predicting the patient’s condition.

According to our training database your due date has passed or is approaching. This is a required annual refresher training for Bloodborne Pathogen, based on EHLS records your last training was August 29,2014. So, your other classmates, may have recently taken this training or will receive an email from me very soon.

Effectively managing the health of the population requires tremendous efforts and strategies of a healthcare organization. Leveraging the use of health information technology such as enabling the use of clinical decision support (CDS) at the point care is one of the strategies for an effective population health management (PHM). Rush-Copley Medical Center has successfully demonstrated the effectiveness of CDS at the point of care in managing a TB outbreak, which has resulted in containing the disease and further outbreak in the general population. Such an example has shown the optimization of the systems and the value of health information technology

The role of the public health is to detect a disease for preventing a disease, and the complexity of the public health is not able to detect a disease for not preventing a disease. The important element of public health is to provide adequate, timely medical intervention for tracking, monitoring, and controlling disease outbreak. However, the challenge is that medical intelligence that allows common and expected diseases or infectious agent at endemic level that usually present in a community to be disregarded to focus on deadly diseases or infectious agent at epidemic level for outbreak of biochemical agent that have the potential to cause mass harm internationally for the medical intelligence.

There is no single way to deal with health forecasting, thus different strategies have regularly been embraced to conjecture total or specific health conditions. Then, there are no characterized health forecasting skylines (time spans) to coordinate the decisions of health forecasting methods/approaches that are regularly connected. The essential standards of health forecasting have not likewise been satisfactorily depicted to manage the procedure. This paper gives a brief presentation and possible investigation of health forecasting. It describes the key issues that are essential for health forecasting, including definitions, standards of health forecasting, and the

Based on rules, clinical decision support system (CDSS) may serve poorly in healthcare due to the if—then function. This system takes a diagnosis and spits out appropriate information based on the individual situation that it is analyzing. However, modern medicine can be considered as scientifically grounded, but evidence based practices can maintain a theoretical approach. There is some sort of conflict when trying to combine scientific perspectives with rational theories5. Hence, many errors can occur using the CDSS, such as alert fatigue, shifting of human roles, and currency of the CDS content6. There is a key issues of safety management when considering CDSS. There are certain systems that are better than others; hence great attention

Knowledge attained wth the use of data mining techniques can be used to make innovative and successful decisions that will increase the success rate of health care sector and the health of patients. In this paper, the study of classification algorithms in data mining techniques and its applications are discussed. The popular classification algorithms used in healthcare domain are explained in detail. The open source data mining tools are discussed. The applications of healthcare sector using data mining techniques are studied. With the future development of information communication technologies, data mining will attain its full potential in the discovery of knowledge hidden in the health care organizations and medical

HIT is also responsible for the development of structures for the public to input their health details in a quick and easy-to-use manner to avoid possible human mistakes. Developing and maintaining the health care information infrastructure, which are the web connecting institutions so they can share data and information securely is also a core function of HIT. Also with the help of technology, it is now possible to detect health problems at earlier stages due to availability of key data, which allows important analysis to be completed accurately.

The information Technologies Applications is widely used nowadays. Information technology (IT) has the potential to improve the quality, safety, and efficiency of health care. But before everything we should increasing our understanding of the information technologies in the health care. Also, we should understand what types of (IT) applications are most useful for improving health care? In this paper I will compares

Abstract - The healthcare industry collects large amounts of Healthcare data, but unfortunately not all the data are mined which is required for discovering hidden patterns and effective decision making. We propose efficient genetic algorithm with the back propagation technique approach for heart disease predic-tion. This paper has analyzed prediction systems for Heart dis-ease using more number of input attributes. The System uses medical terms such as Gender, blood pressure, cholesterol like13 attributes to predict the likelihood of patient getting a Heart dis-ease.

This research aims to present a hybrid intelligence model that can analyze medical data and processes, and provide decision support for

It is important to understand that using electronic health system helps physicians to provide a more accurate diagnosis which helps to reduce medical errors and incorrect diagnosis which make patients very happy knowing that physicians have their best interest at heart (Kudyba, 2010). In electronic health system, information is structured and well organized in a manner that helps to eliminate the time spent searching for information. Moreover, patients are very happy since electronic health system helps to provide privacy and security of patients’ information and data so as to eliminate the problem of leaving patients’ information unattended on papers so that unauthorized personnel can see and

In another research, Preuveneers, Berbers & Joosen (2013) stated the main factor which makes individual feel completely being in control is good techniques of data visualization. The approach and methods used by these authors are gathering data day by day to filter for similar situations and scenarios of the past. Based on that, the system can provide a suggestion for the diabetes patients to choose their insulin dosage. Clearly, each strategy is suitable in difference situations but in general context-aware technology can be used to offer support for the decision-making by the ability of collecting data automatically. Without doubt, with the information overload today, using context aware technology to support for decision-making will outbreak in near future not only in healthcare area but also in other fields such as business, sports or even military.