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Results for “generative artificial intelligence”

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PubMed2001

Aero-acoustics of silicone rubber lip reeds for alternative voice production in laryngectomees.

To improve voice quality after laryngectomy, a small pneumatic sound source to be incorporated in a regular tracheoesophageal shunt valve was designed. This artificial voice source consists of a single floppy lip reed, which performs self-sustaining flutter-type oscillations driven by the expired pulmonary air that flows through the tracheoesophageal shunt valve along the outward-striking lip reed. In this in vitro study, aero-acoustic data and detailed high-speed digital image sequences of lip reed behavior are obtained for 10 lip configurations. The high-speed visualizations provide a more explicit understanding and reveal details of lip reed behavior, such as the onset of vibration, beating of the lip against the walls of its housing, and chaotic behavior at high volume flow. We discuss several aspects of lip reed behavior in general and implications for its application as an artificial voice source. For pressures above the sounding threshold, volume flow, fundamental frequency and sound pressure level generated by the floppy lip reed are almost linear functions of the driving force, static pressure difference across the lip. Observed irregularities in these relations are mainly caused by transitions from one type of beating behavior of the lip against the walls of its housing to another. This beating explains the wide range and the driving force dependence of fundamental frequency, and seems to have a strong effect on the spectral content. The thickness of the lip base is linearly related to the fundamental frequency of lip reed oscillation.

Humans↗

PubMed2005

Semantic enrichment for medical ontologies.

The Unified Medical Language System (UMLS) contains two separate but interconnected knowledge structures, the Semantic Network (upper level) and the Metathesaurus (lower level). In this paper, we have attempted to work out better how the use of such a two-level structure in the medical field has led to notable advances in terminologies and ontologies. However, most ontologies and terminologies do not have such a two-level structure. Therefore, we present a method, called semantic enrichment, which generates a two-level ontology from a given one-level terminology and an auxiliary two-level ontology. During semantic enrichment, concepts of the one-level terminology are assigned to semantic types, which are the building blocks of the upper level of the auxiliary two-level ontology. The result of this process is the desired new two-level ontology. We discuss semantic enrichment of two example terminologies and how we approach the implementation of semantic enrichment in the medical domain. This implementation performs a major part of the semantic enrichment process with the medical terminologies, with difficult cases left to a human expert.

Artificial Intelligence↗

PubMed2006

Automated validation of polymerase chain reaction amplicon melting curves.

The polymerase chain reaction (PCR) is a fundamental tool of molecular biology. Quantitative PCR is the gold-standard methodology for determination of DNA copy numbers, quantitating transcription, and numerous other applications. A major barrier to large-scale application of PCR for quantitative genomic analyses is the current requirement for manual validation of individual PCRs to ensure generation of a single product. This typically requires visual inspection either of gel electrophoreses or temperature dissociation ("melting") curves of individual PCRs--a time-consuming and costly process. Here we describe a robust computational solution to this fundamental problem. Using a training set of 10 080 reactions comprising multiple quantitative PCRs from each of 1728 unique human genomic amplicons, we developed a support vector machine classifier capable of discriminating single-product PCRs with better than 99% accuracy. This approach has broad utility, and eliminates a major bottleneck to widespread application of PCR for high-throughput genomic applications.

Algorithms↗

PubMed2005

A coarse-to-fine approach to prostate boundary segmentation in ultrasound images.

BACKGROUND: In this paper a novel method for prostate segmentation in transrectal ultrasound images is presented. METHODS: A segmentation procedure consisting of four main stages is proposed. In the first stage, a locally adaptive contrast enhancement method is used to generate a well-contrasted image. In the second stage, this enhanced image is thresholded to extract an area containing the prostate (or large portions of it). Morphological operators are then applied to obtain a point inside of this area. Afterwards, a Kalman estimator is employed to distinguish the boundary from irrelevant parts (usually caused by shadow) and generate a coarsely segmented version of the prostate. In the third stage, dilation and erosion operators are applied to extract outer and inner boundaries from the coarsely estimated version. Consequently, fuzzy membership functions describing regional and gray-level information are employed to selectively enhance the contrast within the prostate region. In the last stage, the prostate boundary is extracted using strong edges obtained from selectively enhanced image and information from the vicinity of the coarse estimation. RESULTS: A total average similarity of 98.76%(+/- 0.68) with gold standards was achieved. CONCLUSION: The proposed approach represents a robust and accurate approach to prostate segmentation.

Algorithms↗

PubMed1996

Computer assisted diabetes care: a 6-year retrospective.

