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PubMed2000

Biomechanical study of gait using an intelligent brace.

We have developed a new long leg brace fitted with a computer-controlled knee joint that allows the user to go up and down a slope and stairs. Using this new brace, we analyzed gait under various conditions in ten normal men in terms of dynamic electromyography. Walking up a slope with normal step produced discharges in the leg muscles other than the gastrocnemius lateral head before and after the heel-strike on the ground, which indicated the absorption of the impact of landing. Going down a slope with controlled step resulted in lower activities of the tibialis anterior and the gastrocnemius lateral head than doing so with non-locked step or locked step. In subjects walking up stairs with tandem gait, the large discharges of the gastrocnemius lateral head that were observed in the late stance phase with normal step disappeared when the brace was applied. These results indicated that the muscle activity levels decreased during controlled walking with this new brace, and that the new brace can compensate for reduced muscle strength of the lower leg during walking. Kinematic analysis of walking revealed no significant difference between normal walking and controlled walking under any walking conditions.

Adult↗
PubMed1996

A virtual reality interface to an intelligent dental care system.

The design and fabrication of teeth restorations in dentistry rely increasingly on CAD/CAM techniques. We present an approach for interactive design of the occlusal surface of teeth based on simulation of jaw articulation and computer-aided diagnosis of occlusal disorders. To bridge the cognitive gap between the dentist and the computer system, we propose a virtual reality user interface, which applies the metaphors of tools and techniques known in dentistry. This makes the restoration design more intuitive for dentists. The system uses Virtual Reality Modeling Language (VRML) and HTML standards to generate a treatment report and exchange data in an electronic form. The simulation of jaw articulation requires fast calculation of multi-point contacts and detection of collisions between surfaces of teeth and restorations. We have developed a distance maps technique which exhibits realtime performance for objects with complex geometry and is suitable for other virtual reality systems dealing with complex contacts. The characteristics of contacts between teeth acquired during lower jaw motion are compactly represented as accumulated distance maps. These maps are then used for automatic removal of interferences between the restorations and the opponent teeth, and provide the dentist with information for further manual adjustments of the occlusal surfaces.

Artificial Intelligence↗
PubMed2002

Empirical evaluation of a hybrid intelligent monitoring system using different measures of effectiveness.

The validation of a software product is a fundamental part of its development, and focuses on an analysis of whether the software correctly resolves the problems it was designed to tackle. Traditional approaches to validation are based on a comparison of results with what is called a gold standard. Nevertheless, in certain domains, it is not always easy or even possible to establish such a standard. This is the case of intelligent systems that endeavour to simulate or emulate a model of expert behaviour. This article describes the validation of the intelligent system computer-aided foetal evaluator (CAFE), developed for intelligent monitoring of the antenatal condition based on data from the non-stress test (NST), and how this validation was accomplished through a methodology designed to resolve the problem of the validation of intelligent systems. System performance was compared to that of three obstetricians using 3450 min of cardiotocographic (CTG) records corresponding to 53 different patients. From these records different parameters were extracted and interpreted, and thus, the validation was carried out on a parameter-by-parameter basis using measurement techniques such as percentage agreement, the Kappa statistic or cluster analysis. Results showed that the system's agreement with the experts is, in general, similar to agreement between the experts themselves which, in turn, permits our system to be considered at least as skillful as our experts. Throughout our article, the results obtained are commented on with a view to demonstrating how the utilisation of different measures of the level of agreement existing between system and experts can assist not only in assessing the aptness of a system, but also in highlighting its weaknesses. This kind of assessment means that the system can be fine-tuned repeatedly to the point where the expected results are obtained.

Artificial Intelligence↗
PubMed1990

Computer-aided intelligence: application of an expert system to brachial plexus injuries.

