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PubMed2006

An improved stability criterion for T-S fuzzy discrete systems via vertex expression.

The stability criteria for Takagi-Sugeno (T-S) fuzzy discrete systems based on a weighting-dependent Lyapunov function have been studied in numerous literature. Most of those results need to find r (number of rules) positive matrices P(i)s to satisfy r2 Lyapunov inequalities. This paper combines the ideas of group-fired rules, estimation of the maximum distance between two successive states of the system, and the vertex expression of any point in a region together, so that the relaxed stability that satisfies fewer Lyapunov inequalities is derived. Finally, an example is illustrated to reveal the merit of the proposed stability criterion.

Algorithms↗
PubMed2006

A relative reward-strength algorithm for the hierarchical structure learning automata operating in the general nonstationary multiteacher environment.

A new learning algorithm for the hierarchical structure learning automata (HSLA) operating in the nonstationary multiteacher environment (NME) is proposed. The proposed algorithm is derived by extending the original relative reward-strength algorithm to be utilized in the HSLA operating in the general NME. It is shown that the proposed algorithm ensures convergence with probability 1 to the optimal path under a certain type of the NME. Several computer-simulation results, which have been carried out in order to compare the relative performance of the proposed algorithm in some NMEs against those of the two of the fastest algorithms today, confirm the effectiveness of the proposed algorithm.

Algorithms↗
PubMed2004

Nonlinear system modelling via optimal design of neural trees.

This paper introduces a flexible neural tree model. The model is computed as a flexible multi-layer feed-forward neural network. A hybrid learning/evolutionary approach to automatically optimize the neural tree model is also proposed. The approach includes a modified probabilistic incremental program evolution algorithm (MPIPE) to evolve and determine a optimal structure of the neural tree and a parameter learning algorithm to optimize the free parameters embedded in the neural tree. The performance and effectiveness of the proposed method are evaluated using function approximation, time series prediction and system identification problems and compared with the related methods.

Algorithms↗
PubMed1997

Classification of user expertise level by neural networks.

A neural network approach to low-level user modeling is described, in the context of text editing tasks using the Jove editor. Knowledge of a user's expertise is extracted automatically, based on their interaction with Jove over a two week period. A MLP classifier which uses rprop learning and incorporates output data fuzzification is developed to classify users into one of five expertise levels. Classification into the correct level is achieved in around 80% of the cases, with misclassification being restricted to adjacent classes. The neurofuzzy system is seen to outperform not only the binary classifier of Beale [1989], but also production rule and inductive expert systems developed especially for comparison purposes in this study.

Artificial Intelligence↗
PubMed2006

Machine learning for detection and diagnosis of disease.

Machine learning offers a principled approach for developing sophisticated, automatic, and objective algorithms for analysis of high-dimensional and multimodal biomedical data. This review focuses on several advances in the state of the art that have shown promise in improving detection, diagnosis, and therapeutic monitoring of disease. Key in the advancement has been the development of a more in-depth understanding and theoretical analysis of critical issues related to algorithmic construction and learning theory. These include trade-offs for maximizing generalization performance, use of physically realistic constraints, and incorporation of prior knowledge and uncertainty. The review describes recent developments in machine learning, focusing on supervised and unsupervised linear methods and Bayesian inference, which have made significant impacts in the detection and diagnosis of disease in biomedicine. We describe the different methodologies and, for each, provide examples of their application to specific domains in biomedical diagnostics.

Algorithms↗
PubMedDate not supplied

Multiparametric time course prognoses by means of case-based reasoning and abstractions of data and time.

In this paper we describe an approach to utilize Case-Based Reasoning methods for trend prognoses for medical problems. Since using conventional methods for reasoning over time does not fit for course predictions without medical knowledge of typical course pattern, we have developed abstraction methods suitable for integration into our Case-Based Reasoning system ICONS. These methods combine medical experience with prognoses of multiparametric courses. We have chosen the monitoring of the kidney function in an Intensive Care Unit (ICU) setting as an example for diagnostic problems. On the ICU, the monitoring system NIMON provides a daily report based on current measured and calculated kidney function parameters. We abstract these parameters to a daily kidney function state. Subsequently, we use these states to generate course-characteristic trend descriptions of the renal function over the course of time. Using Case-Based Reasoning retrieval methods, we search in the case base for courses similar to the current trend descriptions. Finally, we present the current course together with similar courses as comparisons and as possible prognoses to the user.

Artificial Intelligence↗
PubMed2004

Online model-based diagnosis to support autonomous operation of an advanced life support system.

This article describes methods for online model-based diagnosis of subsystems of the advanced life support system (ALS). The diagnosis methodology is tailored to detect, isolate, and identify faults in components of the system quickly so that fault-adaptive control techniques can be applied to maintain system operation without interruption. We describe the components of our hybrid modeling scheme and the diagnosis methodology, and then demonstrate the effectiveness of this methodology by building a detailed model of the reverse osmosis (RO) system of the water recovery system (WRS) of the ALS. This model is validated with real data collected from an experimental testbed at NASA JSC. A number of diagnosis experiments run on simulated faulty data are presented and the results are discussed.

