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At least 685 records · Page 38Linked to original sources
PubMed2001

Processing and representation of meta-data for sleep apnea diagnosis with an artificial intelligence approach.

In this article, we revise and try to resolve some of the problems inherent in questionnaire screening of sleep apnea cases and apnea diagnosis based on attributes which are relevant and reliable. We present a way of learning information about the relevance of the data, comparing this with the definition of the information by the medical expert. We generate a predictive data model using a data aggregation operator which takes relevance and reliability information about the data into account to produce a diagnosis for each case. We also introduce a grade of membership for each question response which allows the patient to indicate a level of confidence or doubt in their own judgement. The method is tested with data collected from patients in a Sleep Clinic using questionnaires specially designed for the study. Other artificial intelligence predictive modeling algorithms are also tested on the same data and their predictive accuracy compared to that of the aggregation operator.

Adult↗
PubMed1997

Visualization of clinical data with neural networks, case study: polycystic ovary syndrome.

In medicine, the use of neural networks has concentrated mainly on classification problems. Clinicians are often interested in knowing what a patient's status is compared with other similar cases. Compared with biostatistics neural networks have one major drawback: the reliability of the classification is difficult to express. Therefore, clear visualization of the measurements can be more helpful than the calculated probability of a disease. The self-organizing map is the most widely used neural network for data visualization. Although, visualization can be attached to almost any feed-forward network as well. In this paper, we describe a topology-preserving feed-forward network and compare it with the self-organizing map. The two neural network models are used in a case study on the diagnosis of polycystic ovary syndrome, which is a common female endocrine disorder characterized by menstrual abnormalities, hirsutism and infertility.

Adolescent↗
PubMed1996

Motivated closing of the mind: "seizing" and "freezing".

A theoretical framework is outlined in which the key construct is the need for (nonspecific) cognitive closure. The need for closure is a desire for definite knowledge on some issue. It represents a dimension of stable individual differences as well as a situationally evocable state. The need for closure has widely ramifying consequences for social-cognitive phenomena at the intrapersonal, interpersonal, and group levels of analysis. Those consequences derive from 2 general tendencies, those of urgency and performance. The urgency tendency represents an individual's inclination to attain closure as soon as possible, and the permanence tendency represents an individual's inclination to maintain it for as long as possible. Empirical evidence for present theory attests to diverse need for closure effects on fundamental social psychological phenomena, including impression formation, stereotyping, attribution, persuasion, group decision making, and language use in intergroup contexts.

Cognition↗
PubMed1975

Age differences in the speed of a spatial cognitive process.

Older subjects responded slower than younger subjects on a spatial task which required them to rotate mentally a perspectively drawn figure in order to judge whether it was congruent with a second figure. Decision time was partitioned into two components: (1) the time taken for the "mental rotation" process, (2) the time taken for the remaining processes such as stimulus encoding and motor output. Both components required more time in the elderly group. The increase with age in the component of decision time representing rotational processes was similar to those increases seen by other investigators examining age-dependent differences in time required for memory-scanning processes using verbal stimuli.

Adolescent↗
PubMed2002

Recent innovations in intensive care unit risk-prediction models.

During the past 20 years, ICU risk-prediction models have undergone significant development, validation, and refinement. Among the general ICU severity of illness scoring systems, the Acute Physiology and Chronic Health Evaluation (APACHE), Mortality Prediction Model (MPM), and the Simplified Acute Physiology Score (SAPS) have become the most accepted and used. To risk-adjust patients with longer, more severe illnesses like sepsis and acute respiratory distress syndrome, several models of organ dysfunction or failure have become available, including the Multiple Organ Dysfunction Score (MODS), the Sequential Organ Failure Assessment (SOFA), and the Logistic Organ Dysfunction Score (LODS). Recent innovations in risk adjustment include automatic physiology and diagnostic variable retrieval and the use of artificial intelligence. These innovations have the potential of extending the uses of case-mix and severity-of-illness adjustment in the areas of clinical research, patient care, and administration. The challenges facing intensivists in the next few years are to further develop these models so that they can be used throughout the IUC stay to assess quality of care and to extend them to more specific patient groups such as the elderly and patients with chronic ICU courses.

Artificial Intelligence↗
PubMed2003

Intelligent machines in the twenty-first century: foundations of inference and inquiry.

