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At least 1,639 records · Page 91Linked to original sources
PubMed2005

The Intelligent Dosing System: application for insulin therapy and diabetes management.

Diabetes mellitus is an increasing public health problem. Insulin is an essential tool in the management of hyperglycemia, but methods of dose adjustment are purely empirical. The Intelligent Dosing System (IDS, Dimensional Dosing Systems, Inc., Wexford, PA) is a software suite that incorporates patient-specific, dose-response data in a mathematical model and then calculates the new dose of the medication needed to achieve the next desired therapeutic goal. We discuss the application of the IDS in insulin management. The IDS concept and the initial modeling used to construct an insulin doser are reviewed first. Additional data are then provided on the use of the IDS for titrating insulin therapy in a clinical setting. Finally, recent modifications in the IDS software and future applications of this technology for insulin dosing and diabetes management are discussed.

Algorithms↗
PubMed1989

An AI-based communication system for motor and speech disabled persons: design methodology and prototype testing.

An intelligent communication device is developed to assist the nonverbal, motor disabled in the generation of written and spoken messages. The device is centered on a knowledge base of the grammatical rules and message elements. A "belief" reasoning scheme based on both the information from external sources and the embedded knowledge is used to optimize the process of message search. The search for the message elements is conceptualized as a path search in the language graph, and a special frame architecture is used to construct and to partition the graph. Bayesian "belief" reasoning from the Dempster-Shafer theory of evidence is augmented to cope with time-varying evidence. An "information fusion" strategy is also introduced to integrate various forms of external information. Experimental testing of the prototype system is discussed.

Artificial Intelligence↗
PubMed1995

Towards an intelligent system for the automatic assignment of domains in globular proteins.

The automatic identification of protein domains from coordinates is the first step in the classification of protein folds and hence is required for databases to guide structure prediction. Most algorithms encode a single concept based and sometimes do not yield assignments that are consistent with the generally accepted perception. Our development of an automatic approach to identify reliably domains from protein coordinates is described. The algorithm is benchmarked against a manual identification of the domains in 284 representative protein chains. The first step is the domain assignment by distance (DAD) algorithm that considers the density of inter-residue contacts represented in a contact matrix. The algorithm yields 85% agreement with the manual assignment. The paper then considers how the reliability of these assignments could be evaluated. Finally the use of structural comparisons using the STAMP algorithm to validate domain assignment is reported on a test case.

Algorithms↗
PubMed1998

Medical affective computing: medical informatics meets affective computing.

"The need to cope with a changing and partly unpredictable world makes it very likely that any intelligent system with multiple motives and limited powers will have emotions." [1] From advisory systems that understand emotional attitudes toward medical outcomes, to wearable computers that compensate for communication disability, to computer simulations of emotions and their disorders, the research agendas of medical informatics and affective computing--how and why to create computers that detect, convey, and even have emotions--increasingly overlap. Some psychiatric and neurological researchers state their theories in terms of actual or hypothetical computer programs. Adaptive intelligent systems will increasingly rely on emotions to compensate for their own conflicting goals and limited resources--emotional reactions about which psychiatrists and neurologists have special insights. DEP2 (Depression Emulation Program 2) is a computer simulation of adaptive depression--learning from explainable patterns of failure in autobiographical memory--that simulates many depressive behaviors. In the terminology of fault-tolerant computing, adaptive depression involves fault detection (triggered by failure), fault location (strategic retreat and failure diagnosis), and fault recovery (return to on-line operation). DEP2 relies on subsystems whose structures and behaviors are based on popular hypotheses about left and right brain hemispheric function during depression and emotion. DEP2 and its predecessors, DEP and DEPlanner, are relevant to psychiatric and neurological informatics, and to the design of adaptive autonomous robots and software agents.

Adaptation, Psychological↗
PubMed1993

Intelligent Medical Record--entry (IMR-E).

