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At least 1,603 records · Page 89Linked to original sources
PubMed2000

Intelligent system for topic survey in MEDLINE by keyword recommendation and learning text characteristics.

We have implemented a system for assisting experts in selecting MEDLINE records for database construction purposes. This system has two specific features: The first is a learning mechanism which extracts characteristics in the abstracts of MEDLINE records of interest as patterns. These patterns reflect selection decisions by experts and are used for screening the records. The second is a keyword recommendation system which assists and supplements experts' knowledge in unexpected cases. Combined with a conventional keyword-based information retrieval system, this system may provide an efficient and comfortable environment for MEDLINE record selection by experts. Some computational experiments are provided to prove that this idea is useful.

Artificial Intelligence↗
PubMed2006

Automatic generation of spoken dialogue from medical plans and ontologies.

This paper presents some research undertaken as part of the EU-funded HOMEY project, into the application of intelligent dialogue systems to healthcare systems. The work presented here concentrates on the ways in which knowledge of underlying task structure (e.g., a medical guideline) can be combined with ontological knowledge (e.g., medical semantic dictionaries) to provide a basis for the automatic generation of flexible and re-configurable dialogue. This approach is next evaluated via a specific application that provides decision support to general practitioners to help determine whether or not a patient should be referred to a cancer specialist. The competence of the resulting dialogue application, its speech recognition performance, and dialogue performance are all evaluated to determine the applicability of this approach.

Artificial Intelligence↗
PubMed2006

Medical image segmentation using minimal path deformable models with implicit shape priors.

This paper presents a new method for segmentation of medical images by extracting organ contours, using minimal path deformable models incorporated with statistical shape priors. In our approach, boundaries of structures are considered as minimal paths, i.e., paths associated with the minimal energy, on weighted graphs. Starting from the theory of minimal path deformable models, an intelligent "worm" algorithm is proposed for segmentation, which is used to evaluate the paths and finally find the minimal path. Prior shape knowledge is incorporated into the segmentation process to achieve more robust segmentation. The shape priors are implicitly represented and the estimated shapes of the structures can be conveniently obtained. The worm evolves under the joint influence of the image features, its internal energy, and the shape priors. The contour of the structure is then extracted as the worm trail. The proposed segmentation framework overcomes the short-comings of existing deformable models and has been successfully applied to segmenting various medical images.

Algorithms↗
PubMed1997

An intelligent system for the tracking--localization of changes & exploratory analysis--of long-term ECG.

An interactive methodology for the analysis of long-term ECG is introduced. It is an anthropomimetic technique and consists of three parts. At the first stage, the clinicians' scan of the ECG traces is imitated and changes in the shape of QRS are quantified. In the sequel, a clinician involves in the interpretation of the most prominent changes providing the patient-dependent prototypes for the subsequent machine learning procedure. Finally, a classification scheme incorporates the portion of medical knowledge needed to explore the whole patient's ECG. This scheme, being very robust to noise, presents excellent generalization properties and can serve as a reliable automation in a future examination of the certain subject.

Arrhythmias, Cardiac↗
PubMed2001

Intelligent knowledge retrieval for decision support in medical applications.

Knowledge management and retrieval are key issues to be addressed in the medical domain, where a large amount of information is generally available, and where the expert's skills need to be properly shared across the organisation, with the aim of improving the quality of care. Case Based Reasoning (CBR) is a very well suited methodology for the Knowledge Management task, when knowledge is in the operative form. Nevertheless, also well-assessed, formalised medical knowledge, such as clinical guidelines, should be made available to physicians in order to optimise their reasoning process. To take advantage of both knowledge types, we have defined a Multi Modal Reasoning methodology, that integrates CBR and Rule Based Reasoning, for supporting context detection, information retrieval and therapy revision in diabetes care.

Artificial Intelligence↗
PubMed2006

Intelligent compensation of friction, ripple, and hysteresis via a regulated chatter.

In this paper, a hybrid control scheme utilizing a PID feedback control with an additional regulated chatter signal is developed to compensate motion impeding influences such as the effects due to friction, force ripples, and hysteresis in linear piezoelectric motor. The regulated chatter signal is a pulse sequence superimposed on the PID control signal. It has a fixed amplitude, and a pulse width regulated via iterative learning control. As such, the scheme is expected to be useful for applications involving iterative motion sequences. An analysis of the closed-loop performance is presented in the paper. Simulation and experimental results are also furnished to demonstrate that the proposed control scheme can reduce tracking errors significantly.

Algorithms↗
PubMed2000

Dimension reduction for highdimensional online-monitoring data in intensive care.

Nowadays high dimensional data in intensive care medicine can be captured, stored, and retrieved with the help of clinical information systems. Intelligent alarm systems are needed for an adequate bedside decision support, in the course of which the detection of qualitative patterns in physiologic monitoring data such as outliers, level changes, or trends aims at a proper classification of the patients state. Statistical time series techniques have already been applied successfully to the analysis of single physiological variables. The simultaneous online analysis of the multivariate patient curve yields further challenges. We describe methods for reducing the dimension and for keeping the computational efforts necessary for monitoring low. We present preliminary results of an ongoing study on monitoring critically ill patients.

