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PubMedDate not supplied

Data mining as a tool for research and knowledge development in nursing.

The ability to collect and store data has grown at a dramatic rate in all disciplines over the past two decades. Healthcare has been no exception. The shift toward evidence-based practice and outcomes research presents significant opportunities and challenges to extract meaningful information from massive amounts of clinical data to transform it into the best available knowledge to guide nursing practice. Data mining, a step in the process of Knowledge Discovery in Databases, is a method of unearthing information from large data sets. Built upon statistical analysis, artificial intelligence, and machine learning technologies, data mining can analyze massive amounts of data and provide useful and interesting information about patterns and relationships that exist within the data that might otherwise be missed. As domain experts, nurse researchers are in ideal positions to use this proven technology to transform the information that is available in existing data repositories into useful and understandable knowledge to guide nursing practice and for active interdisciplinary collaboration and research.

Algorithms↗
PubMed2004

Feature selection in MLPs and SVMs based on maximum output information.

This paper presents feature selection algorithms for multilayer perceptrons (MLPs) and multiclass support vector machines (SVMs), using mutual information between class labels and classifier outputs, as an objective function. This objective function involves inexpensive computation of information measures only on discrete variables; provides immunity to prior class probabilities; and brackets the probability of error of the classifier. The maximum output information (MOI) algorithms employ this function for feature subset selection by greedy elimination and directed search. The output of the MOI algorithms is a feature subset of user-defined size and an associated trained classifier (MLP/SVM). These algorithms compare favorably with a number of other methods in terms of performance on various artificial and real-world data sets.

Algorithms↗
PubMed1997

Practitioner review: physical investigations in mental retardation.

Mental retardation occurs in more than 1% of the child population. A cause can be found in almost 80% of individuals with severe mental retardation, but in fewer than 40% of those with mild mental retardation. A work-up is indicated in all cases of mental retardation. A medical doctor with specific training in the field is needed to make "decision-tree diagnosis" and to suggest the most appropriate physical investigations in each case. This paper provides practical guidelines for diagnosis and work-up both in severe and mild mental retardation.

Adolescent↗
PubMedDate not supplied

Promoting the online use of radiology appropriateness criteria.

Radiology appropriateness criteria and practice guidelines seek to promote the cost-effective use of radiology procedures and interventions and can be most useful when integrated with electronic patient records and order-entry systems. The task of translating practice guidelines into computer-based formats can highlight deficiencies and lead to revisions that make them more useful. Computer-based practice guidelines can include additional didactic material, such as images, videos, sounds, simulations, and links to bibliographic databases. Given patient data, information systems can select the most appropriate intervention automatically; some systems can function autonomously. Knowledge representation schemes can make appropriateness criteria available across a wide variety of computer platforms. Internet-based tools can allow developers to collaborate across an institution or around the globe. Information systems can bring appropriateness criteria to physicians at the point of care. The use of standardized approaches is important to ensure that appropriateness criteria reach the broadest possible audience and that such efforts can be incorporated easily into automated systems.

Artificial Intelligence↗
PubMed2004

Basic problems of serological laboratory diagnosis.

Serological laboratory diagnosis of infectious diseases is inflicted with several kinds of basic problems. One difficulty relates to the fact that the serological diagnosis of infectious diseases is double indirect: The first indirect aim in diagnosing an infectious disease is to identify the microbial agent that caused the disease. The second indirect aim is to identify this infectious agent by measuring the patient's immune response to the potential agent. Thus, the serological test is neither measuring directly disease nor the cause of the disease, but the patient's immune system. The latter poses another type of problem, because each person's immune system is unique. The immune response to an infectious agent is usually of polyclonal nature, and the exact physicochemical properties of antibodies are unique for each clone of antibody. The clonal makeup and composition and, therefore, the way an individual's immune system sees an infectious agent, depends not only on the genetic background of the person but also on the individual experience from former encounters with various infectious agents. In consequence, the reaction of a patient's serum in an analytical system is not precisely predictable. Also, the antigenic makeup of an infectious agent is not always foreseeable. Antigenic variations leading to different serotypes is a quite common phenomenon. Altogether, these biological problems lead to complexities in selecting the appropriate tests and strategies for testing, in interpreting the results, and in standardizing serological test systems. For that reason, a close collaboration of the laboratory with the clinic is mandatory to avoid erroneous conclusions from serological test results, which might lead to wrong decisions in patient care.

Antibody Affinity↗
PubMedDate not supplied

The mental life of artifacts. Explications, questions, arguments.

Our working assumption reads: Every mental property exhibited by a natural system can be exhibited by an artificial system, at least in principle. Is this true? The paper is in two parts. The first part is explicative: What do we mean by 'natural' or by 'artificial'? When are we ready to ascribe thinking to a system? We show that sometimes behavior is enough, sometimes underlying mechanisms are decisive. We study the difference between simulation and realization of a property and the kinds of explanation used in the philosophy of mind. The second part is argumentative. We collect and discuss positive arguments for and critical arguments against the working hypothesis. No decisive counterargument is found.

Artifacts↗
PubMed2005

Security architecture for health grid using ambient intelligence.

OBJECTIVES: To propose a novel approach of incorporating ambient intelligence in the health grid security architecture. METHODS: Security concerns are severely impeding the grid community effort in spreading its wings in health applications. In this paper, we have proposed a high level approach to incorporate ambient intelligence for health grid security architecture and have argued that this will significantly improve the current state of the grid security paradigm with an enhanced user-friendly environment. We believe that the time is right to shift the onus of traditional security mechanisms onto the new technologies. The incorporation of ambient intelligence in the security architecture of a grid will not only render a security paradigm robust but also provide an attractive vision for the future of computing by bringing the two worlds together. RESULTS: In this article we propose an evolutionary approach of utilizing smart devices for grid security architecture. We argue that such an infrastructure will impart unique features to the existing grid security paradigms by offering fortified and relentless monitoring. This new security architecture will be comprehensive in nature but will not be cumbersome for the users due to its typical characteristics of not prying into their lives and adapting to their needs. CONCLUSIONS: We have identified a new paradigm of the security architecture for a health grid that will not only render a security mechanism robust but will also provide the high levels of user-friendliness. As our approach is a first contribution to this problem, a number of other issues for future research remain open. However, the prospects are fascinating.

Access to Information↗
PubMed1992

Toward normative expert systems: Part I. The Pathfinder project.

Pathfinder is an expert system that assists surgical pathologists with the diagnosis of lymph-node diseases. The program is one of a growing number of normative expert systems that use probability and decision theory to acquire, represent, manipulate, and explain uncertain medical knowledge. In this article, we describe Pathfinder and our research in uncertain-reasoning paradigms that was stimulated by the development of the program. We discuss limitations with early decision-theoretic methods for reasoning under uncertainty and our initial attempts to use non-decision-theoretic methods. Then, we describe experimental and theoretical results that directed us to return to reasoning methods based in probability and decision theory.

Artificial Intelligence↗