This paper describes an automated medical record designed to allow providers to enter patient data at the point of care. The system runs on PCs and Macintoshes and uses a graphical user interface and object-oriented programming to take advantage of current mouse and pen technologies. The provider acquires all relevant patient data by pointing and clicking at selections on input screens, many of which contain anatomical drawings to help the provider quickly and accurately describe patient findings. The system also generates a grammatically correct progress note using the problem-oriented structure. Furthermore, items identified in the assessment and plans portion of the program can be ported to expert systems for medical decisions assistance or to billing systems. The system allows the provider to obtain the necessary information on a focused patient visit in less than 5 min or to enter a complete history and physical.

Artificial Intelligence↗
PubMed1992

Knowledge-based decision support for patient monitoring in cardioanesthesia.

An approach to generating 'intelligent alarms' is presented that aggregates many information items, i.e. measured vital signs, recent medications, etc., into state variables that more directly reflect the patient's physiological state. Based on these state variables the described decision support system AES-2 also provides therapy recommendations. The assessment of the state variables and the generation of therapeutic advice follow a knowledge-based approach. Aspects of uncertainty, e.g. a gradual transition between 'normal' and 'below normal', are considered applying a fuzzy set approach. Special emphasis is laid on the ergonomic design of the user interface, which is based on color graphics and finger touch input on the screen. Certain simulation techniques considerably support the design process of AES-2 as is demonstrated with a typical example from cardioanesthesia.

Anesthesia↗
PubMed2002

Intelligent brace system for the treatment of scoliosis.

Measurement of the biomechanical effectiveness of a brace for the treatment of scoliosis has been hampered by the lack of compressive information about wear characteristics. Orthotists and orthopaedic surgeons believe that the effectiveness of bracing is correlated with the strap tensions. If the strap tensions can be maintained at the optimal level while patients wear their braces, a better treatment outcome may be obtained. However, strap tensions vary significantly during different activities. An intelligent brace system has been developed to control the strap tension so that the optimal prescribed level is maintained at all time. This system consists of an innovative strap tension transducer, a microcomputer unit and a motorized unit. The strap transducer has been developed with an accuracy +/- 1.0N in the range of 0 to 100N. An instrumented Boston brace was built to test the concept. When the strap tension was below 80% of the prescribed level for a 15 minutes interval, the microcomputer unit signaled the motor to tighten the strap. While the strap tension level was above 120% of the prescribed level for a 15 minutes interval, the motor reversed the direction. Laboratory testing results showed that the strap tension could be maintained at the optimal prescribed level.

Artificial Intelligence↗
PubMed2000

Intelligent analysis of clinical time series: an application in the diabetes mellitus domain.

This paper describes the application of a method for the intelligent analysis of clinical time series in the diabetes mellitus domain. Such a method is based on temporal abstractions and relies on the following steps: (i) 'pre-processing' of raw data through the application of suitable filtering techniques: (ii) 'extraction' from the pre-processed data of a set of abstract episodes (temporal abstractions); and (iii) 'post-processing' of temporal abstractions; the post-processing phase results in a new set of features that embeds high level information on the patient dynamics. The derived features set is used to obtain new knowledge through the application of machine learning algorithms. The paper describes in detail the application of this methodology and presents some results obtained on simulated data and on a data-set of four diabetic patients monitored for > 1 year.

Artificial Intelligence↗
PubMed2004

Intelligent automated control of life support systems using proportional representations.

Effective automatic control of Advanced Life Support Systems (ALSS) is a crucial component of space exploration. An ALSS is a coupled dynamical system which can be extremely sensitive and difficult to predict. As a result, such systems can be difficult to control using deliberative and deterministic methods. We investigate the performance of two machine learning algorithms, a genetic algorithm (GA) and a stochastic hill-climber (SH), on the problem of learning how to control an ALSS, and compare the impact of two different types of problem representations on the performance of both algorithms. We perform experiments on three ALSS optimization problems using five strategies with multiple variations of a proportional representation for a total of 120 experiments. Results indicate that although a proportional representation can effectively boost GA performance, it does not necessarily have the same effect on other algorithms such as SH. Results also support previous conclusions that multivector control strategies are an effective method for control of coupled dynamical systems.

Algorithms↗
PubMed2007

Intelligence by mechanics.