When confronted with a patient with a brachial plexus injury, how often as neurosurgeons do we consult an atlas to confirm the anatomy of the brachial plexus and then attempt to establish the location of the lesion? Similar difficulties are encountered with lumbar and sacral plexus lesions. In a project organized to assist the neurosurgeon in this time-consuming task, a computer program that can rapidly determine the site of a lesion in a brachial, lumbar, or sacral plexus injury was created. Using known anatomical pathways (37 clinically relevant upper and 20 lower extremity muscle innervations), and relying solely upon the neurological motor examination, rapid computer-assisted diagnosis is possible. When more than one final common pathway lesion occurs (for example, multiple root avulsions of the brachial plexus), possible lesion sites can be obtained. An interactive dialogue between the user and the program helps to determine the location of the lesion. The program can be run on any IBM-compatible personal computer and is presented as an instrument that provides assistance in cases of complex peripheral nerve injuries, when expert consultants are unavailable. In addition, it can be used as an aid to learning and as a review of basic neuroanatomy.

Artificial Intelligence↗
PubMed2005

Security architecture for health grid using ambient intelligence.

OBJECTIVES: To propose a novel approach of incorporating ambient intelligence in the health grid security architecture. METHODS: Security concerns are severely impeding the grid community effort in spreading its wings in health applications. In this paper, we have proposed a high level approach to incorporate ambient intelligence for health grid security architecture and have argued that this will significantly improve the current state of the grid security paradigm with an enhanced user-friendly environment. We believe that the time is right to shift the onus of traditional security mechanisms onto the new technologies. The incorporation of ambient intelligence in the security architecture of a grid will not only render a security paradigm robust but also provide an attractive vision for the future of computing by bringing the two worlds together. RESULTS: In this article we propose an evolutionary approach of utilizing smart devices for grid security architecture. We argue that such an infrastructure will impart unique features to the existing grid security paradigms by offering fortified and relentless monitoring. This new security architecture will be comprehensive in nature but will not be cumbersome for the users due to its typical characteristics of not prying into their lives and adapting to their needs. CONCLUSIONS: We have identified a new paradigm of the security architecture for a health grid that will not only render a security mechanism robust but will also provide the high levels of user-friendliness. As our approach is a first contribution to this problem, a number of other issues for future research remain open. However, the prospects are fascinating.

Access to Information↗
PubMed2006

Feature extraction for systolic heart murmur classification.

Heart murmurs are often the first signs of pathological changes of the heart valves, and they are usually found during auscultation in the primary health care. Distinguishing a pathological murmur from a physiological murmur is however difficult, why an "intelligent stethoscope" with decision support abilities would be of great value. Phonocardiographic signals were acquired from 36 patients with aortic valve stenosis, mitral insufficiency or physiological murmurs, and the data were analyzed with the aim to find a suitable feature subset for automatic classification of heart murmurs. Techniques such as Shannon energy, wavelets, fractal dimensions and recurrence quantification analysis were used to extract 207 features. 157 of these features have not previously been used in heart murmur classification. A multi-domain subset consisting of 14, both old and new, features was derived using Pudil's sequential floating forward selection (SFFS) method. This subset was compared with several single domain feature sets. Using neural network classification, the selected multi-domain subset gave the best results; 86% correct classifications compared to 68% for the first runner-up. In conclusion, the derived feature set was superior to the comparative sets, and seems rather robust to noisy data.

Aged↗
PubMed2001

An intelligent data acquisition system for simultaneous screening of microsomal stability and metabolite profiling by liquid chromatography/mass spectrometry.

This paper describes the development of a mass spectrometer-based, intelligent, programmable, sample-selection data acquisition system with two unique features. One is that the system allows automatic determination of the mass to charge ratio (m/z) of an unknown compound and the utilization of the molecular ion information to perform selective ion monitoring (SIM) experiments for quantitation. The other is its decision-making capability to select intelligently different samples and perform different experiments during data acquisition. These features were demonstrated by the application of the system to simultaneous screening for the microsomal stability and metabolite profiling of adatanserin. In this application, the data acquisition system continuously calculated the peak areas of adatanserin from SIM analyses of a batch of microsomal incubates stopped at various time points. Once the peak area of adatanserin had dropped to an arbitrarily predefined 60% of the initial value, the system made a decision to perform metabolite profiling of the sample. This decision initiated a series of automated operations, such as selecting a sample for re-analysis, changing the data acquisition time and liquid chromatographic gradient and switching the SIM mode to the data-dependent product ion scanning mode. The completed analysis of the batch of samples provided information both on the microsomal stability and on the metabolic profile of adatanserin. This simultaneous approach to investigating microsomal stability and metabolite profiling significantly increases the throughput for drug discovery support.

