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Results for “generative artificial intelligence”

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PubMed1995

The concept of "template" assisted electronic medical record.

A new design for an electronic medical record with flexible template generation was developed. The "template" in this paper refers to one of the tools that calls a user's attention to entering the data items for each problem or to ordering tests when they are due. The template shows the patient's previous data and guides physicians to record the consistent description. Two kinds of medical data dictionaries are prepared. Data representations of signs, symptoms, laboratory tests, and other examinations are defined in the check items dictionary. The problem dictionary contains the possible patient problems with relation to check items and other kinds of related subjects. This system provides Problem Oriented Medical Record (POMR) and the graphical presentation of various patient data. The system was designed to establish a constant and integrated medical record.

Artificial Intelligence↗

PubMed2004

Machine learning based pattern recognition applied to microarray data.

MOTIVATION: Microarrays have allowed the expression level of thousands of genes or proteins to be measured simultaneously. Data sets generated by these arrays consist of a small number of observations (e.g., 20-100 samples) on a very large number of variables (e.g., 10,000 genes or proteins). The observations in these data sets often have other attributes associated with them such as a class label denoting the pathology of the subject. Finding the genes or proteins that are correlated to these attributes is often a difficult task since most of the variables do not contain information about the pathology and as such can mask the identity of the relevant features. We describe a genetic algorithm (GA) that employs both supervised and unsupervised learning to mine gene expression and proteomic data. The pattern recognition GA selects features that increase clustering, while simultaneously searching for features that optimize the separation of the classes in a plot of the two or three largest principal components of the data. Because the largest principal components capture the bulk of the variance in the data, the features chosen by the GA contain information primarily about differences between classes in the data set. The principal component analysis routine embedded in the fitness function of the GA acts as an information filter, significantly reducing the size of the search space since it restricts the search to feature sets whose principal component plots show clustering on the basis of class. The algorithm integrates aspects of artificial intelligence and evolutionary computations to yield a smart one pass procedure for feature selection, clustering, classification, and prediction.

Algorithms↗

PubMedDate not supplied

Auditory prostheses research with multiple channel intracochlear stimulation in man.

Although single-channel electrode arrays implanted in the scala tympani of deaf patients are useful as an aid to lip reading and for distinguishing some environmental sounds, they do not transmit intelligible speech. However, multichannel electrode arrays, which take advantage of the cochlea's tonotopic organization, may be capable of generating the complex patterns of neural activity necessary for speech discrimination. In this study, multichannel electrodes were implanted in the cochleas of four volunteers, with access to the connecting wires made through the skin via a percutaneous connector. The major portion of the data presented is from two of these subjects: one has been bilaterally deaf since birth and the other has been unilaterally deaf for 15 years. Preliminary results of experiments with two more recently implanted subjects are described as well as experiments with a fifth volunteer who was implanted with five electrodes by House in 1969. Data on pitch and loudness discrimination as well as the effects of stimulation parameters on threshold, impedance, and electrode interaction are presented. Place pitch and periodicity pitch were observed in all five volunteers. The results of pitch-matching experiments with the unilaterally deaf volunteer were consistent with tonotopic maps of the cochlea, and experiments indicated that a pitch continuum may be achieved by combining place and periodicity pitch modulation. Preliminary experiments in tune recognition with one subject demonstrate his ability to recognize simple melodies based on periodicity pitch cues. These results, coupled with the finding that subjective sensations remain stable over the long-term, support the feasibility of providing artificial hearing with a multichannel cochlear stimulation system.

Acoustic Stimulation↗

PubMed1989

Generation of surgical pathology report using a 5,000-word speech recognizer.

Pressures to decrease both turnaround time and operating costs simultaneously have placed conflicting demands on traditional forms of medical transcription. The new technology of voice recognition extends the promise of enabling the pathologist or other medical professional to dictate a correct report and have it printed and/or transmitted to a database immediately. The usefulness of voice recognition systems depends on several factors, including ease of use, reliability, speed, and accuracy. These in turn depend on the general underlying design of the systems and inclusion in the systems of a specific knowledge base appropriate for each application. Development of a good knowledge base requires close collaboration between a domain expert and a knowledge engineer with expertise in voice recognition. The authors have recently completed a knowledge base for surgical pathology using the Kurzweil VoiceReport 5,000-word system.