Adult↗
PubMed1998

The use of Matlab for colour fuzzy representation of multichannel EEG short time spectra.

During the last years, a lot of EEG research efforts was directed to intelligent methods for automatic analysis of data from multichannel EEG recordings. However, all the applications reported were focused on specific single tasks like detection of one specific "event" in the EEG signal: spikes, sleep spindles, epileptic seizures, K complexes, alpha or other rhythms or even artefacts. The aim of this paper is to present a complex system being able to perform a representation of the dynamic changes in frequency components of each EEG channel. This representation uses colours as a powerful means to show the only one frequency range chosen from the shortest epoch of signal able to be processed with the conventional "Short Time Fast Fourier Transform" (S.T.F.F.T.) method.

Artificial Intelligence↗
PubMedDate not supplied

The fuzzy medical group in the centre for computational intelligence.

In this paper, five ongoing or completed research projects in medicine using fuzzy sets and logic are summarized. They are, a lightweight fuzzy process for diagnosis using fuzzy symptoms, prediction of pulmonary embolisms from linguistic descriptions of perfusion and ventilation scans, application of the fuzzy ART/MAP and MinMax/MAP neural network models to radiographic image classification, the development of a fuzzy expert system for the analysis of umbilical cord blood, modeling nursing intuition using type 2 fuzzy sets. These projects use a variety of fuzzy methods including clustering, simple set aggregation and type 2 inferencing to achieve their aims. The ongoing research projects reflect an interest in using type 2 fuzzy sets for dealing with vagueness and linguistic knowledge which is commonly found in medical areas where perceptions rather than measurements are the norm.

Artificial Intelligence↗
PubMed2002

Advances in molecular labeling, high throughput imaging and machine intelligence portend powerful functional cellular biochemistry tools.

Cellular behavior is complex. Successfully understanding systems at ever-increasing complexity is fundamental to advances in modern science and unraveling the functional details of cellular behavior is no exception. We present a collection of prospectives to provide a glimpse of the techniques that will aid in collecting, managing and utilizing information on complex cellular processes via molecular imaging tools. These include: 1) visualizing intracellular protein activity with fluorescent markers, 2) high throughput (and automated) imaging of multilabeled cells in statistically significant numbers, and 3) machine intelligence to analyze subcellular image localization and pattern. Although not addressed here, the importance of combining cell-image-based information with detailed molecular structure and ligand-receptor binding models cannot be overlooked. Advanced molecular imaging techniques have the potential to impact cellular diagnostics for cancer screening, clinical correlations of tissue molecular patterns for cancer biology, and cellular molecular interactions for accelerating drug discovery. The goal of finally understanding all cellular components and behaviors will be achieved by advances in both instrumentation engineering (software and hardware) and molecular biochemistry.

Animals↗
PubMed2005

A fusion-based clinical decision support for disease diagnosis from endoscopic images.

This paper presents an intelligent decision support system designed on a decision fusion framework coupled with a priori knowledge base for abnormality detection from endoscopic images. Sub-decisions are made based on associated component feature sets derived from the endoscopic images and predefined algorithms, and subsequently fused to classify the patient state. Bayesian probability computations are employed to evaluate the accuracies of sub-decisions, which are utilized in estimating the probability of the fused decision. The overall detectability of abnormalities by using the proposed fusion approach is improved in terms of detection of true positive and true negative conditions when compared with corresponding results from individual methods.

Algorithms↗
PubMed2005

The oncological nurse assistant: a web-based intelligent oncological nurse advisor.

When a person gets a cancer diagnosis the need for medical guidance often appears. In Norway, one of the providers of medical guidelines is the Norwegian Cancer Association where oncological nurses assist people with a cancer diagnosis or their relatives. The nurses search through both national and internal guidebooks and web pages. The input to this process is mostly discharge letters. The whole process is time consuming. To serve more patients, PaSent, a web-based intelligent oncological nurse advisor, has been developed. Through using discharge letters as input to our neural network-based information retrieval system PaSent, we have been able to provide relevant medical information to the patient as well as to the health personnel themselves. The PaSent search method uses predefined knowledge about the context, paired with the vocabulary of the input document, to compute a relevance measure for a potential result document. The system has been validated by oncological nurses and medical doctors. In the reported experiments, the PaSent system is able to recommend literature, in the top section of the search result list, that our judges also found highly relevant.

Artificial Intelligence↗
PubMed1996

Introducing spatio-temporal reasoning into the inverse problem in electroencephalography.