The last century saw the application of Boolean algebra to the construction of computing machines, which work by applying logical transformations to information contained in their memory. The development of information theory and the generalization of Boolean algebra to Bayesian inference have enabled these computing machines, in the last quarter of the twentieth century, to be endowed with the ability to learn by making inferences from data. This revolution is just beginning as new computational techniques continue to make difficult problems more accessible. Recent advances in our understanding of the foundations of probability theory have revealed implications for areas other than logic. Of relevance to intelligent machines, we recently identified the algebra of questions as the free distributive algebra, which will now allow us to work with questions in a way analogous to that which Boolean algebra enables us to work with logical statements. In this paper, we examine the foundations of inference and inquiry. We begin with a history of inferential reasoning, highlighting key concepts that have led to the automation of inference in modern machine-learning systems. We then discuss the foundations of inference in more detail using a modern viewpoint that relies on the mathematics of partially ordered sets and the scaffolding of lattice theory. This new viewpoint allows us to develop the logic of inquiry and introduce a measure describing the relevance of a proposed question to an unresolved issue. Last, we will demonstrate the automation of inference, and discuss how this new logic of inquiry will enable intelligent machines to ask questions. Automation of both inference and inquiry promises to allow robots to perform science in the far reaches of our solar system and in other star systems by enabling them not only to make inferences from data, but also to decide which question to ask, which experiment to perform, or which measurement to take given what they have learned and what they are designed to understand.

Artificial Intelligence↗
PubMed2004

A database for therapy evaluation in neurological disorders: application in epilepsy.

Due to the high diversity of cases and the variety of treatment modalities, the information to be managed and processed in therapy evaluation is excessive in many neurological diseases. Additionally, in order to gain more insight into, not only the disorders, but also the applied therapies, more objectivity is requested in clinical practice. Therefore, in this study, a database system is designed for assisting therapy evaluation in neurological diseases. A sample implementation is accomplished for structuring and managing the information accessible for different treatment modalities available for epilepsy. The first application of the developed database system for clinical use in epilepsy is tested with selected quantitative parameters extracted from electrophysiological signals of epilepsy patients who receive neurotherapy as a complementary treatment to the pharmacological therapy, as well as of voluntary control subjects. The system contributes to the automation of statistical analysis for therapy planning, monitoring, and evaluation. It is extendible for other disorders such as attention deficit hyperactivity disorder, depression, and sleep disorders.

Algorithms↗
PubMed2005

A multiagent system enhancing home-care health services for chronic disease management.

In this paper, a multiagent system (MAS) is presented, aiming to enhance monitoring, surveillance, and educational services of a generic medical contact center (MCC) for chronic disease management. In such a home-care scenario, a persistent need arises for efficiently monitoring the patient contacts and the MCC's functionality, in order to effectively manage and interpret the large volume of medical data collected during the patient sessions with the system, and to assess the use of MCC resources. Software agents were adopted to provide the means to accomplish such real-time information-processing tasks, due to their autonomous, reactive and/or proactive nature, and their effectiveness in dynamic environments by incorporating coordination strategies. Specifically, the objective of the MAS is to monitor the MCC environment, detect important cases, and inform the healthcare and administrative personnel via alert messages, notifications, recommendations, and reports, prompting them for actions. The main aim of this paper is to present the overall design and implementation of a proposed MAS, emphasizing its functional model and architecture, as well as on the agent interactions and the knowledge-sharing mechanism incorporated, in the context of a generic MCC.

Artificial Intelligence↗
PubMed2006

A novel evolutionary drug scheduling model in cancer chemotherapy.

In this paper, we introduce a modified optimal control model of drug scheduling in cancer chemotherapy and a new adaptive elitist-population-based genetic algorithm (AEGA) to solve it. Working closely with an oncologist, we first modify the existing model, because its equation for the cumulative drug toxicity is inconsistent with medical knowledge and clinical experience. To explore multiple efficient drug scheduling policies, we propose a novel variable representation--a cycle-wise representation, and modify the elitist genetic search operators in the AEGA. The simulation results obtained by the modified model match well with the clinical treatment experiences, and can provide multiple efficient solutions for oncologists to consider. Moreover, it has been shown that the evolutionary drug scheduling approach is simple, and capable of solving complex cancer chemotherapy problems by adapting multimodal versions of evolutionary algorithms.

Algorithms↗
PubMed2004

An adaptive Hinfinity controller design for bank-to-turn missiles using ridge Gaussian neural networks.

A new autopilot design for bank-to-turn (BTT) missiles is presented. In the design of autopilot, a ridge Gaussian neural network with local learning capability and fewer tuning parameters than Gaussian neural networks is proposed to model the controlled nonlinear systems. We prove that the proposed ridge Gaussian neural network, which can be a universal approximator, equals the expansions of rotated and scaled Gaussian functions. Although ridge Gaussian neural networks can approximate the nonlinear and complex systems accurately, the small approximation errors may affect the tracking performance significantly. Therefore, by employing the Hinfinity control theory, it is easy to attenuate the effects of the approximation errors of the ridge Gaussian neural networks to a prescribed level. Computer simulation results confirm the effectiveness of the proposed ridge Gaussian neural networks-based autopilot with Hinfinity stabilization.