Over the past 6 years we have designed a number of computer-based prototypes for the provision of therapeutic advice and the generation of glycaemic predictions in insulin-dependent (type 1) diabetic patients. In this paper we provide an overview of some of this work, and describe our experiences in trying to develop such methods for clinical use. We review, as an example, a model of the glucoregulatory system which has been developed for patient and medical staff education about type 1 diabetes mellitus, as well as possibly for therapeutic use. Using individualised parameter values the predictions of the model can be applied to generate 24-h simulations of patient blood glucose profiles. Previous preliminary retrospective validation work performed with this model has revealed a mean predictive accuracy for blood glucose simulations of approximately 2 mmol/l. Conceptual limitations of such modelling approaches are considered. We comment that such "mechanistic' models may lack the necessary sophistication and flexibility to represent the complexity of the human glucoregulatory system and the challenges it has to face. Although such methodologies may therefore not be suitable for safe and effective application in routine clinical practice, we conclude that the evolution of such a system for demonstration/educational purposes could have widespread clinical utility as an interactive teaching tool.

Artificial Intelligence↗

PubMed2006

Multi-modal image set registration and atlas formation.

In this paper, we present a Bayesian framework for both generating inter-subject large deformation transformations between two multi-modal image sets of the brain and for forming multi-class brain atlases. In this framework, the estimated transformations are generated using maximal information about the underlying neuroanatomy present in each of the different modalities. This modality independent registration framework is achieved by jointly estimating the posterior probabilities associated with the multi-modal image sets and the high-dimensional registration transformations mapping these posteriors. To maximally use the information present in all the modalities for registration, Kullback-Leibler divergence between the estimated posteriors is minimized. Registration results for image sets composed of multi-modal MR images of healthy adult human brains are presented. Atlas formation results are presented for a population of five infant human brains.

Algorithms↗

PubMed2004

Image enhancement and denoising by complex diffusion processes.

The linear and nonlinear scale spaces, generated by the inherently real-valued diffusion equation, are generalized to complex diffusion processes, by incorporating the free Schrödinger equation. A fundamental solution for the linear case of the complex diffusion equation is developed. Analysis of its behavior shows that the generalized diffusion process combines properties of both forward and inverse diffusion. We prove that the imaginary part is a smoothed second derivative, scaled by time, when the complex diffusion coefficient approaches the real axis. Based on this observation, we develop two examples of nonlinear complex processes, useful in image processing: a regularized shock filter for image enhancement and a ramp preserving denoising process.

Algorithms↗

PubMed2004

A knowledge based approach for automated signal generation in pharmacovigilance.

BACKGROUND: Pharmacovigilance experts detect new adverse drug reactions (ADR) by manually reviewing spontaneous reporting systems. Automated signal generation aims to focus the attention of experts on drug-adverse event associations which are disproportionally present in the database. Although adverse events are coded by means of controlled vocabularies such as the MedDRA dictionary, this semantic information is not taken into account for signal generation. OBJECTIVE: To improve the performance of current signal detection algorithms using knowledge based approach. METHOD: We developed a formal ontology of ADRs and built a data mining tool that uses description logic representations of MedDRA terms to group medically related case reports. RESULTS: This knowledge based approach increased the sensitivity of signal detection with no decrease in specificity. DISCUSSION: A knowledge based approach improved the performance of signal detection tools. However, the huge work-load involved in the knowledge engineering step limits the use of this approach for machine learning.

Adverse Drug Reaction Reporting Systems↗

PubMed1996

ABGEN: a knowledge-based automated approach for antibody structure modeling.

Immunoglobulin (Ig) amino acid sequences are highly conserved and often have sequence homology ranging from 70 to 95%. Antigen binding fragments (Fab), variable region fragments (Fv), and single chain Fv (scFv) of more than 50 myeloma proteins and monoclonal antibodies (mAb) have been crystallized and display a high degree of structural similarity. Based on this observation, several homology modeling approaches have been developed for the prediction of Fab and Fv structures prior to their experimental determination. We have extracted features from existing Ig sequences, 44 known Fab and Fv structures to create an automated AntiBody structure GENeration (ABGEN) algorithm for obtaining structural models of antibody fragments. ABGEN utilizes a homology based scaffolding technique, and includes the use of invariant and strictly conserved residues, structural motifs of known Fab, canonical features of hypervariable loops, torsional constraints for residue replacements and key inter-residue interactions. The validity of the ABGEN algorithm has been tested using a five-fold cross validation with the existing Fab structures. Molecular mechanics and dynamics methods have been implemented with ABGEN models to accurately predict two Fab structures of anti-sweetener antibodies prior to crystallographic determinations.