Algorithms↗
PubMed2001

Redundancy reduction revisited.

Soon after Shannon defined the concept of redundancy it was suggested that it gave insight into mechanisms of sensory processing, perception, intelligence and inference. Can we now judge whether there is anything in this idea, and can we see where it should direct our thinking? This paper argues that the original hypothesis was wrong in over-emphasizing the role of compressive coding and economy in neuron numbers, but right in drawing attention to the importance of redundancy. Furthermore there is a clear direction in which it now points, namely to the overwhelming importance of probabilities and statistics in neuroscience. The brain has to decide upon actions in a competitive, chance-driven world, and to do this well it must know about and exploit the non-random probabilities and interdependences of objects and events signalled by sensory messages. These are particularly relevant for Bayesian calculations of the optimum course of action. Instead of thinking of neural representations as transformations of stimulus energies, we should regard them as approximate estimates of the probable truths of hypotheses about the current environment, for these are the quantities required by a probabilistic brain working on Bayesian principles.

Artificial Intelligence↗
PubMed2004

A knowledge-based time-oriented active database approach for intelligent abstraction, querying and continuous monitoring of clinical data.

Query and interpretation of time-oriented medical data involves two subtasks: Temporal-reasoning--intelligent analysis of time-oriented data, and temporal-maintenance--effective storage, query, and retrieval of these data. Integration of these tasks into one system, known as temporal-mediator, has been proven to be beneficial to biomedical applications such as monitoring, therapy, quality assessment, visualization and exploration of time-oriented data. One potential problem in existing temporal-mediation approaches is lack of sufficient responsiveness when querying or continuously monitoring the database for complex abstract concepts that are derived from the raw data, especially regarding a large patient group. We propose a new approach: the knowledge-based time-oriented active database, a temporal extension of the active-database concept, and a merger of temporal reasoning and temporal maintenance within a persistent database framework. The approach preserves the efficiency of databases in handling data storage and retrieval, while enabling specification and performance of complex temporal reasoning using an incremental-computation approach. We implemented our approach within the Momentum system. Initial experiments are encouraging; an evaluation is underway

Algorithms↗
PubMed2000

[Modeling asthma evolution by a multi-state model].

BACKGROUND: There are many scores for the evaluation of asthma. However, most do not take into account the evolutionary aspects of this illness. We propose a model for the clinical course of asthma by a homogeneous Markov model process based on data provided by the A.R.I.A. (Association de Recherche en Intelligence Artificielle dans le cadre de l'asthme et des maladies respiratoires). METHODS: The criterion used is the activity of the illness during the month before consultation. The activity is divided into three levels: light (state 1), mild (state 2) and severe (state 3). The model allows the evaluation of the strength of transition between states. RESULTS: We found that strong intensities were implicated towards state 2 (lambda(12) and lambda(32)), less towards state 1 (lambda(21) and lambda(31)), and minimum towards state 3 (lambda(23)). This results in an equilibrium distribution essentially divided between state 1 and 2 (44.6% and 51.0% respectively) with a small proportion in state 3 (4.4%). CONCLUSIONS: In the future, the increasing amount of available data should permit the introduction of covariables, the distinction of subgroups and the implementation of clinical studies. The interest of this model falls within the domain of the quantification of the illness as well as the representation allowed thereof, while offering a formal framework for the clinical notion of time and evolution.

Artificial Intelligence↗
PubMed1999

Self-learning fuzzy control with temporal knowledge for atracurium-induced neuromuscular block during surgery.

Self-learning fuzzy logic control has the important property of accommodating uncertain, nonlinear, and time-varying process characteristics. This intelligent control scheme starts with no fuzzy control rules and learns how to control each process presented to it in real time without the need for detailed process modeling. In this study we utilize temporal knowledge of generated rules to improve control performance. A suitable medical application to investigate this control strategy is atracurium-induced neuromuscular block of patients in the operating theater where the patient response exhibits high nonlinearity and individual patient dose requirements may vary fivefold during an operating procedure. We developed a computer control system utilizing Relaxograph (Datex) measurements to assess the clinical performance of a self-learning fuzzy controller in this application. Using a T1 setpoint of 10% of baseline in 10 patients undergoing general surgery, we found a mean T1 error of 0.28% (SD = 0.39%) while accommodating a 0.25 to 0.38 mg/kg/h range in the mean atracurium infusion rate. This result compares favorably with more complex and computationally intensive model-based control strategies for atracurium infusion.

