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PubMed2003

Intelligent optimal control with dynamic neural networks.

The application of neural networks technology to dynamic system control has been constrained by the non-dynamic nature of popular network architectures. Many of difficulties are-large network sizes (i.e. curse of dimensionality), long training times, etc. These problems can be overcome with dynamic neural networks (DNN). In this study, intelligent optimal control problem is considered as a nonlinear optimization with dynamic equality constraints, and DNN as a control trajectory priming system. The resulting algorithm operates as an auto-trainer for DNN (a self-learning structure) and generates optimal feed-forward control trajectories in a significantly smaller number of iterations. In this way, optimal control trajectories are encapsulated and generalized by DNN. The time varying optimal feedback gains are also generated along the trajectory as byproducts. Speeding up trajectory calculations opens up avenues for real-time intelligent optimal control with virtual global feedback. We used direct-descent-curvature algorithm with some modifications (we called modified-descend-controller-MDC algorithm) for the optimal control computations. The algorithm has generated numerically very robust solutions with respect to conjugate points. The adjoint theory has been used in the training of DNN which is considered as a quasi-linear dynamic system. The updating of weights (identification of parameters) are based on Broyden-Fletcher-Goldfarb-Shanno BFGS method. Simulation results are given for an intelligent optimal control system controlling a difficult nonlinear second-order system using fully connected three-neuron DNN.

Artificial Intelligence↗
PubMedDate not supplied

[Analytic design and clinical application of an intelligent control system for pharmacotherapy with insulin--1].

To determine whether insulin dosage recommendations provided by a computer system are as effective as those given by human experts, we developed an intelligent control system and prospectively studied its use in 42 type-1 diabetics attending a diabetes education center. Control algorithms were based on blood glucose self-monitoring and included parameter estimations to determine glucose metabolism. The algorithms were implemented in a vest pocket-sized system. Over a period of 32 days, 21 patients used the computer to determine the necessary dose of insulin, while a second group of 21 patients followed the recommendations of the diabetes specialists. Baseline HbA1 levels (9.8 +/- 1.6 vs 9.9 +/- 1.6%) were identical in the two groups. The mean serum glucose over the last two weeks of the study was lower in the computer group (151.3 +/- 25.2 vs 165.7 +/- 36.0 mg/dl; p < 0.01) although the rates of hypoglycaemic episodes were equal (1.7 vs 2.3%). Metabolic control, measured by the day-to-day standard deviation of the serum glucose (46.8 +/- 14.4 vs 50.4 +/- 16.2 mg/dl; p < 0.01), was more stable in the computer group. We conclude that metabolic control and safety were comparable in the two groups, and suggest that such an intelligent control system may be of benefit for use at home, when the help of doctors or diabetes educators is not available.

Artificial Intelligence↗
PubMed1996

Weaning from mechanical ventilation.

Weaning from mechanical ventilation is a period of transition from total ventilatory support to spontaneous breathing. It represents a relevant clinical problem because as many as 25% of intubated and mechanically-ventilated critically ill patients will need a progressive withdrawal from artificial ventilatory support. From a clinical standpoint, it is very important to recognize as soon as possible when a patient is ready to be weaned. Accordingly, a daily routine follow-up should be performed in every patient in order to verify whether patients meet clinical criteria to be disconnected from the ventilator. Several physiological indices have been used to predict the outcome of weaning trials. However, an adequate clinical tolerance to spontaneous breathing during a 2 h T-piece trial is very useful to predict a successful extubation. A number of physiopathological mechanisms explain why some patients fail the weaning trials; particularly important from a clinical point of view are those related to respiratory pump failure and cardiovascular instability, which are usually accompanied by an abnormal gas exchange. Different ventilatory techniques can be used to wean these patients from mechanical ventilation. Up to now, the most efficient techniques seem to be pressure support ventilation and once daily trials of T-piece interspersed with conventional volume assist-control ventilation. Finally, knowledge-based system applied to modern microprocessor mechanical ventilators can help in the process of weaning by automatically reducing the ventilatory assistance and indicating the optimal time to perform extubation.

Artificial Intelligence↗
PubMed1997

Computational predictive programs (expert systems) in toxicology.

The increasing number of pollutants in the environment raises the problem of the toxicological risk evaluation of these chemicals. Several so called expert systems (ES) have been claimed to be able to predict toxicity of certain chemical structures. Different approaches are currently used for these ES, based on explicit rules derived from the knowledge of human experts that compiled lists of toxic moieties for instance in the case of programs called HazardExpert and DEREK or relying on statistical approaches, as in the CASE and TOPKAT programs. Here we describe and compare these and other intelligent computer programs because of their utility in obtaining at least a first rough indication of the potential toxic activity of chemicals.