Algorithms↗

PubMed2006

Channel smoothing: efficient robust smoothing of low-level signal features.

In this paper, we present a new and efficient method to implement robust smoothing of low-level signal features: B-spline channel smoothing. This method consists of three steps: encoding of the signal features into channels, averaging of the channels, and decoding of the channels. We show that linear smoothing of channels is equivalent to robust smoothing of the signal features if we make use of quadratic B-splines to generate the channels. The linear decoding from B-spline channels allows the derivation of a robust error norm, which is very similar to Tukey's biweight error norm. We compare channel smoothing with three other robust smoothing techniques: nonlinear diffusion, bilateral filtering, and mean-shift filtering, both theoretically and on a 2D orientation-data smoothing task. Channel smoothing is found to be superior in four respects: It has a lower computational complexity, it is easy to implement, it chooses the global minimum error instead of the nearest local minimum, and it can also be used on nonlinear spaces, such as orientation space.

Algorithms↗

PubMed2005

Integrative analysis of multiple gene expression profiles with quality-adjusted effect size models.

BACKGROUND: With the explosion of microarray studies, an enormous amount of data is being produced. Systematic integration of gene expression data from different sources increases statistical power of detecting differentially expressed genes and allows assessment of heterogeneity. The challenge, however, is in designing and implementing efficient analytic methodologies for combination of data generated by different research groups. RESULTS: We extended traditional effect size models to combine information from different microarray datasets by incorporating a quality measure for each gene in each study into the effect size estimation. We illustrated our method by integrating two datasets generated using different Affymetrix oligonucleotide types. Our results indicate that the proposed quality-adjusted weighting strategy for modelling inter-study variation of gene expression profiles not only increases consistency and decreases heterogeneous results between these two datasets, but also identifies many more differentially expressed genes than methods proposed previously. CONCLUSION: Data integration and synthesis is becoming increasingly important. We live in a high-throughput era where technologies constantly change leaving behind a trail of data with different forms, shapes and sizes. Statistical and computational methodologies are therefore critical for extracting the most out of these related but not identical sources of data.

Algorithms↗

PubMed2006

Matching 2.5D face scans to 3D models.

The performance of face recognition systems that use two-dimensional images depends on factors such as lighting and subject's pose. We are developing a face recognition system that utilizes three-dimensional shape information to make the system more robust to arbitrary pose and lighting. For each subject, a 3D face model is constructed by integrating several 2.5D face scans which are captured from different views. 2.5D is a simplified 3D (x, y, z) surface representation that contains at most one depth value (z direction) for every point in the (x, y) plane. Two different modalities provided by the facial scan, namely, shape and texture, are utilized and integrated for face matching. The recognition engine consists of two components, surface matching and appearance-based matching. The surface matching component is based on a modified Iterative Closest Point (ICP) algorithm. The candidate list from the gallery used for appearance matching is dynamically generated based on the output of the surface matching component, which reduces the complexity of the appearance-based matching stage. Three-dimensional models in the gallery are used to synthesize new appearance samples with pose and illumination variations and the synthesized face images are used in discriminant subspace analysis. The weighted sum rule is applied to combine the scores given by the two matching components. Experimental results are given for matching a database of 200 3D face models with 598 2.5D independent test scans acquired under different pose and some lighting and expression changes. These results show the feasibility of the proposed matching scheme.

Algorithms↗

PubMed2006

A new convexity measure based on a probabilistic interpretation of images.

In this paper, we present a novel convexity measure for object shape analysis. The proposed method is based on the idea of generating pairs of points from a set and measuring the probability that a point dividing the corresponding line segments belongs to the same set. The measure is directly applicable to image functions representing shapes and also to gray-scale images which approximate image binarizations. The approach introduced gives rise to a variety of convexity measures which make it possible to obtain more information about the object shape. The proposed measure turns out to be easy to implement using the Fast Fourier Transform and we will consider this in detail. Finally, we illustrate the behavior of our measure in different situations and compare it to other similar ones.

Algorithms↗

PubMed1997

On the use of neural network techniques to analyse sleep EEG data. First communication: application of evolutionary and genetic algorithms to reduce the feature space and to develop classification rules.

