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At least 109 records · Page 6Linked to original sources
PubMed2001

Cognitive subprocesses of mental rotation: why is a good rotator better than a poor one?

The Vanderberg-Kuse Mental Rotation Test is a standard test of mental rotation ability. Recent experiments have demonstrated that mental rotation is a complex cognitive process wherein different subprocesses (focused attention, visual scanning, perceptual decision, visual memory) play important roles in performance. We classified the population as good and poor rotators by performance of mental rotation (ns = 47: 22 men and 25 women, respectively; mean age: 20.7 yr.). To examine differences cognitive subprocesses of mental rotation of these two groups were compared. There were significant differences between poor and good rotators in performance on Raven's test and the Pieron Focused Attention test scores. The good rotators scored better because their perceptual decision-analytical intelligence (Raven) and focused attention scores were higher.

Adult↗
PubMed2001

Hybrid expert system for decision supporting in the medical area: complexity and cognitive computing.

This paper proposes a hybrid expert system (HES) to minimise some complexity problems pervasive to the artificial intelligence such as: the knowledge elicitation process, known as the bottleneck of expert systems; the model choice for knowledge representation to code human reasoning; the number of neurons in the hidden layer and the topology used in the connectionist approach; the difficulty to obtain the explanation on how the network arrived to a conclusion. Two algorithms applied to developing of HES are also suggested. One of them is used to train the fuzzy neural network and the other to obtain explanations on how the fuzzy neural network attained a conclusion. To overcome these difficulties the cognitive computing was integrated to the developed system. A case study is presented (e.g. epileptic crisis) with the problem definition and simulations. Results are also discussed.

Algorithms↗
PubMed1994

Planning diagnostic imaging work-up strategies using case-based reasoning.

ISIS is a developmental decision support system that helps physicians select diagnostic imaging procedures. It uses case-based reasoning, an artificial-intelligence approach that emphasizes reasoning and planning from prior experience. The development, training, and evaluation of a prototype system were used to guide the development of ISIS. To realize a clinically useful system, particular emphasis has been placed on increasing the depth and breadth of case-based knowledge, enhancing the explanatory capabilities of the system, and refining the human-computer interfaces to include a critiquing approach.

Algorithms↗
PubMedDate not supplied

Automated rule-based decision systems in forensic toxicology using expert knowledge: basic principles and practical applications.

This paper presents the basic principles and practical benefits of the application of expert systems (ES) and artificial intelligence (AI) to problem solving in forensic toxicology. We acknowledge the complexity and elegance of the theoretical substance and program algorithms of existing work in these disciplines, while simultaneously observing that many presentations of this material cloak the essential facts and concepts in unnecessary jargon and hyperbole. We attempt to remove the cloak without misrepresenting or oversimplifying the underlying structures. We first present a summary of the history, basic functions, technical fundamentals, and typical applications in three major categories of established ES/AI systems. We then assess the status of ES/AI in the forensic toxicology laboratory (FTL) with emphasis on potential applications. We conclude with an analysis of experiences with ESs in our laboratory where we have used an integrated expert system to reduce laboratory errors, detect internal inconsistencies in data, discover new substance abuse subpopulations, and reduce the frequency of sample reprocessing. We have minimized specimen processing time and instrument wear while maximizing technician efficiency and thus performing more tests for the same or reduced costs.

Artificial Intelligence↗
PubMed2004

On the role of anxiety in decisions under possiblistic uncertainty.

We focus on situations in which we must decide on what time to take an action. The action is not in question it is the time of action. We call these "time for action decisions," a prototypical example being deciding when to leave on a journey. We point out that during this type of decision process, the decision-maker recognizes two groups of forces acting on him: one that pushes him to act now and other that pushes him act later. We note that the strength of these forces depends on the information available about various uncertainties associated with the situation. It also strongly depends upon the personality of the decision-maker. We observe that as time passes these conflicting forces tend to build up an anxiety in the decision-maker resulting in an action being taken at a time of most intense anxiety. In this paper using the ideas of possibility and necessity measures to enable different interpretations of uncertain information we investigate the temporal profile of the decision-maker's anxiety as a function of their decision attitude. We investigate the role of maximization of anxiety as decision paradigm. One of our goals here is to try to understand role of the nature and the quality information plays in these types of decisions as well as its interaction with anxiety.

Anxiety↗
PubMed1997

ECG recognition by Boolean decision rules.

The classical pattern recognition problem is considered. A model of construction of Boolean decision rules is implemented. Computational procedures for construction of non-reducible descriptors is briefly discussed. Applications of Boolean decision rules to ECG analysis and ECG recognition are suggested.

Artificial Intelligence↗
PubMed1999

Distributing knowledge maintenance for clinical decision-support systems: the "knowledge library" model.

The maintenance of knowledge-rich clinical decision-support systems is challenging, in particular in the complex setting of a large academic medical center. Distributing the maintenance tasks to the source of expertise can address scalability, accuracy and currency issues. It also helps to foster a more global sense of ownership among the system users. The knowledge maintenance model must provide processes and tools to deal with a wide range of stakeholders (resident and attending physicians, consulting specialists, other care providers, case managers, ancillary departments), with knowledge embedded in legacy departmental systems, and with the continuous evolution of the content and form of the knowledge base. We describe and illustrate the "knowledge library" model in use at Vanderbilt University Medical Center for the distributed maintenance of the integrated knowledge base that drives the WizOrder clinical decision-support, physician order entry, and notes capture system.

Artificial Intelligence↗
PubMed2001

Using statistical decision theory to predict speech intelligibility. II. Measurement and prediction of consonant-discrimination performance.