Algorithms↗
PubMed2004

Learning mixture models with the regularized latent maximum entropy principle.

This paper presents a new approach to estimating mixture models based on a recent inference principle we have proposed: the latent maximum entropy principle (LME). LME is different from Jaynes' maximum entropy principle, standard maximum likelihood, and maximum aposteriori probability estimation. We demonstrate the LME principle by deriving new algorithms for mixture model estimation, and show how robust new variants of the expectation maximization (EM) algorithm can be developed. We show that a regularized version of LME (RLME), is effective at estimating mixture models. It generally yields better results than plain LME, which in turn is often better than maximum likelihood and maximum a posterior estimation, particularly when inferring latent variable models from small amounts of data.

Algorithms↗
PubMed2004

A new criterion using information gain for action selection strategy in reinforcement learning.

In this paper, we regard the sequence of returns as outputs from a parametric compound source. Utilizing the fact that the coding rate of the source shows the amount of information about the return, we describe l-learning algorithms based on the predictive coding idea for estimating an expected information gain concerning future information and give a convergence proof of the information gain. Using the information gain, we propose the ratio w of return loss to information gain as a new criterion to be used in probabilistic action-selection strategies. In experimental results, we found that our w-based strategy performs well compared with the conventional Q-based strategy.

Algorithms↗
PubMed2004

Entropy-based kernel mixture modeling for topographic map formation.

A new information-theoretic learning algorithm for kernel-based topographic map formation is introduced. In the one-dimensional case, the algorithm is aimed at uniformizing the cumulative distribution of the kernel mixture densities by maximizing its differential entropy. A nonparametric differential entropy estimator is used on which normalized gradient ascent is performed. Both differentiable and nondifferentiable kernels are in principle supported, such as Gaussian and rectangular (on/off) kernels. The relation is shown with joint entropy maximization of the kernel outputs. The learning algorithm's performance is assessed and compared with the theoretically optimal performance. A fixed-point rule is derived for the case of heterogeneous kernel mixtures. Finally, an extension of the algorithm to the multidimensional case is suggested.

Algorithms↗
PubMed2004

From blind signal extraction to blind instantaneous signal separation: criteria, algorithms, and stability.

This paper reports a study on the problem of the blind simultaneous extraction of specific groups of independent components from a linear mixture. This paper first presents a general overview and unification of several information theoretic criteria for the extraction of a single independent component. Then, our contribution fills the theoretical gap that exists between extraction and separation by presenting tools that extend these criteria to allow the simultaneous blind extraction of subsets with an arbitrary number of independent components. In addition, we analyze a family of learning algorithms based on Stiefel manifolds and the natural gradient ascent, present the nonlinear optimal activations (score) functions, and provide new or extended local stability conditions. Finally, we illustrate the performance and features of the proposed approach by computer-simulation experiments.

Algorithms↗
PubMed2004

Probabilistic sequential independent components analysis.

Under-complete models, which derive lower dimensional representations of input data, are valuable in domains in which the number of input dimensions is very large, such as data consisting of a temporal sequence of images. This paper presents the under-complete product of experts (UPoE), where each expert models a one-dimensional projection of the data. Maximum-likelihood learning rules for this model constitute a tractable and exact algorithm for learning under-complete independent components. The learning rules for this model coincide with approximate learning rules proposed earlier for under-complete independent component analysis (UICA) models. This paper also derives an efficient sequential learning algorithm from this model and discusses its relationship to sequential independent component analysis (ICA), projection pursuit density estimation, and feature induction algorithms for additive random field models. This paper demonstrates the efficacy of these novel algorithms on high-dimensional continuous datasets.

Algorithms↗
PubMed2004

Gradient-based manipulation of nonparametric entropy estimates.

This paper derives a family of differential learning rules that optimize the Shannon entropy at the output of an adaptive system via kernel density estimation. In contrast to parametric formulations of entropy, this nonparametric approach assumes no particular functional form of the output density. We address problems associated with quantized data and finite sample size, and implement efficient maximum likelihood techniques for optimizing the regularizer. We also develop a normalized entropy estimate that is invariant with respect to affine transformations, facilitating optimization of the shape, rather than the scale, of the output density. Kernel density estimates are smooth and differentiable; this makes the derived entropy estimates amenable to manipulation by gradient descent. The resulting weight updates are surprisingly simple and efficient learning rules that operate on pairs of input samples. They can be tuned for data-limited or memory-limited situations, or modified to give a fully online implementation.

Algorithms↗