CHAPTER 1
INTRODUCTION
Decision-making has been a topic of significant research over the past few years. The more ambiguous situation for decision-making, the bigger advantage there is to provide decision-support using modeling. Context-awareness is required for better understanding of decision parameters and implications of selecting a particular decision. Most of the decisions in real-time are based on access to information that can support decision-making process. In critical situations such as healthcare management, there is a risk of making incorrect decision based on incomplete or outdated information. Artificial Intelligence (AI) is the realm which enables intelligent decision-support. The information is processed intelligently
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There is a shortage of experts and professionals in the rural parts of India. There are few facilities available in rural parts of the country because of which medical professionals do not stay or visit such areas. This leads to poor rural public health facilities. The aim of this system is to create an intelligent medical system which enables the health workers in these areas to take medical decisions and provide basic medication to the rural population. Our basic purpose therefore is to be able to manage the complexity of real-life situations and successfully deploy a Multi Agent System using DSS. We believe that our motive of providing a decision making medical system is capable of addressing the medical problems in the rural parts of the country.
1.2. COMPLEXITY OF DECISION MAKING IN REAL WORLD
Medical industry is a critical field where incorrect medical and management decisions can have disastrous economic, social and ecological consequences. The complexity of medical problems requires the development of software tools that are not only capable of storing huge amount of information but also process that information intelligently using experience from experts to provide medical decision support.
George A. Miller gave a classical psychology theory in 1956. He proposed that human short term memory has
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The first feature is that multi-agent learning deals with problems involving multiple agents and the area involved can be unusually large. The agents interact with each other and the small changes in learned behavior result in unpredictable changes. These changes result in macro-level (“emergent”) properties of the multiagent group as a whole. The second feature states that multi-agent learning may involve multiple agent as learners, each agent learning and adapting in the context of others. This feature introduces game-theoretic patterns to the learning process which have not been fully understood yet. There are three main approaches to learning. They are reward-based, supervised, and unsupervised learning. These methods distinguish by the kind of feedback learners receive from the critics. In reward-based learning, the critic provides a quality assessment (the “reward”) of the learner’s output. In supervised learning, the critic provides the correct output. In unsupervised learning, no feedback is provided at all.
MAS can be classified into Closed and Open MAS. Closed MAS contain agents designed to cooperate with each other easily towards a global goal. An Open MAS might contain agents that are not designed to cooperate and coordinate. The agents in an Open MAS are designed to assist the agents to cooperate and coordinate to work together. The system
Summary: Chapter 2 Chapter two dives into the concept of learning. As mentioned in the previous chapter, learning is the study of changes in behavior produced by experience, so when studying learning it is vital to examine how events in the environment change an individual’s behavior. Many scientists consider learning to be a natural phenomenon, they make their case based on four assumptions. The first assumption being that natural phenomena’s do not just happen, but instead they are caused as the result of some other event. The second assumption is that causes precede effects.
Since many health information infrastructure systems are relatively new, there is still variability in the implementation stages that different organizations have achieved. Additionally, most systems will have more than one capability that provides value, so the relationship between the system’s functionality and the resulting impact to patient care must be analyzed in order to determine the value it provides (Einstein, Juzwishin, Kushniruk, & Nahm, 2011). Value of health information infrastructures can be assessed in many different ways, including whether the technology allows the availability of useful information, how that information is utilized by staff and patients, and its impact on health outcomes. For information to be of value and influence medical decision making, it must be comprehensive, accessible, useful, and valid (Fitterer, Mettler, Rohner, & Winter, 2011).
In the article, “Past Experience is Invaluable For Complex Decision Making,:” it
There is a need to complete the implementation of EHRs as part of the meaningful use. Clinician Informaticians are needed to help this transition. Clinician Informatician is an individual, physician or a nurse who has more expertise in the field of informatics and is the person to go to with questions. They integrate their knowledge and understanding of science of their clinical specialty with their knowledge of informatics. The integration of these two disciplines Clinical and Informatics, helps informaticians to provide the best quality care by integrating people with systems, processes and information technology.