Animals↗
PubMed2003

Prediction of drug release profiles using an intelligent learning system: an experimental study in transdermal iontophoresis.

This paper investigates the use of a neural-network-based intelligent learning system for the prediction of drug release profiles. An experimental study in transdermal iontophoresis (TI) is employed to evaluate the applicability of a particular neural network (NN) model, i.e. the Gaussian mixture model (GMM), in modeling and predicting drug release profiles. A number of tests are systematically designed using the face-centered central composite design (CCD) approach to examine the effects of various process variables simultaneously during the iontophoresis process. The GMM is then applied to model and predict the drug release profiles based on the data samples collected from the experiments. The GMM results are compared with those from multiple regression models. In addition, the bootstrap method is used to assess the reliability of the network predictions by estimating confidence intervals associated with the results. The results demonstrate that the combination of the face-centered CCD and GMM can be employed as a useful intelligent tool for the prediction of time-series profiles in pharmaceutical and biomedical experiments.

Administration, Cutaneous↗
PubMed2004

The smart house for older persons and persons with physical disabilities: structure, technology arrangements, and perspectives.

Smart houses are considered a good alternative for the independent life of older persons and persons with disabilities. Numerous intelligent devices, embedded into the home environment, can provide the resident with both movement assistance and 24-h health monitoring. Modern home-installed systems tend to be not only physically versatile in functionality but also emotionally human-friendly, i.e., they may be able to perform their functions without disturbing the user and without causing him/her any pain, inconvenience, or movement restriction, instead possibly providing him/her with comfort and pleasure. Through an extensive survey, this paper analyzes the building blocks of smart houses, with particular attention paid to the health monitoring subsystem as an important component, by addressing the basic requirements of various sensors implemented from both research and clinical perspectives. The paper will then discuss some important issues of the future development of an intelligent residential space with a human-friendly health monitoring functional system.

Activities of Daily Living↗
PubMed1996

[Analytic design and clinical application of an intelligent control system for pharmacotherapy with insulin--2].

To determine whether insulin dosage recommendations provided by a computer system are as effective as those given by human experts, we developed an intelligent control system and prospectively studied its use in 42 type-1 diabetics attending a diabetes education center. Control algorithms were based on blood glucose self-monitoring and included parameter estimations to determine glucose metabolism. The algorithms were implemented in a vest pocket-sized system. Over a period of 32 days, 21 patients used the computer to determine the necessary dose of insulin, while a second group of 21 patients followed the recommendations of the diabetes specialists. Baseline HbA1 levels (9.8 +/- 1.6 vs 9.9 +/- 1.6%) were identical in the two groups. The mean serum glucose over the last two weeks of the study was lower in the computer group (151.3 +/- 25.2 vs 165.7 +/- 36.0 mg/dl; p < 0.01) although the rates of hypoglycaemic episodes were equal (1.7 vs 2.3%). Metabolic control, measured by the day-to-day standard deviation of the serum glucose (46.8 +/- 14.4 vs 50.4 +/- 16.2 mg/dl; p < 0.01), was more stable in the computer group. We conclude that metabolic control and safety were comparable in the two groups, and suggest that such an intelligent control system may be of benefit for use at home, when the help of doctors or diabetes educators is not available.

Adult↗
PubMed1995

Discovering the intelligence in molecular biology.

The Third International Conference on Intelligent Systems in Molecular Biology was truly an outstanding event. Computational methods in molecular biology have reached a new level of maturity and utility, resulting in many high-impact applications. The success of this meeting bodes well for the rapid and continuing development of computational methods, intelligent systems and information-based approaches for the biosciences. The basic technology, originally most often applied to 'feasibility' problems, is now dealing effectively with the most difficult real-world problems. Significant progress has been made in understanding protein-structure information, structural classification, and how functional information and the relevant features of active-site geometry can be gleaned from structures by automated computational approaches. The value and limits of homology-based methods, and the ability to classify proteins by structure in the absence of homology, have reached a new level of sophistication. New methods for covariation analysis in the folding of large structures such as RNAs have shown remarkably good results, indicating the long-term potential to understand very complicated molecules and multimolecular complexes using computational means. Novel methods, such as HMMs, context-free grammars and the uses of mutual information theory, have taken center stage as highly valuable tools in our quest to represent and characterize biological information. A focus on creative uses of intelligent systems technologies and the trend toward biological application will undoubtedly continue and grow at the 1996 ISMB meeting in St Louis.