Artificial Intelligence↗

PubMed1986

Visual training and correction of articulation disorders by use of dynamic palatography: serial observation in a case of cleft palate.

The dynamic palatograph is an electrical apparatus that generates a visual display of constantly changing palatolingual contact as a function of time, using an artificial palatal plate with affixed electrodes. This paper describes a technique of speech therapy incorporating dynamic palatography for a cleft palate patient. The patient, a 6-year-old Japanese girl with a repaired unilateral cleft lip and palate, had been judged to demonstrate articulation disorders involving contact of the tongue with the hard palate or alveolus following surgical improvement of velopharyngeal function. Prior to therapy the tongue tended to contact the hard palate more posteriorly than normal. After therapy with the dynamic palatograph, palatolingual contact was normal in comparison with average speakers. Our findings suggest that the facility of constant visual indication of tongue posture to the clinician and patient during corrective speech therapy using dynamic palatography may expedite results with cleft palate patients in the speech clinic when implemented in a carefully structured treatment plan.

Articulation Disorders↗

PubMed2007

Insect walking is based on a decentralized architecture revealing a simple and robust controller.

Control of walking in rugged terrain requires one to incorporate different issues, such as the mechanical properties of legs and muscles, the neuronal control structures for the single leg, the mechanics and neuronal control structures for the coordination between legs, as well as central decisions that are based on external information and on internal states. Walking in predictable environments and fast running, to a large degree, rely on muscle mechanics. Conversely, slow walking in unpredictable terrain, e.g. climbing in rugged structures, has to rely on neuronal systems that monitor and intelligently react to specific properties of the environment. An arthropod model system that shows the latter abilities is the stick insect, based on which this review will be focused. An insect, when moving its six legs, has to control 18 joints, three per leg, and therefore has to control 18 degrees of freedom (d.f.). As the body position in space is determined by 6 d.f. only, there are 12 d.f. open to be selected. Therefore, a fundamental problem is as to how these extra d.f. are controlled. Based mainly on behavioural experiments and simulation studies, but also including neurophysiological results, the following control structures have been revealed. Legs act as basically independent systems. The quasi-rhythmic movement of the individual leg can be described to result from a structure that exploits mechanical coupling of the legs via the ground and the body. Furthermore, neuronally mediated influences act locally between neighbouring legs, leading to the emergence of insect-type gaits. The underlying controller can be described as a free gait controller. Cooperation of the legs being in stance mode is assumed to be based on mechanical coupling plus local positive feedback controllers. These controllers, acting on individual leg joints, transform a passive displacement of a joint into an active movement, generating synergistic assistance reflexes in all mechanically coupled joints. This architecture is summarized in the form of the artificial neural network, Walknet, that is heavily dependent on sensory feedback at the proprioceptive level. Exteroceptive feedback is exploited for global decisions, such as the walking direction and velocity.

Animals↗

PubMed2005

Discriminative analysis of brain function at resting-state for attention-deficit/hyperactivity disorder.

In this work, a discriminative model of attention deficit hyperactivity disorder (ADHD) is presented on the basis of multivariate pattern classification and functional magnetic resonance imaging (fMRI). This model consists of two parts, a classifier and an intuitive representation of discriminative pattern of brain function between patients and normal controls. Regional homogeneity (ReHo), a measure of brain function at resting-state, is used here as a feature of classification. Fisher discriminative analysis (FDA) is performed on the features of training samples and a linear classifier is generated. Our initial experimental results show a successful classification rate of 85%, using leave-one-out cross validation. The classifier is also compared with linear support vector machine (SVM) and Batch Perceptron. Our classifier outperforms the alternatives significantly. Fisher brain, the optimal projective-direction vector in FDA, is used to represent the discriminative pattern. Some abnormal brain regions identified by Fisher brain, like prefrontal cortex and anterior cingulate cortex, are well consistent with that reported in neuroimaging studies on ADHD. Moreover, some less reported but highly discriminative regions are also identified. We conclude that the discriminative model has potential ability to improve current diagnosis and treatment evaluation of ADHD.

Algorithms↗

PubMed2004

A diagnostic expert system for structured reports, quality assessment, and training of residents in sonography.

