An Evolving Diagnostic Decision Support System

Analyze the requirements of the system and how this DSS is reducing medical errors and improving clinical practice.

ABSTRACT- An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information [1]. Artificial Neural Networks (ANN) also called neuro-computing, or parallel distributed processing (PDP), provide an alternative approach to be applied to problems where the algorithmic and symbolic approaches are not well suited. The objective of the neural network is to transform the inputs into meaningful outputs. There are many researches which show that brain store information as pattern. Some of these patterns are very complicated and allows us to recognize from different angles. This paper gives a review of the artificial neural network and analyses the techniques in terms of performance.

This mode is particularly useful in helping the user select the clinical manifestations that are important without forcing them to enter a large number of less relevant findings. At any time in this mode, the user can interrupt DXplain to ask “Why?” i.e. to ask DXplain to justify why this particular clinical manifestation is important. DXplain will respond by displaying the name of the disease that is being considered at that point in the interaction and the reason the particular finding might be important in confirming the presence of that disease. DXplain’s ability to explain and justify are key elements of the system. It is critical that this system not be perceived as a magic black box that can somehow provide the “answer” to a complex diagnostic problem. We believe that physicians will not accept DXplain as a useful diagnostic assistant unless the clinical interpretations seem reasonable and unless the system can offer explanations that are understandable and persuasive. (Barnett et al.,

Level 1 :As per the Considered Data Sets: (Generated nearby values through Rough Data Sets Theory  Produces)

In this fast paced ever changing world, there is always a need to increase the efficiency and reduce the time required for a particular problem to be solved. Most biological experiments, apart from being time consuming, are unpredictable and difficult to maintain, which results in a wastage of resources. With the advent of computers and their increased application in many different fields of study, we can now theoretically test out the hypothesis before proceeding with the practical experiment. Computational biology, as it is termed, is paving the path towards achieving this. With the success of the human genome project, the need for biologists equipped with the ability to look at diseases from a computational and mathematical standpoint

The processing unit of a single - layer perceptor network can be able to solve the

Decision support systems are ideally interactive systems that allow the decision making physician to come to the conclusion based on a host of information pulled from data bases, personal knowledge, predesigned modals etc. Decision support systems have many benefits such as; patient-time efficiency, speed up process of decision making, promotes learning and training, reveals new approaches in thought process, generates new evidence in support of a decision and encourages exploration and discovery of the decision maker (Bosworth, York, Kotansky, & Berman, 2011). Although these systems require end user expertise, correct inputs and appropriate modals, they also require vast and exstenive information. Immunoinformatics are used to compile vast amounts of data for the immunology field (De Groot A. , Immunomics: discovering new targets for vaccines and therapeutics , 2006). This data includes genetic mapping, protein structures, cytometry data and many other data pools needed by immunologists to make correct decisions. Immunoinformatics face to challenge of compiling this enormous amounts of data in an organised and correct way. This data needs to be mapped into correct diagnostic modals for the physician to use in their decision support system (Barh, Misra, & Kumar, 2010). This data leads to new ways of hypothesis testing for immune responses

Logistic regression model, as a usual approach before, was used to analyze the stroke outcomes' data. Fortunately, because of its potentially more powerful high-level prediction performance, machine learning algorithms have been proposed as an alternative to analyze large-scale multivariate data. Support vector machine (SVM) is one of the most popular machine learning methods to use for recognition or classification. Support vector machine (SVM) is one of the most popular machine learning methods used for recognition or classification.

Clinical decision-support systems (CDSS) apply best-known medical knowledge to patient data for the purpose of generating case-specific decision-support advice. CDSS forms the cornerstone of health informatics research and practice. It is an embedded concept in almost all major clinical information systems and plays an instrumental role in helping health care achieve its ultimate goal: providing high quality patient care while, at the same time, assuring patient safety and reducing costs. This computer based systems designed to impact clinician decision making about individual patients at the point in time that these decisions are made. If used properly, CDSS have the potential to change the way medicine has been taught and

There are a lot of problems and challenges involved in implementing a clinical decision support system. It is important for medical staff (doctors, administrators etc.) to be an integral part in the implementation and development of CDSS. Our limited research concluded that acceptance of such technology is not easy amongst physicians. One of the main reasons for non acceptance is that the physicians want to be a standalone entity. We plan to investigate many challenges such as lack of technical expertise, cost, integration, misdiagnosis, speed etc involved in implementation of different types of CDSS in the health care industry today. Our research paper will focus on the different decision problems involved in these challenges.

The concept of artificial intelligence was first labeled by a man named Alan Turing in 1950, he believed that the future would hold the possibility for man to communicate with computers and sustain a conversation (Atkinson, Solar 1). Although, we have reached the point where it is possible to hold a simple preprogrammed conversation with a computer and give them the ability to learn, there is still a long way to go in making computers fully artificially intelligent. Atkinson and Solar continue to describe some real world applications of artificial intelligence such as, “Data mining technologies, fraud detection, and industrial-strength optimization” (8). In these examples, forms of artificial intelligence like cognitive reasoning abilities are already being used making the demand for them higher.

The development and implementation of decision support systems within the clinical field represent a myriad of methodologies. While most institutions have adopted the IT centric or traditional approaches of DSS development and implementation, experts have recommended a user centric approach as an alternative method. If the user centric model is initiated within the industry, it makes it possible for the management to justify decisions through value analysis (Burstein & Holsapple, 2008). The development and implementation of DSS within the clinical setting demands a set of continuous and expensive endeavors. Project managers must initiate coordinated technical, human, and clinical efforts in engineering the required expertise for a prolonged period.

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

is described in mathematical tool by it . It is a powerful tool for managing