Algorithms↗
PubMed2002

An intelligent biological information management system.

MOTIVATION: As biomedical researchers are amassing a plethora of information in a variety of forms resulting from the advancements in biomedical research, there is a critical need for innovative information management and knowledge discovery tools to sift through these vast volumes of heterogeneous data and analysis tools. In this paper we present a general model for an information management system that is adaptable and scalable, followed by a detailed design and implementation of one component of the model. The prototype, called BioSifter, was applied to problems in the bioinformatics area. RESULTS: BioSifter was tested using 500 documents obtained from PubMed database on two biological problems related to genetic polymorphism and extracorporal shockwave lithotripsy. The results indicate that BioSifter is a powerful tool for biological researchers to automatically retrieve relevant text documents from biological literature based on their interest profile. The results also indicate that the first stage of information management process, i.e. data to information transformation, significantly reduces the size of the information space. The filtered data obtained through BioSifter is relevant as well as much smaller in dimension compared to all the retrieved data. This would in turn significantly reduce the complexity associated with the next level transformation, i.e. information to knowledge.

Abstracting and Indexing↗
PubMed1995

Use of data abstraction methods to simplify monitoring.

I describe the Temporal Control System (TCS), a programming system designed for building intelligent temporal monitoring programs. The ICU data set provided as part of the 1994 AAAI Spring Symposium challenge is used to conduct several experiments. Empirical results from the ICU data set validate the scalable design of the TCS. The remaining experiments examine the computational problem of generating interval values from sample points through persistent assumptions. Using abstractions in combination with persistence assumptions makes the design of higher-level clinical reasoning programs simpler. Abstraction can be used to suppress clinically unimportant details, allowing an expert system to focus on the key information provided by clinical monitors. The TCS provides the framework for the implementation as well as a method of calculating the 'cost' of different approaches. To prevent the use of outdated information, it is often useful to limit the time span of a persistent interval. I show that such limitations can be very costly computationally and then show how the application of symbolic abstraction can help. Further performance improvements from switching from continuous to discrete step persistence are shown. These performance enhancing techniques have general applicability.

Abstracting and Indexing↗
PubMed2003

Personal relationships with an intelligent interactive telephone health behavior advisor system: a multimethod study using surveys and ethnographic interviews.

The burgeoning of consumer health informatics and virtual health care can help people improve their health. However, little is known about individuals' reactions to such systems. We conducted an evaluation of the telephone-linked care (TLC) system, a computer-based telecommunications system, that functions as an at home monitor, educator, and counselor for patients with chronic health conditions. Our multimethod assessment of individuals' reactions to using TLC included both quantitative and qualitative methods. Ethnographic in-depth open-ended interviews indicated more subtle and surprising reactions to TLC than the overall positive responses from surveys: individuals formed personal relationships with this technology. This relationship formation suggests that TLC designers may have been successful in their attempts to emulate a conversation with a human being. Our study adds to evidence that technology can serve as a projective device for peoples' values and psychological issues. Both designers and users project values and goals onto computer-based technologies and take on different identities through it. Different groups of users, therefore, may see the same technology differently. People also form relationships with technologies, as they did with TLC. These findings, as well as implications for system design and health outcomes, need to be explored in additional studies.

Adult↗
PubMed2006

Time dependent neural network models for detecting changes of state in complex processes: applications in earth sciences and astronomy.

A computational intelligence approach is used to explore the problem of detecting internal state changes in time dependent processes; described by heterogeneous, multivariate time series with imprecise data and missing values. Such processes are approximated by collections of time dependent non-linear autoregressive models represented by a special kind of neuro-fuzzy neural network. Grid and high throughput computing model mining procedures based on neuro-fuzzy networks and genetic algorithms, generate: (i) collections of models composed of sets of time lag terms from the time series, and (ii) prediction functions represented by neuro-fuzzy networks. The composition of the models and their prediction capabilities, allows the identification of changes in the internal structure of the process. These changes are associated with the alternation of steady and transient states, zones with abnormal behavior, instability, and other situations. This approach is general, and its sensitivity for detecting subtle changes of state is revealed by simulation experiments. Its potential in the study of complex processes in earth sciences and astrophysics is illustrated with applications using paleoclimate and solar data.

