Hybrid Model Of Frfs And Rnn

Recently technology has become a significant part of society, specifically for the medical field. People in the past have expressed concerns about the security and safety of implementing artificial intelligence (AI) into the medical field. Artificial intelligence is a computer system with human capabilities, such as decision making. Research has shown that AI could increase the efficiency and quality of patient care in the medical field. AI could greatly improve efficiency by using software that can analyze all of the symptoms the patient has and the patient’s family history in a shorter period of time than a human doctor could. For the time period from 2000 to 2010 the conversation about artificial intelligence was focused on the ethical

The argument of the author consists of the limitations of the practical model of diagnosis, which have current systems based on the AI. Salvado underlines, even throughout the significant gap between the amount of information of which the computer may know, compared to the human brain, there is still a great difference between the accuracy of diagnosis provided by a real doctor and a program. The examples provided by the author brightly support his argument and show disadvantages and gaps in the current model of artificial intelligence technology application.

The other technology applied in the healthcare sector is that of applying the connected medical devices. These devices have been applied in making sure that patient information is picked up and automatically transmitted to other connected computers for analysis and easy treatment (Desourdis, 2009). The devices usually allow the patients to see how they are fairing in real-time as well as giving them an opportunity to monitor their own lives on real-time basis. The other important technology that is important in the merged healthcare system is the of installing electronic health record systems that are hosted on physical servers with few healthcare entities by applying cloud-based systems to make sure that data security in the

Using clinical information system helps physicians to provide a more accurate diagnosis which helps to reduce medical errors and incorrect diagnosis. In CIS, information is structured and well organized in a manner that helps to eliminate the

For this component, we plan to optimize the DSS in three ways: 1) by enhancing the knowledge representation of the patient database; 2) by adding a probabilistic component to the classification system; 3) by improving the prediction accuracy of the classification system through the creation of statistically coherent committees. We propose to revisit the machine learning choices made in our preliminary study and integrate into the classification process descriptive features represented in different formats. We hypothesize that the information they carry may be important to consider in conjunction with the information carried by other features. This is particularly important given the sensitivity of the new task that we are planning to study.

The data are divided into training sets and test sets, and a set of training data is used to build a classification algorithm model to assign test sets to one category or the other. The SVM algorithm has been widely applied in the biological

Clinical Decision Support Systems is a helpful and beneficial aid the healthcare industry decision making. Clinical Decision Support Systems is needed in majority of healthcare processes. The healthcare system is considered to be one of the largest industries in the United States. The services that healthcare providers provide are endless. The Clinical Decision Support Systems use tools and systems in order to determine different scenarios of how to explore the risk associated with different situations. There has been a large increase in the amount of technology that is used and there is higher need for decision making to be integrated in the healthcare operations.

We have presented a new framework to analyze the brain image and identify the disorder associated with it. We have mainly focused on identification of Tumor and classification of Age and Gender. Tumor identification is an important thing in medical imaging because it is helpful to update the condition of human being. Tumor segmentation is used to calculate the location and size of tumor using Modified RBF algorithm. If it is malignant tumor, then patients need to treat immediately. So, severity of the disorder can be found using tumor segmentation and accordingly medication can be given. Many previous work have done to find the disorder but it is the first time, we tried to identify the age and gender. Age is classified as Child, Old and Adult

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

Breast cancer is the most common invasive type of cancer among women. Many machine learning and pattern recognition techniques have been proposed to detect the breast cancer. One of these techniques is Bayes classifier. In this paper naïve bayes classifier is used to detect the breast cancer. Naïve Bayesian (NB) is also known as a simple classifier, which is based on the Bayes theorem. In this paper, a new NB (weighted NB) classifier was used and its application on breast cancer

from check-up to to surgery or remote surgery, are dynamically linked to geographically distributed computing resources, intelligent analysis software/algorithms, and databases via secure networks. The ability between numerous standards and the exchange of information amongst totally different resources constitutes a vital issue in Health Care applications. Standards enable institutes to transmit clinical information and pictures from the electronic patient folder to completely different hospital sites requiring these elements for diagnosing, medical studies and treatment. Medical image examination may be a set of the medical. Folder that has pictures, prescription, reports of specimen analysis and different parts. A system known as ETICS has been designed to change the investigation of the ability between numerous standards and also the exchange of information among totally different resources. It provides a service for software system comes and infrastructures by integration well-established procedures, tools and resources during a coherent Framework and adapting them to the special desires of distributed software system. These tools facilitate rising the interoperability of software system applications and middleware parts, evaluating software system by

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

Machines with AI could be used in medical facilities to diagnose illnesses or to search for symptoms or the presence of anomalies in the bodies of patients (Keiser, 2017). Doctors constantly lament the delays in diagnosing diseases in a developing country. The use Artificial

The Rule based approach, also known as knowledge engineering approach, relies on grammar rules coming from the linguistic knowledge and heuristic rules to identify names, such rules are implemented as regular expressions or finite state transduction based grammar for pattern matching.

Computer-based clinical decision support (CDS) system employ decision rules that either uses procedure rules or production rules. A procedure is a compilation of data and logical statements that influence them by using control structures to control the decision-making flow. A rule-based system, utilizing a heuristic approach, comprises a set of statements called production rules. Production rules were initially studied in the 1940s and used in a different field of grammar. According to Greenes (2014), they were created as axioms to aid in rewriting strings as a section of the specification of a formal grammar. This concept was then transferred to solving problems. It took the