Research on the biomechanics of animal and human locomotion provides insight into basic principles of locomotion and respective implications for construction and control. Nearly elastic operation of the leg is necessary to reproduce the basic dynamics in walking and running. Elastic leg operation can be modelled with a spring-mass model. This model can be used as a template with respect to both gaits in the construction and control of legged machines. With respect to the segmented leg, the humanoid arrangement saves energy and ensures structural stability. With the quasi-elastic operation the leg inherits the property of self-stability, i.e. the ability to stabilize a system in the presence of disturbances without sensing the disturbance or its direct effects. Self-stability can be conserved in the presence of musculature with its crucial damping property. To ensure secure foothold visco-elastic suspended muscles serve as shock absorbers. Experiments with technically implemented leg models, which explore some of these principles, are promising.

Artificial Intelligence↗
PubMed1998

Virtual reality in psychotherapy: the MYTHSEEKER software.

This chapter outlines the use in psychotherapy and medical diagnosis of an intelligent software system that helps clients to explore Personal Myth within virtual reality environments. Patented MYTHSEEKER software will allow clients to work with mythic analogues of lifeshapes and aspirations. This can help to focus therapy directions, find ways to participate with the person's world, and allow a kind of personal expression not previously possible. The software phases of assessment, facilitation, and enaction are described by which the client is assisted to explore systems of mythology or spirituality (called Depth Systems) that are traditional, ancient or newly-arising. The client builds a Personal Depth System representing Personal Myth, based on experiencing other Depth Systems, which can itself be experienced in the virtual environment. This paper outlines our methodology and technology to realize these operations. Space limitations prevent further description in the present chapter of MYTHSEEKER software technology or psychotherapy scenarios of involvement.

Artificial Intelligence↗
PubMed2005

A design of multiagent-based framework for volume image construction and analysis.

This paper describes a design of a multiagent-based system that can be used to manage the acquisition and analysis of ultrasonograph images. The major concept is to design a management framework consisting of multiple intelligent agents to direct the ultrasonograph image acquisition and analysis operations carried out using a high-speed bit-parallel architecture efficiently as well as to allow for the construction of 3D images from 2D ones. Volume image operations need reactivity, autonomy, and intelligence of software. Therefore, agents can play an important role in enhancing the overall operation of medical image analysis. The system suggests a set of image analysis operations including smoothing, noise removal, and enhancing techniques. These operations will be implemented using parallel processing architectures while the management framework will consist of different agent types such as: simple reflex agents, agents that keep track of the world, goal-based agents, and utility-based agents. These agents interact with each other and exchange data among themselves in order to achieve a comprehensive speed in performing the volume image construction operations. Guided with the fact that the agent consists of program and architecture, the system deploys parallel processing architectures to implement the image analysis operations. The system is considered a step towards a complete multiagent-based framework for medical image acquisition and analysis.

Algorithms↗
PubMed2002

Consistency and reproducibility of attribute extraction by different machine learning systems.

Machine learning systems as tools for intelligent data analysis are used for extracting attributes relevant for prediction of defined outcomes. The aim of the paper was to compare two machine learning (ML) systems and propose method for intra- and inter-testing of consistency and reproducibility in attributes extracted from real dataset. See5 and FMLS extracted relevant attributes from real medical dataset. Comparison of results of both systems shows that: accuracy and sensitivity are nearly the same for both FMLS and See5 when FMLS was forced by attributes extracted by See5, and little bit lower when FMLS used its own extracted attributes; specificity is nearly the same for both FMLS and See5 when FMLS was forced by attributes extracted by See5, and much more higher when FMLS used its own extracted attributes; both of ML systems show intra-testing consistency and does not show any inter-testing consistency.

Artificial Intelligence↗
PubMed1992

A specialized framework for medical diagnostic knowledge-based systems.