To automate sleep stage scoring, the system sleep analysis system to challenge innovative artificial networks (SASCIA) has been developed and implemented. The aims of our investigation were twofold: In addition to automatic sleep stage scoring the hypothesis was tested that the information of only 1 EEG channel (C4-A2) should be sufficient to automatically generate sleep profiles which are comparable with profiles made by sleep experts on the basis of at least 3-channel EEG (C4-A2), EOG and EMG, as EOG and EMG are seen as epiphenomena during sleep and the full information about the sleep stage should--according to our hypothesis--be available in the EEG. The main components of the SASCIA sleep analysis system are designed to meet the requirements of flexible adaptation to the interindividual differences of the sleep EEG. The core of the SASCIA sleep analysis system consists of neural networks. Supervised learning was implemented and the experts' scorings were included into the learning set and test set. The feature selections out of a large number (118) are performed by genetic algorithms and the topologies of the networks are optimized by evolutionary algorithms. Different mathematical procedures were used to evaluate and optimize the efficiency of the system. The profiles generated by SASCIA are in reasonable agreement with the sleep stages scored by experts according to RKR. The development of the system is communicated in three parts: the first communication deals with the application of the neural network techniques using evolutionary and genetic algorithms and with the selection of feature space. The second communication shows the training of these evolutionary optimized network techniques with multiple subjects and the application of context rules, while the third communication shows an improvement in the robustness by the simultaneous application of 9 different networks obtained from 9 subject types which were used in combination with context rules.

Algorithms↗

PubMed1997

New computer-based tools for empiric antibiotic decision support.

Since 1995 we have been developing a decision-support model, called Q-ID, which uses a series of infectious disease knowledge bases to make recommendations for empirical treatment or to check the appropriateness of current antibiotic therapy. From disease manifestations and risk factors, a differential diagnosis for the patient is generated by a diagnostic medical expert system. The resulting probability of each: disease is multiplied by the expected benefit in improved mortality and morbidity from optimal antibiotic treatment of each disease. To generate empirical treatment recommendations, site-specific data on sensitivity to antibiotics of each organism is used as an estimate of the likelihood of achieving maximum benefit for each disease on the patient's differential. Combining this data with drug and patient specific factors, the model recommends the antibiotic(s) most likely to produce the optimal benefit in this patient with the least risk and expense. In this paper the model is described, excerpts from each of the knowledge bases are presented, and performance of the model in a real case is shown for illustration.

Aged↗

PubMed2000

Event discovery in medical time-series data.

Vast amounts of clinical information are generated daily on patients in the health care setting. Increasingly, this information is collected and stored for its potential utility in advancing health care. Knowledge-based systems, for example, might be able to apply rules to the collected data to determine whether a patient has a certain condition. Often, however, the underlying knowledge needed to write such rules is not well understood. How could these clinical data be useful then? Use of machine learning is one answer. We present a pipeline for discovering the knowledge needed for event detection in medical time-series data. We demonstrate how this process can be applied in the development of intelligent patient monitoring for the intensive care unit (ICU). Specifically, we develop a system for detecting Otrue alarmO situations in the ICU, where currently as many as 86% of bedside monitor alarms are false.

Artificial Intelligence↗

PubMed2004

Discovering significant and interpretable patterns from multifactorial DNA microarray data with poor replication.

MOTIVATION: Multivariate analyses are advantageous for the simultaneous testing of the separate and combined effects of many variables and of their interactions. In factorial designs with many factors and/or levels, however, sufficient replication is often prohibitively costly. Furthermore, complicated statements are often required for the biological interpretation of the higher-order interactions determined by standard statistical techniques like analysis of variance. RESULTS: Because we are usually interested in finding factor-specific effects or their interactions, we assumed that the observed expression profile of a gene is a manifestation of an underlying factor-specific generative pattern (FSGP) combined with noise. Thus, a genetic algorithm was created to find the nearest FSGP for each expression profile. We then measured the distance between each profile and the corresponding nearest FSGP. Permutation testing for the distance measures successfully identified those genes with statistically significant profiles, thus yielding straightforward biological interpretations. Association networks of genes, drugs, and cell lines were created as tripartite graphs, representing significant and interpretable relations, by using a microarray experiment of gastric-cancer cell lines with a factorial design and no replication. The proposed method may benefit the combined analysis of heterogeneous expression data from the growing public repositories.

Algorithms↗

PubMedDate not supplied

Data-based modeling and analysis of bioprocesses: some real experiences.

Data-generated models find numerous applications in areas where the speed of collection and logging of data surpasses the ability to analyze it. This work is meant to addresses some of the challenges and difficulties encountered in the practical application of these methods in an industrial setting and, more specifically, in the bioprocess industry. Neural network and principal component models are the two topics that are covered in detail in this paper. A review of these modeling technologies as applied to bioprocessing is provided, and four original case studies using industrial fermentation data are presented that utilize these models in the context of prediction and monitoring of bioprocess performance.

Algorithms↗