Studying the Brain's Electrical and Magnetic Signals (BEMS) requires the contribution of many area of research that include anatomy, neurophysiology and electromagnetic theory. NEUROLAB is a framework dedicated to the study of brain disorders. Upon completion, it should provide users with an intelligent computational environment that incorporates qualitative and quantitative models of the brain and head, and a model for representing and reasoning about time and space. Spatio-temporal knowledge of a given problem is represented as a constraint network where to each node of the network are attached temporal and spatial variables that must satisfy the constraints defined by the arch labels connecting the nodes. In this paper, we show how temporal reasoning can be combined with spatial descriptions to produce different scenarios of possible seizure spread. These scenarios can provide a priori information for the inverse problem the role of which is to localise the sources of the observed BEMS.

Artificial Intelligence↗
PubMed2004

Intelligent modelling of bioprocesses: a comparison of structured and unstructured approaches.

This contribution moves in the direction of answering some general questions about the most effective and useful ways of modelling bioprocesses. We investigate the characteristics of models that are good at extrapolating. We trained three fully predictive models with different representational structures (differential equations, differential equations with inheritance of rates and a network of reactions) on Saccharopolyspora erythraea shake flask fermentation data using genetic programming. The models were then tested on unseen data outside the range of the training data and the resulting performances were compared. It was found that constrained models with mathematical forms analogous to internal mass balancing and stoichiometric relations were superior to flexible unconstrained models, even though no a priori knowledge of this fermentation was used.

Artificial Intelligence↗
PubMed2005

Software agents in molecular computational biology.

Progress made in applying agent systems to molecular computational biology is reviewed and strategies by which to exploit agent technology to greater advantage are investigated. Communities of software agents could play an important role in helping genome scientists design reagents for future research. The advent of genome sequencing in cattle and swine increases the complexity of data analysis required to conduct research in livestock genomics. Databases are always expanding and semantic differences among data are common. Agent platforms have been developed to deal with generic issues such as agent communication, life cycle management and advertisement of services (white and yellow pages). This frees computational biologists from the drudgery of having to re-invent the wheel on these common chores, giving them more time to focus on biology and bioinformatics. Agent platforms that comply with the Foundation for Intelligent Physical Agents (FIPA) standards are able to interoperate. In other words, agents developed on different platforms can communicate and cooperate with one another if domain-specific higher-level communication protocol details are agreed upon between different agent developers. Many software agent platforms are peer-to-peer, which means that even if some of the agents and data repositories are temporarily unavailable, a subset of the goals of the system can still be met. Past use of software agents in bioinformatics indicates that an agent approach should prove fruitful. Examination of current problems in bioinformatics indicates that existing agent platforms should be adaptable to novel situations.

Algorithms↗
PubMed2005

An intelligent adaptive control scheme for postsurgical blood pressure regulation.

This paper presents an adaptive modeling and control scheme for drug delivery systems based on a generalized fuzzy neural network (G-FNN). The proposed G-FNN is a novel intelligent modeling tool, which can model unknown nonlinearities of complex drug delivery systems and adapt to changes and uncertainties in these systems online. It offers salient features, such as dynamic fuzzy neural topology, fast online learning ability and adaptability. System approximation formulated by the G-FNN is employed in the adaptive controller design for drug infusion in intensive care environment. In particular, this paper investigates automated regulation of mean arterial pressure (MAP) through intravenous infusion of sodium nitroprusside (SNP), which is one attractive application in automation of drug delivery. Simulation studies demonstrate the capability of the proposed approach in estimating the drug's effect and regulating blood pressure at a prescribed level.

Artificial Intelligence↗
PubMed1986

Computers and videodiscs in pathology education: ECLIPS as an example of one approach.

We have enumerated ways in which the evolving computer and videodisc technologies are being used in pathology education and discussed in some detail the particular use with which we are most familiar, text management. While it is probably premature to speculate as to how these technologies will ultimately affect pathology education, one recent trend--the convergence that seems to be developing between those working on expert consulting systems and those working primarily on educational applications--will probably influence this impact substantially. We believe that we are moving, from opposite directions, toward the same end result, namely, the use of machine intelligence to facilitate and augment human learning. We expect that, as the two groups come closer together, very powerful, interesting, and eminently useful educational tools will emerge. While this is occurring, we think that most would agree that one of the very urgent needs is to develop forums in which the academic and practice communities can interact with researchers and developers. With apologies to Clemenceau, computers are rapidly becoming too important to be left exclusively to computer scientists. Such forums would serve to give these communities a chance to learn what the new technologies have to offer and give developers a better idea of where these technologies can make the greatest contributions.

Artificial Intelligence↗
PubMed2000

CIM explorer--intelligent tool for exploring the ICD Romanian version.

The CIM Explorer software provides us with an intelligent interface for exploring the Romanian version of the International Classification of Diseases (in Romanian Clasificarea Internationala a Maladiilor-CIM). The ICD was transposed from its initial appearance as a printed document into a database. The classification can be accessed in two modes: "Navigation" and "Code" and queried in the "Key words" mode. In the last mode CIM Explorer program searches for the right content of the ICD records starting from naturally written expressions which it "understands". As a results it returns all the records containing the key words regardless their grammatical form. This program implements the specificity of the Romanian language where the words are made up from a root and a flexional termination.

Artificial Intelligence↗