Algorithms↗
PubMed2005

Multicriteria meta-heuristics for AGV dispatching control based on computational intelligence.

In many manufacturing environments, automated guided vehicles are used to move the processed materials between various pickup and delivery points. The assignment of vehicles to unit loads is a complex problem that is often solved in real-time with simple dispatching rules. This paper proposes an automated guided vehicles dispatching approach based on computational intelligence. We adopt a fuzzy multicriteria decision strategy to simultaneously take into account multiple aspects in every dispatching decision. Since the typical short-term view of dispatching rules is one of the main limitations of such real-time assignment heuristics, we also incorporate in the multicriteria algorithm a specific heuristic rule that takes into account the empty-vehicle travel on a longer time-horizon. Moreover, we also adopt a genetic algorithm to tune the weights associated to each decision criteria in the global decision algorithm. The proposed approach is validated by means of a comparison with other dispatching rules, and with other recently proposed multicriteria dispatching strategies also based on computational Intelligence. The analysis of the results obtained by the proposed dispatching approach in both nominal and perturbed operating conditions (congestions, faults) confirms its effectiveness.

Algorithms↗
PubMed2002

Design aspects for novel, telemedical unattended diagnosis and therapy control systems for sleep disorders.

Medical research confirmed the relevance of diagnosis and therapy of sleep-related breathing disorders, revealing prevalence and patho-physiological mechanisms [1-10]. Reliable sleep assessment currently demands examinations in the sleep-lab, which is very technical and time-consuming. Thus, expanding the conventional procedure to meet capacity demands does not seem realistic regarding cost aspects. As a solution, the authors present telemetric system concepts for the unattended assessment of a patient's sleep profile and cardio-respiratory parameters. Taking initial experience with home polysomnography into consideration, the presented research work focuses on easily applicable sensors, the corresponding sleep profile assessment rules, wireless data recording, intelligent signal processing algorithms and telemedical information logistics to include sleep-labs in the diagnosis and treatment process as competence centers.

Algorithms↗
PubMed1999

Meta-manager: a requirements analysis.

The digital imaging network-picture-archiving and communications system (DIN-PACS) will be implemented in ten sites within the Great Plains Regional Medical Command (GPRMC). This network of PACS and teleradiology technology over a shared T1 network has opened the door for round the clock radiology coverage of all sites. However, the concept of a virtual radiology environment poses new issues for military medicine. A new workflow management system must be developed. This workflow management system will allow us to efficiently resolve these issues including quality of care, availability, severe capitation, and quality of the workforce. The design process of this management system must employ existing technology, operate over various telecommunication networks and protocols, be independent of platform operating systems, be flexible and scaleable, and involve the end user at the outset in the design process for which it is developed. Using the unified modeling language (UML), the specifications for this new business management system were created in concert between the University of Arizona and the GPRMC. These specifications detail a management system operating through a common object request brokered architecture (CORBA) environment. In this presentation, we characterize the Meta-Manager management system including aspects of intelligence, interfacility routing, fail-safe operations, and expected improvements in patient care and efficiency.

Arizona↗
PubMed2001

The outlook for cosmic company.

The last 100 million years or so has seen a continued increase in encephalization for several terrestrial species. Intelligence has survival value. Developments in astrobiology suggest that what was once considered enormously improbable, namely life, is now suspected of being ubiquitous. It may be that the evolution of intelligence is unlikely, but in a finite, breathtakingly large universe (10(22) stars) small probability likely does not matter. Even if nature is indifferent to producing intelligence, SETI might still succeed. Biological intelligence may be rare, but it has the potential for creating engineered synthetic intelligence, capable of rapid and directed self-evolution. The galaxy could be rife with such long-lived, communicating devices, even if intelligent protoplasm is both rare and fleeting. SETI is looking for narrow-band, microwave signals that are not produced naturally. Ultimately, SETI is more exploration than experimentation.

Artificial Intelligence↗
PubMed1996

Distributed medical intelligence. A systems approach for developing and integrative health care information distribution infrastructure.

Recent trends in healthcare informatics and telemedicine indicate that systems are being developed with a primary focus on technology and business, not on the process of medicine itself. Distributed Medical Intelligence promotes the development of an integrative medical communication system which addresses the process of providing expert medical knowledge to the point of need.