Algorithms↗
PubMed2005

Online fault adaptive control for efficient resource management in Advanced Life Support Systems.

This article presents the design and implementation of a controller scheme for efficient resource management in Advanced Life Support Systems. In the proposed approach, a switching hybrid system model is used to represent the dynamics of the system components and their interactions. The operational specifications for the controller are represented by utility functions, and the corresponding resource management problem is formulated as a safety control problem. The controller is designed as a limited-horizon online supervisory controller that performs a limited forward search on the state-space of the system at each time step, and uses the utility functions to decide on the best action. The feasibility and accuracy of the online algorithm can be assessed at design time. We demonstrate the effectiveness of the scheme by running a set of experiments on the Reverse Osmosis (RO) subsystem of the Water Recovery System (WRS).

Algorithms↗
PubMed2003

Using genetic programming to discover nonlinear variable interactions.

Psychology has to deal with many interacting variables. The analyses usually used to uncover such relationships have many constraints that limit their utility. We briefly discuss these and describe recent work that uses genetic programming to evolve equations to combine variables in nonlinear ways in a number of different domains. We focus on four studies of interactions from lexical access experiments and psychometric problems. In all cases, genetic programming described nonlinear combinations of items in a manner that was subsequently independently verified. We discuss the general implications of genetic programming and related computational methods for multivariate problems in psychology.

Adult↗
PubMed1996

The expert surgical assistant. An intelligent virtual environment with multimodal input.

Virtual Reality has made computer interfaces more intuitive but not more intelligent. This paper shows how an expert system can be coupled with multimodal input in a virtual environment to provide an intelligent simulation tool or surgical assistant. This is accomplished in three steps. First, voice and gestural input is interpreted and represented in a common semantic form. Second, a rule-based expert system is used to infer context and user actions from this semantic representation. Finally, the inferred user actions are matched against steps in a surgical procedure to monitor the user's progress and provide automatic feedback. In addition, the system can respond immediately to multimodal commands for navigational assistance and/or identification of critical anatomical structures. To show how these methods are used we present a prototype sinus surgery interface. The approach described here may easily be extended to a wide variety of medical and non-medical training applications by making simple changes to the expert system database and virtual environment models. Successful implementation of an expert system in both simulated and real surgery has enormous potential for the surgeon both in training and clinical practice.

Artificial Intelligence↗
PubMed1997

Telematics for clinical guidelines: a conceptual modelling approach.

PRESTIGE is a project for applying telematics to assist the dissemination and application of clinical practice guidelines and protocols. Previous publications have described PRESTIGE's technical approach, including the use of a generic model for representing the knowledge content of clinical guidelines. This approach offers the possibility of 'plug-and-play' electronic distribution of clinical guidelines produced by multiple authoring bodies for use on multiple healthcare clinical management software platforms. A recent joint workshop held with the Section on Medical Informatics, Stanford University School of Medicine compared the European consensus approach developed in PRESTIGE with a parallel series of projects for computer-assisted protocol-based healthcare undertaken at Stanford and other American centres over the past, which confirmed the convergence and complementarity of our approaches, and holds out prospects of world-wide standardization in healthcare protocol knowledge representation. This paper summarises PRESTIGE conceptual model set which is the design of the project's approach.

Artificial Intelligence↗
PubMed1997

SMART: a system supporting medical activities in real-time.

This paper describes the system SMART whose goal is real-time assistance to physicians who execute diagnostic or therapeutic protocols in a clinical context. SMART is able to retrieve a protocol from its knowledge base and to monitor its execution step by step for a single patient. Different protocols for different patients can be followed at the same time in a health care structure. The prototype realized supports the execution of protocols for evaluating surgical risks. It has been implemented according to the specifications given by the 4th Surgical Clinic of "Policlinico Umberto I" and reflects the activities actually performed in that hospital. However, the protocol model defined is general purpose and we envisage an easy application to other contexts and therefore to the informatization of other protocols.

Artificial Intelligence↗
PubMed1998

Simple models for estimating dementia severity using machine learning.

Estimating dementia severity using the Clinical Dementia Rating (CDR) Scale is a two-stage process that currently is costly and impractical in community settings, and at best has an interrater reliability of 80%. Because staging of dementia severity is economically and clinically important, we used Machine Learning (ML) algorithms with an Electronic Medical Record (EMR) to identify simpler models for estimating total CDR scores. Compared to a gold standard, which required 34 attributes to derive total CDR scores, ML algorithms identified models with as few as seven attributes. The classification accuracy varied with the algorithm used with naïve Bayes giving the highest. (76%) The mildly demented severity class was the only one with significantly reduced accuracy (59%). If one groups the severity classes into normal, very mild-to-mildly demented, and moderate-to-severely demented, then classification accuracies are clinically acceptable (85%). These simple models can be used in community settings where it is currently not possible to estimate dementia severity due to time and cost constraints.

Algorithms↗