For a knowledge-based system (KBS) to exhibit an intelligent behavior, it must be endowed with knowledge enabling it to represent the expert's strategies. The elicitation task is inherently difficult for strategic knowledge, because strategy is often tacit, and, even when it has been made explicit, it is not an easy task to describe it in a form which may be directly translated and implemented into a program. This paper describes a Specialized Framework for Medical Diagnostic Knowledge-Based Systems that can help an expert in the process of building KBSs in a medical domain. The framework is based on an epistemological model of diagnostic reasoning which has proven to be helpful in describing the diagnostic process in terms of the tasks that it is composed of. It allows a straightforward modeling of diagnostic reasoning at the knowledge level by the domain expert, thus helping to convey domain-dependent strategies into the target KBS.

Artificial Intelligence↗
PubMed2005

The comparison of different feed forward neural network architectures for ECG signal diagnosis.

The electrocardiograms (ECGs) record the electrical activity of the heart and are used to diagnose many heart disorders. This paper proposes a two-stage feed forward neural network for ECG signal classification. The research is aimed at the design of an intelligent ECG diagnosis tool that can recognise heart abnormalities while reducing the complexity, cost, and response time of the system. A number of neural network architectures are designed and compared for their ability to classify six different heart conditions. Two network architectures based on one stage and two stage feed forward neural networks are chosen for this investigation. The training and testing ECG signals are obtained from MIT-BIH database. The network inputs are comprised of 12 ECG features and 13 compressed components of each heart beat signal. The performance of the different modules as well as the efficiency of the whole system is presented. Among different architectures, a proposed multi-stage network named NET_BST possesses the highest recognition rate of around 93%. Therefore, this network proves to be a suitable candidate in ECG signal diagnosis systems.

Algorithms↗
PubMed2001

Computerized radiographic mass detection--part II: Decision support by featured database visualization and modular neural networks.

Based on the enhanced segmentation of suspicious mass areas, further development of computer-assisted mass detection may be decomposed into three distinctive machine learning tasks: 1) construction of the featured knowledge database; 2) mapping of the classified and/or unclassified data points in the database; and 3) development of an intelligent user interface. A decision support system may then be constructed as a complementary machine observer that should enhance the radiologists performance in mass detection. We adopt a mathematical feature extraction procedure to construct the featured knowledge database from all the suspicious mass sites localized by the enhanced segmentation. The optimal mapping of the data points is then obtained by learning the generalized normal mixtures and decision boundaries, where a is developed to carry out both soft and hard clustering. A visual explanation of the decision making is further invented as a decision support, based on an interactive visualization hierarchy through the probabilistic principal component projections of the knowledge database and the localized optimal displays of the retrieved raw data. A prototype system is developed and pilot tested to demonstrate the applicability of this framework to mammographic mass detection.

Artificial Intelligence↗
PubMed2006

Fuzzy approach to the intelligent management of virtual spaces.

This paper presents research carried out toward the improvement of current virtual environments from an intelligent systems approach. A novel architecture to solve vague queries that allows users to find objects and scenes in virtual environments is described. As a base, a new virtual worlds representation model and an associated fuzzy querying approach are used. The new representation model adds a semantic level to the usual models, providing more suitable environments for the interaction with users. The query solver is able to work with queries expressing the vagueness inherent to human conceptualization of visual perception (for example, tall tree, a park with many tall trees, or a park bench near approximately five tall trees). The system has been developed and evaluated with user experiments, where comparison with navigation and keyword-based query approaches have been realized. The results of this study show that the proposed architecture is more powerful and intuitive for finding the targets.

Algorithms↗
PubMed2002

[Child neurology and multimedia technology].

Methods of computer technology (intelligent technology, IT), such as multimedia and virtual reality, are utilized more and more in all medical fields including child neurology. Advances in the digitalization of individual medical data and multi-media technology have enabled patients to be able to obtain their own medical data by small media and to receive medical treatment at any hospitals even if they are located in distance place. Changes from a doctor oriented to patients oriented medicine is anticipated. It is necessary to store medical data from birth to adulthood and to accumulate epidemiological data of rare diseases such as metabolic diseases or degenerative diseases especially in child neurology, which highly require tele medicine and telecare at home. Moreover, IT may improve in the QOL of patients with neurological diseases and of their families. Cooperation of medicine and engineering is therefore necessary. Results of our experiments on telemedicine, telecare and virtual reality are described.

Artificial Intelligence↗