Artificial Intelligence↗
PubMed2006

Rule generation for protein secondary structure prediction with support vector machines and decision tree.

Support vector machines (SVMs) have shown strong generalization ability in a number of application areas, including protein structure prediction. However, the poor comprehensibility hinders the success of the SVM for protein structure prediction. The explanation of how a decision made is important for accepting the machine learning technology, especially for applications such as bioinformatics. The reasonable interpretation is not only useful to guide the "wet experiments," but also the extracted rules are helpful to integrate computational intelligence with symbolic AI systems for advanced deduction. On the other hand, a decision tree has good comprehensibility. In this paper, a novel approach to rule generation for protein secondary structure prediction by integrating merits of both the SVM and decision tree is presented. This approach combines the SVM with decision tree into a new algorithm called SVM_ DT, which proceeds in three steps. This algorithm first trains an SVM. Then, a new training set is generated through careful selection from the output of the SVM. Finally, the obtained training set is used to train a decision tree learning system and to extract the corresponding rule sets. The results of the experiments of protein secondary structure prediction on RS126 data set show that the comprehensibility of SVM_DT is much better than that of the SVM. Moreover, the generalization ability of SVM_DT is better than that of C4.5 decision trees and is similar to that of the SVM. Hence, SVM_DT can be used not only for prediction, but also for guiding biological experiments.

Algorithms↗
PubMedDate not supplied

Ovarian cancer diagnosis by hippocampus and neocortex-inspired learning memory structures.

Early detection and accurate staging of ovarian cancer are the keys to improving survival rate. However, at present there is no single diagnosis modality that is sufficiently sensitive. DNA microarray analysis is an emerging technique that has potential for ameliorating the hardship in early detection and staging of ovarian disease. However, microarray data is ultra-huge and difficult to analyze. Hence, computational intelligence methods are often utilized to assist in the diagnosis and analysis process. Fuzzy Neural Networks (FNN) are more suitable for this task as FNN provides not only the accuracy, but also the interpretability of its reasoning process. Hippocampus-inspired Complementary Learning FNN (CLFNN) is able to rapidly derive fuzzy sets and formulate fuzzy rules. CLFNN uses positive and negative learning, and hence it reduces the effect of the curse of dimensionality and is capable of modeling the dynamics of the problem space with relatively good classification performance. One of its successors, a hybrid of complementary hippocampal learning and associative neocortical learning called Pseudo Associative Complementary Learning (PACL), is a structure that seeks to functionally model the memory consolidation process. Both PACL and CLFNN have human-like reasoning that allows physicians to examine their computation using familiar terms. They can construct intuitive fuzzy rules autonomously to justify their reasoning, which is important to generate trust among the users. Hence, CLFNN and PACL are applied as a diagnostic decision support system in ovarian cancer diagnosis. The experimental results are encouraging.

Algorithms↗
PubMed2001

Intelligent system and risk of different diseases in the general population.

This is a new approach to define the risk of the general population for a specific disease. We use Bayesian theory to define the risk of the population. Analyzing the major risk factors of a disease, how they affect the incidence and/or prevalence of the disease, and the statistics of each risk factor in the population, along with the power of the Bayes theorem helps us define the risk of that population for that disease. To find out what the risk of the general population for a disease is, prospective epidemiological studies in the population shown are needed. These studies usually lead to the identification of the major risk factors for a disease and their impacts which are quite costly and requires a long time to get to the results. Also a large personnel is needed to perform effectively in the study. The result of the risk of the general population for a first heart attack using our software is in agreement with the result of the Framingham heart study. Large studies like Framingham is not available for other diseases to enable us to evaluate the accuracy of our software precisely. To overcome this shortage we have sought medical experts' evaluation of the predicted risk of the general population for other disease by this software, which needs to be completed.

Artificial Intelligence↗