Artificial Intelligence↗
PubMed1996

Artificial neural networks: current status in cardiovascular medicine.

Artificial neural networks are a form of artificial computer intelligence that have been the subject of renewed research interest in the last 10 years. Although they have been used extensively for problems in engineering, they have only recently been applied to medical problems, particularly in the fields of radiology, urology, laboratory medicine and cardiology. An artificial neural network is a distributed network of computing elements that is modeled after a biologic neural system and may be implemented as a computer software program. It is capable of identifying relations in input data that are not easily apparent with current common analytic techniques. The functioning artificial neural network's knowledge is built on learning and experience from previous input data. On the basis of this prior knowledge, the artificial neural network can predict relations found in newly presented data sets. In cardiology, artificial neural networks have been successfully applied to problems in the diagnosis and treatment of coronary artery disease and myocardial infarction, in electrocardiographic interpretation and detection of arrhythmias and in image analysis in cardiac radiography and sonography. This report focuses on the current status of artificial neural network technology in cardiovascular medical research.

Cardiology↗
PubMed2001

Database reusability in intelligent medical systems.

Reuse or reusability is not a specific, algorithmic, heuristic or only a simple set of guidelines. Database reuse means the use of an existing component--a database entity--in a new context, either elsewhere in the same system or in another system. According to different definitions, an intelligent system is a "power tool for thinking"; but on the other side it is only a kind of information system with built-in knowledge to support decisions made by human experts. Similar conclusions could be made for intelligent medical systems and introduce the database reusability in this environment with a purpose to increase the quality of an intelligent medical system. In the paper the problem of the database reusability will be presented more in detail, especially its integration in an intelligent medical system. Finally, the results of such integration and the benefits for the medicine will be discussed.

Artificial Intelligence↗
PubMed2005

An intelligent tutoring system that generates a natural language dialogue using dynamic multi-level planning.

OBJECTIVE: The objective of this research was to build an intelligent tutoring system capable of carrying on a natural language dialogue with a student who is solving a problem in physiology. Previous experiments have shown that students need practice in qualitative causal reasoning to internalize new knowledge and to apply it effectively and that they learn by putting their ideas into words. METHODS: Analysis of a corpus of 75 hour-long tutoring sessions carried on in keyboard-to-keyboard style by two professors of physiology at Rush Medical College tutoring first-year medical students provided the rules used in tutoring strategies and tactics, parsing, and text generation. The system presents the student with a perturbation to the blood pressure, asks for qualitative predictions of the changes produced in seven important cardiovascular variables, and then launches a dialogue to correct any errors and to probe for possible misconceptions. The natural language understanding component uses a cascade of finite-state machines. The generation is based on lexical functional grammar. RESULTS: Results of experiments with pretests and posttests have shown that using the system for an hour produces significant learning gains and also that even this brief use improves the student's ability to solve problems more then reading textual material on the topic. Student surveys tell us that students like the system and feel that they learn from it. The system is now in regular use in the first-year physiology course at Rush Medical College. CONCLUSION: We conclude that the CIRCSIM-Tutor system demonstrates that intelligent tutoring systems can implement effective natural language dialogue with current language technology.

Artificial Intelligence↗
PubMed2001

Generation of an intelligent medical system, using a real database, to diagnose bacterial infection in hospitalized patients.

The initial diagnosis of bacterial infections in the absence of laboratory microbiological data requires physicians to use clinical algorithms based on symptoms, patient history and infection site. Optimization of such algorithms would be achieved by including as many variables associated with bacterial infection as possible. Demographic data are easily available and frequently used to sub-group human populations. A prospective investigation was, therefore, undertaken to examine the influence of demographic variables on bacterial infection rates, using data obtained from 173 patients presenting to Albert Einstein Medical Center. Data was randomly selected from 149 of these patients and used to generate fuzzy rules to model an intelligent medical system. To test the accuracy of this system at determining bacterial infection, based solely on demographic data, the program was given the remaining 24 patients' information. All 18 patients with either streptococcal, staphylococcal or Escherichia coli infections were correctly diagnosed. Non-E.coli GNR were misdiagnosed as E. coli infections in two patients resulting in an overall prediction rate for the 24 patients of 91.66%. This study suggests that the direct correlation of demographic variables with a predisposition to bacterial infection allow the design of an intelligent medical system, which shows great future potential as a diagnostic tool for all physicians.

Adult↗