The speech recognition sensitivity (SRS) model [H. Müsch and S. Buus, J. Acoust. Soc. Am. 109, 2896-2909 (2001)] was tested by applying it to consonant-discrimination data collected in this study. Normally hearing listeners' abilities to discriminate among 18 consonants were measured in 58 filter conditions using two test paradigms. In one paradigm, listeners chose among all 18 stimuli. In the other, response alternatives were restricted to the correct response and eight consonants that were randomly selected among the 17 incorrect response alternatives. The effect of the number of response alternatives on performance can be described by statistical decision theory. Most filter conditions included one or more sharply filtered narrow bands of speech. Depending on the selection of bands, listeners' performance in multi-band conditions falls short of, equals, or exceeds the performance expected from multiplication of the error rates in the individual bands. The performance advantage in multi-band conditions increases with average band separation. The SRS model provides a good fit to the data and predicts the data more accurately than does the speech intelligibility index.

Adult↗
PubMed1996

An intelligent information systems architecture for clinical decision support on the Internet.

This paper presents a prototype of an agent-based intelligent information systems architecture that can provide clinical decision support in a distributed, heterogeneous environment such as the Internet. After presenting the architecture, a specific transaction sequence is detailed and implemented to test the architecture. A transaction sequence is a detailed analysis of all actions by all entities to accomplish the system goal. In this case, the goal is to give decision support information access to a provider in the context of a computerized patient record. Based on the results of the prototype implementation, we argue that the system is scaleable and discuss other transactions, standards, and needed development.

Computer Communication Networks↗
PubMed2000

Tacit knowledge--an epistemological framework and implications for research in doctors information needs.

We propose an epistemological framework of medical knowledge which might guide research and attempts in fulfilling doctors information needs. Tacit knowledge is influencing information needs and medical work but it is epistemological unresolved whether it is possible to articulate all this tacit knowledge and thus making it accessible to decision support system programming. Tacit knowledge might explain the difficulties in building successful decision support systems and should be taken into account in research.

Artificial Intelligence↗
PubMed2002

GLEE--a model-driven execution system for computer-based implementation of clinical practice guidelines.

We have developed the GLEE system for execution of guidelines encoded in the GLIF3 format. This system can be integrated with a local clinical information system through standard interfaces to EMRs and clinical applications. The execution model of GLEE takes the "system suggests, user controls" approach. A tracing system is used to record the state of guideline steps and their transitions. GLEE provides an internal event-driven execution model that can be hooked up with the clinical event monitor in a local environment. We discuss the execution flexibility provided by GLEE and issues related to its integration in a local environment. Potential use of GLEE includes clinical decision support, quality assurance, guideline development and medical education.

Algorithms↗
PubMedDate not supplied

Performance of a neural network trained to make third-molar treatment-planning decisions.

The authors developed and tested 12 neural networks of different architectures to make lower-third-molar treatment-planning decisions, using a software-based neural network (Neudesk 1.2, Neural Computer Sciences, Southampton, UK). Network training was undertaken using clinical histories from 119 patients (with 238 lower third molars) referred for treatment planning (79 females and 40 males, mean age 25 years) together with output data consisting of actual treatments planned by a senior oral surgeon. Both the input clinical data and the consultant decisions were treated on a tooth-wise basis and were coded to numerical values. Binary data (e.g., present/absent) were coded to 1 and 0, while quantitative data (e.g., age) were scaled to fall between 0 and 1. A network based on the optimal architecture was trained and then interrogated with test data derived from a further 174 patients (119 females and 55 males, mean age 26 years) with 348 lower third molars. Network decisions were dichotomized with a threshold of 0.8. With no knowledge of the network decisions, the senior oral surgeon indicated his preferred treatments. The teeth were then assigned to "gold-standard" categories of indications present or absent based on National Institutes of Health consensus criteria. Against this, the network achieved a sensitivity of 0.78, which was slightly inferior to that of the oral surgeon (0.88), although this difference was not significant, and a specificity of 0.98, compared with 0.99 for the oral surgeon (p = NS). Agreement between the oral surgeon and network decisions was very high (kappa = 0.850). This study demonstrates that it is possible to train a neural network to provide reliable decision support for lower-third-molar treatment planning.

Adult↗
PubMedDate not supplied

Query-handling in MLM-based decision support systems.

Arden Syntax for Medical Logic Modules is a standard specification for creation and sharing of knowledge bases. The standard specification focuses on knowledge that can be represented as a set of independent Medical Logic Modules (MLMs) such as rules, formulas and protocols. The basic functions of an MLM are to retrieve patient data, manipulate the data, come to some decision, and possibly perform an action. All connections to the world outside an MLM are collected in the data-slot of the MLM. The institution specific parts of these connections are inside the notation of curly brackets ([]) to facilitate sharing of MLM between institutions. This paper focuses on some of the problems that occur in relation to Arden Syntax and connections to a patient database such as database queries. Problems related to possibilities of moving one or several module(s) are also discussed, with emphasis on database connections. As an example, an MLM based Decision Support System (DSS) developed at Linköping University is described.

Artificial Intelligence↗
PubMed1991

Representation requirements for supporting knowledge-based construction of decision models in medicine.

This paper analyzes the medical knowledge required for formulating decision models in the domain of pulmonary infectious diseases (PIDs) with acquired immunodeficiency syndrome (AIDS). Aiming to support dynamic decision-modeling, the knowledge characterization focuses on the ontology of the clinical decision problem. Relevant inference patterns and knowledge types are identified.

Acquired Immunodeficiency Syndrome↗