Application of Conflict Theory to the Gun Control Debate Being a debate, the conflict theory is a very applicable theory that can be applied to guns/gun control laws and their roles in society. A debate is something that is associated with conflict, so by observing how deep and exactly in what directions this conflict extends, one might be able to understand this topic in a new light. In other words, by analyzing the very nature of this argument, this sociological perspective can be used to generate a deepened understanding of the debate on the extent of gun control laws. The Conflict Theory
Dr. Song, Clinical Decision Support has been defined as a “process for enhancing health-related decisions and actions with pertinent, organized clinical knowledge and patient information to improve healthcare, as well as, healthcare delivery (Campbell & CPHIMS, 2013). Clinical Decision-supporting tools are utilized to manage and support patient care. Healthcare information systems and information-retrieval systems are tools that manage information. There are various programs that provide custom tailored assessments or advice based on sets of patient specific data (Musen, Middleton, & Greenes, 2014, p. 701). Decision tools may follow simple logics (such as algorithms), may be based on decision theory, cost benefit analysis, or may use numerical approaches only as an adjunct to symbolic problem solving (Musen, Middleton, & Greenes, 2014, p. 701).
If information stored in the short-term memory is not learned and given attention, it will decay over time (Schunk 2012, p. 183). The short-term memory has a small capacity, and large amounts of information cannot all be stored (Schunk 2012, p. 183). To make it esier, information can be shortened or broken up to fit it in the short-term memory (Schunk 2012, p. 183). Information that is used will be transferred into the long-term store/ long-term memory (Schunk 2012, p. 183). There are different strategies to strengthen the memory of information from short-term to long-term.
A Study of the District Health Information System in Mandera County, Kenya Study Justification Improvement in health is recognized as a central factor for development in poor countries. Public health decision making is critically dependent on accurate, timely and reliable information to improve development. To make decision-making possible, managers and policy makers need good information about the current situation of the health of the population. To acquiring good information, a well-functioning health information system (HIS) for gathering, processing, analyzing and using health information is needed (WHO, 2007). There is a widespread belief that most of the national and sub-national health information systems fail in providing much needed information support for evidence based health planning and interventions.
Many of us face difficulties when it comes to decision making. When we are well connected with many different people and by asking them for their opinion, we can obtain many different
Given the pros and cons of rational decision-making, the healthcare environment may not be the best fit for the willful choice model. Within healthcare organizations, chaos is prevalent with little time to thoroughly analyze a problem and produce
Based upon the analysis, Parnas’ article is geared more towards people involved in the field of Artificial Intelligence where Eldridge’s article is geared towards people who are not necessarily knowledgeable about Artificial Intelligence yet are interested to learn more about the topic. Throughout the article, Parnas maintains the skeptical attitude towards Artificial Intelligence, literally ending with “Devices that use heuristics to create the illusion of Intelligence present a risk we should not accept” (Parnas, 6). Eldridge on the other hand, maintains a positive attitude throughout the article despite the shortcomings of AI. Together, both authors provide compelling arguments for and against Artificial
INTRODUCTION Have you ever thought on how people explain about behaviour? How do we know when learning process has occurred? Learning is permanent change that happened in the way of your behaviour acts, arises from experience one’s had gone through. This kind of learning and experience are beneficial for us to adapt with new environment or surrounding (Surbhi, 2018). The most simple form of learning is conditioning which is divided into two categories which are operant conditioning and classical conditioning.
Remarks made during this conference concluded that the major achievements of AI are going to be reached soon [7]. Artificial Intelligence is a major component in many solutions to areas in medicine such as logistics, data mining, image processing, genetics and molecular medicine [7]. The power and flexibility these solutions can provide better healthcare options for patients as well as a lower chance of a misdiagnosis [6]. Mario Stefanelli, a panelist at the AIME conference in 2007, had this to say about one important aspect of AI: “Knowledge management (KM) is one of the most interesting AI fields. The goal of KM is to improve organizational performance by enabling individuals to capture, share and apply their collective knowledge to make optimal 'decisions in real time'...
SIGNIFICANCE OF THE CONCEPTS Behaviorist learning theory (module 1.2) I chose this topic because it has many applications to and utilities. Behaviorism aims to study the laws of association between a stimulus (S) and response (R) of the animal with respect to this stimulus (info). It will be dominant for a half century. These connectionist theories or associationists consider that the individual receives stimuli and sends responses.
I do not believe the field has been developed to its potential in any regard, and feel that considerable progress can be made to improve the interactive experience that users have with an artificial intelligence application. This genuine intrigue combined with my curiosity for the subject matter and the limitless potential of the field are the reason why I wish to pursue a greater depth of knowledge in artificial