BACKGROUND: The quality of medical reports on diagnostic procedures has a considerable impact on the quality of medical care. Handwritten or otherwise unstructured reports tend to be incomplete, whereas structured questionnaires are of limited flexibility and not considered case-adequate. Thus, medical reports of this kind may promote an incomplete and misleading documentation and, therefore, be problematic with respect to their reliability. METHODS: SonoConsult (SC), an expert system for structured and case-adequate documentation of sonographic findings with an additional diagnostic component, was evaluated with respect to user acceptance and suitability for enhancing the quality of reports and supporting sonographic beginners. The expectations and the attitudes of the users toward the program were evaluated by anonymous questionnaires. The documentation of findings and the diagnostic conclusions in 103 free text reports made by experienced examiners were evaluated by subjecting their information to a subsequent input into SC. Free text reports were checked for information that was asked by SC but not mentioned in the reports. In a series of 150 cases, the system diagnoses were blinded during input of findings into SC-questionnaires and the examiners' diagnostic conclusions were compared with the uncovered SC-diagnoses with respect to forgotten diagnoses. RESULTS: The structured and data-driven acquisition of information by the program was well accepted by the users. However, only a medium interest in the system-delivered diagnoses was noted. The program-generated reports were characterized by a more detailed description of the findings and a higher number of diagnoses in comparison to the unstructured reports before introduction of SC as the only documentation system. When unaware of the system diagnoses, information was entered into the questionnaires, and SC generated some diagnoses which were not mentioned by the examiners in their conclusions. The possibility to inspect the system diagnoses led to an enhancement of the number of diagnoses the examiners mentioned in their conclusions. By contrast, the examiners meant that the influence of the program on their conclusions was minimal or dispensable. Beginners in sonography acknowledged that the program led them to perform a complete examination in an adequate sequence. CONCLUSION: An expert system for the data-driven, case-adequate information acquisition of abdominal ultrasound examinations may enhance the quality of the reports and, potentially, of the examinations at the same time. In addition, it may help beginners to learn a structured problem- and finding-adequate examination sequence.

Abdomen↗

PubMed2004

SELDI-TOF-based serum proteomic pattern diagnostics for early detection of cancer.

Proteomics is more than just generating lists of proteins that increase or decrease in expression as a cause or consequence of pathology. The goal should be to characterize the information flow through the intercellular protein circuitry that communicates with the extracellular microenvironment and then ultimately to the serum/plasma macroenvironment. The nature of this information can be a cause, or a consequence, of disease and toxicity-based processes. Serum proteomic pattern diagnostics is a new type of proteomic platform in which patterns of proteomic signatures from high dimensional mass spectrometry data are used as a diagnostic classifier. This approach has recently shown tremendous promise in the detection of early-stage cancers. The biomarkers found by SELDI-TOF-based pattern recognition analysis are mostly low molecular weight fragments produced at the specific tumor microenvironment.

Algorithms↗

PubMed2006

Combining multi-species genomic data for microRNA identification using a Naive Bayes classifier.

MOTIVATION: Most computational methodologies for microRNA gene prediction utilize techniques based on sequence conservation and/or structural similarity. In this study we describe a new technique, which is applicable across several species, for predicting miRNA genes. This technique is based on machine learning, using the Naive Bayes classifier. It automatically generates a model from the training data, which consists of sequence and structure information of known miRNAs from a variety of species. RESULTS: Our study shows that the application of machine learning techniques, along with the integration of data from multiple species is a useful and general approach for miRNA gene prediction. Based on our experiments, we believe that this new technique is applicable to an extensive range of eukaryotes' genomes. Specific structure and sequence features are first used to identify miRNAs followed by a comparative analysis to decrease the number of false positives (FPs). The resulting algorithm exhibits higher specificity and similar sensitivity compared to currently used algorithms that rely on conserved genomic regions to decrease the rate of FPs.

Algorithms↗

PubMed2002

Biological data integration: wrapping data and tools.

Nowadays scientific data is inevitably digital and stored in a wide variety of formats in heterogeneous systems. Scientists need to access an integrated view of remote or local heterogeneous data sources with advanced data accessing, analyzing, and visualization tools. Building a digital library for scientific data requires accessing and manipulating data extracted from flat files or databases, documents retrieved from the Web as well as data generated by software. We present an approach to wrapping web data sources, databases, flat files, or data generated by tools through a database view mechanism. Generally, a wrapper has two tasks: it first sends a query to the source to retrieve data and, second builds the expected output with respect to the virtual structure. Our wrappers are composed of a retrieval component based on an intermediate object view mechanism called search views mapping the source capabilities to attributes, and an eXtensible Markup Language (XML) engine, respectively, to perform these two tasks. The originality of the approach consists of: 1) a generic view mechanism to access seamlessly data sources with limited capabilities and 2) the ability to wrap data sources as well as the useful specific tools they may provide. Our approach has been developed and demonstrated as part of the multidatabase system supporting queries via uniform object protocol model (OPM) interfaces.

Algorithms↗

PubMed2005

On the comparison of FROC curves in mammography CAD systems.

We present a novel method for assessing the performance of computer-aided detection systems on unseen cases at a given sensitivity level. The sampling error introduced when training the system on a limited data set is captured as the uncertainty in determining the system threshold that would yield a certain predetermined sensitivity on unseen data sets. By estimating the distribution of system thresholds, we construct a confidence interval for the expected number of false positive markings per image at a given sensitivity. We present two alternative procedures for estimating the probability density functions needed for the construction of the confidence interval. The first is based on the common assumption of Poisson distributed number of false positive markings per image. This procedure also relies on the assumption of independence between false positives and sensitivity, an assumption that can be relaxed with the second procedure, which is nonparametric. The second procedure uses the bootstrap applied to the data generated in the leave-one-out construction of the FROC curve, and is a fast and robust way of obtaining the desired confidence interval. Standard FROC curve analysis does not account for the uncertainty in setting the system threshold, so this method should allow for a more fair comparison of different systems. The resulting confidence intervals are surprisingly wide. For our system a conventional FROC curve analysis yields 0.47 false positive markings per image at 90% sensitivity. The 90% confidence interval for the number of false positive markings per image is (0.28, 1.02) with the parametric procedure and (0.27, 1.04) with the nonparametric bootstrap. Due to its computational simplicity and its allowing more fair comparisons between systems, we propose this method as a complement to the traditionally presented FROC curves.

Algorithms↗

PubMedDate not supplied

X-ray spectral reconstruction from attenuation data using neural networks.

An artificial neural network using input data derived from attenuation measurements was trained to generate spectral profiles (relative number of photons versus energy). Once the relative spectral distribution is reconstructed, absolute spectra (number of photons per unit exposure spectral distribution is reconstructed, absolute spectra (number of photons per unit exposure versus energy) can be calculated. A neural network was trained on spectra generated mathematically using the Birch-Marshall model, combined with attenuation data, calculated from the spectra by numerical integration. Whereas attenuation data can be calculated in a straightforward manner from the x-ray spectra, the reverse is not true. Several neural networks were successfully taught to reconstruct the spectra, given the attenuation data. The networks were tested using kV/inherent filtration combinations that were not in the training set, and the performance of the reconstruction was excellent. Noise in the attenuation data was simulated to test the effects of noise propagation in the reconstruction. The effects of network architecture and data averaging on noise propagation were investigated. Experimentally determined spectral data complied by Fewell were also used to train a neural network, and the results of the reconstruction were also found to be excellent.

Artificial Intelligence↗

PubMed2003

The role of soft computing in intelligent machines.

An intelligent machine relies on computational intelligence in generating its intelligent behaviour. This requires a knowledge system in which representation and processing of knowledge are central functions. Approximation is a 'soft' concept, and the capability to approximate for the purposes of comparison, pattern recognition, reasoning, and decision making is a manifestation of intelligence. This paper examines the use of soft computing in intelligent machines. Soft computing is an important branch of computational intelligence, where fuzzy logic, probability theory, neural networks, and genetic algorithms are synergistically used to mimic the reasoning and decision making of a human. This paper explores several important characteristics and capabilities of machines that exhibit intelligent behaviour. Approaches that are useful in the development of an intelligent machine are introduced. The paper presents a general structure for an intelligent machine, giving particular emphasis to its primary components, such as sensors, actuators, controllers, and the communication backbone, and their interaction. The role of soft computing within the overall system is discussed. Common techniques and approaches that will be useful in the development of an intelligent machine are introduced, and the main steps in the development of an intelligent machine for practical use are given. An industrial machine, which employs the concepts of soft computing in its operation, is presented, and one aspect of intelligent tuning, which is incorporated into the machine, is illustrated.

Algorithms↗

PubMed2001

Disambiguating ambiguous biomedical terms in biomedical narrative text: an unsupervised method.

With the growing use of Natural Language Processing (NLP) techniques for information extraction and concept indexing in the biomedical domain, a method that quickly and efficiently assigns the correct sense of an ambiguous biomedical term in a given context is needed concurrently. The current status of word sense disambiguation (WSD) in the biomedical domain is that handcrafted rules are used based on contextual material. The disadvantages of this approach are (i) generating WSD rules manually is a time-consuming and tedious task, (ii) maintenance of rule sets becomes increasingly difficult over time, and (iii) handcrafted rules are often incomplete and perform poorly in new domains comprised of specialized vocabularies and different genres of text. This paper presents a two-phase unsupervised method to build a WSD classifier for an ambiguous biomedical term W. The first phase automatically creates a sense-tagged corpus for W, and the second phase derives a classifier for W using the derived sense-tagged corpus as a training set. A formative experiment was performed, which demonstrated that classifiers trained on the derived sense-tagged corpora achieved an overall accuracy of about 97%, with greater than 90% accuracy for each individual ambiguous term.

Algorithms↗

PubMed2004

Evaluating methods for classifying expression data.

An attractive application of expression technologies is to predict drug efficacy or safety using expression data of biomarkers. To evaluate the performance of various classification methods for building predictive models, we applied these methods on six expression datasets. These datasets were from studies using microarray technologies and had either two or more classes. From each of the original datasets, two subsets were generated to simulate two scenarios in biomarker applications. First, a 50-gene subset was used to simulate a candidate gene approach when it might not be practical to measure a large number of genes/biomarkers. Next, a 2000-gene subset was used to simulate a whole genome approach. We evaluated the relative performance of several classification methods by using leave-one-out cross-validation and bootstrap cross-validation. Although all methods perform well in both subsets for a relative easy dataset with two classes, differences in performance do exist among methods for other datasets. Overall, partial least squares discriminant analysis (PLS-DA) and support vector machines (SVM) outperform all other methods. We suggest a practical approach to take advantage of multiple methods in biomarker applications.

Algorithms↗

PubMed2005

Visual learning by imitation with motor representations.

We propose a general architecture for action (mimicking) and program (gesture) level visual imitation. Action-level imitation involves two modules. The viewpoint Transformation (VPT) performs a "rotation" to align the demonstrator's body to that of the learner. The Visuo-Motor Map (VMM) maps this visual information to motor data. For program-level (gesture) imitation, there is an additional module that allows the system to recognize and generate its own interpretation of observed gestures to produce similar gestures/goals at a later stage. Besides the holistic approach to the problem, our approach differs from traditional work in i) the use of motor information for gesture recognition; ii) usage of context (e.g., object affordances) to focus the attention of the recognition system and reduce ambiguities, and iii) use iconic image representations for the hand, as opposed to fitting kinematic models to the video sequence. This approach is motivated by the finding of visuomotor neurons in the F5 area of the macaque brain that suggest that gesture recognition/imitation is performed in motor terms (mirror) and rely on the use of object affordances (canonical) to handle ambiguous actions. Our results show that this approach can outperform more conventional (e.g., pure visual) methods.

Algorithms↗

PubMed1997

[Expert systems for medicine--functions and developments].

Medical knowledge doubles every five years. Electronic media are necessary to manage these volumes. "Multimedia" and "information highway" are buzz-words and also concern medicine. Medical informatics tries to build a bridge between medical research which generates knowledge and daily practice in hospitals, ambulatory care and public services. Knowledge is used in different representations for multiple purposes and functions. Expert systems are mostly designed to fulfil certain functions and to support routine clinical practice. This paper tries to point out some important functions and to explain the capabilities by means of two example systems which can be used in several medical units. The main problem in using expert systems is, besides the quality of knowledge, the availability of patient data. For this reason the existence of an electronic patient record is another area which will be described in this paper. Expert systems are specialised medical information systems and have to fulfil the same success criteria as all the other systems, e.g. the integration into the daily routine as a trusted tool.

Adult↗