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PubMed2000

Boosting naïve Bayesian learning on a large subset of MEDLINE.

We are concerned with the rating of new documents that appear in a large database (MEDLINE) and are candidates for inclusion in a small specialty database (REBASE). The requirement is to rank the new documents as nearly in order of decreasing potential to be added to the smaller database as possible, so as to improve the coverage of the smaller database without increasing the effort of those who manage this specialty database. To perform this ranking task we have considered several machine learning approaches based on the naï ve Bayesian algorithm. We find that adaptive boosting outperforms naï ve Bayes, but that a new form of boosting which we term staged Bayesian retrieval outperforms adaptive boosting. Staged Bayesian retrieval involves two stages of Bayesian retrieval and we further find that if the second stage is replaced by a support vector machine we again obtain a significant improvement over the strictly Bayesian approach.

Algorithms↗

PubMed2001

Anterior cruciate ligament injuries in the skeletally immature patient.

Injuries to the anterior cruciate ligament in children and youth pose a complex clinical challenge. The literature does not provide the clinician with a well-defined natural history or pathoanatomy, nor does it show agreement as to the outcomes of various treatment choices. Decision making is made difficult by the patients' skeletal and emotional immaturity and by associated intra-articular fracture, physeal fracture, and combination injuries. In an effort to place the problem of ACL injury in children and youth into some perspective and to present an algorithm for management, this article reviews issues in its diagnosis, classification, anatomy, mechanism of injury, and management.

Adolescent↗

PubMedDate not supplied

[Early tracheostomy in patients with tumors in the posterior cranial fossa during postoperative period].

Analyzed in the paper is the early postoperative management of 50 neurosurgery patients operated on the structures of the posterior cranial fossa, primarily, in as far as the respiratory problems and their solution by early tracheostomy are concerned. A classification of indications for early tracheostomy as well as an algorithm of the management of such patients are described.

Adolescent↗

PubMed1992

Expert system design in hematology diagnosis.

A two-part study was designed to test the hypothesis that sufficient information is available from a modern hematology analyzer (the Coulter STKS) to reach a reliable intermediate conclusion which can be used as input to the next decision-making level in the design of a high-performance expert system for hematology diagnosis. In phase one, we analyzed the performance of three probabilistic systems (using Bayes' rule) which interpret STKS data: a control system which took the traditional approach of classifying cases into specific diagnoses, and two test systems which were designed to reach only an intermediate conclusion but not a final diagnosis. One of the test systems classified cases into "textbook categories" of disease and the other utilized defined diagnostic patterns. The systems were tested with 150 cases. The pattern approach ranked the correct choice first in 141 of 150 cases (94%). In phase two, we abandoned Bayes' rule, reformulated the pattern approach into a heuristic classification system, and tested its reliability on 820 cases. The algorithm of the reformulated system was able to classify all 820 cases into the same predominant pattern as a panel of three experienced laboratory hematologists.

Algorithms↗

PubMed2006

[Spectra classification based on generalized discriminant analysis].

A kernel based generalized discriminant analysis (GDA) technique is proposed for the classification of stars, galaxies, and quasars. GDA combines the LDA algorithm with kernel trick, and samples are projected by nonlinear mapping onto the feature space F with high dimensions, and then LDA is conducted in F. Also, it could be inferred that GDA which combines the extension of Fisher's criterion with kernel trick is complementary to kernel Fisher discriminant framework. LDA, GDA, PCA and KPCA were experimentally compared with these three different kinds of spectra. Among these four techniques, GDA obtains the best result, followed by LDA, and PCA is the worst. Although KPCA is also a kernel based technique, its performance is not satisfactory if the selected number of the principal components is small, and in some cases, it appears even worse than LDA, a non-kernel based technique.

English Abstract↗

PubMed1993

Wave packets analysis of two-dimensional protein maps: a new approach to study the diversity of immunoglobulins.

This report describes a mathematical approach for classifying two-dimensional (2D)-protein maps without spot detection or pattern matching. Analysis of electrophoretograms was performed using wave packet decompositions of the signals. The scanned images were automatically decomposed into a set of sub-images organized in a tree structure. Each sub-image contained relevant information such as its energy, or entropy. Moreover the node position itself of the sub-image reflected a frequency localization. A distance was then defined using the tree repartition of these quantities. Finally a statistical clustering on the tree structures was performed, terminating with a classification of the images according to their repartition frequencies. The algorithm has been applied to classify immunoglobulin (Ig) light chain patterns and proved useful to automatically detect monoclonal, oligoclonal or polyclonal Igs.

Algorithms↗

PubMed2000

Classification of 3D structural character of RNA by hydrogen bond and base stacking.

We are developing a computational system to classify RNA structures by its structural character. Here, an improved grouping algorithm was introduced to the system and the base-stacking pattern (BSP) is used as a criterion for the classification in addition to hydrogen-bond pattern (HBP). 279 conformers of 15 mer RNA hairpin were classified into 89 and 36 groups by HBP and BSP, respectively, suggesting that HBP represents conformational character better than BSP.

Algorithms↗

PubMed2006

Multiple hierarchical classification of free-text clinical guidelines.

OBJECTIVE: Manual classification of free-text documents within a predefined hierarchy, commonly required in the medical domain, is highly time consuming task. We present an approach based on supervised learning to automate the classification of clinical guidelines into predefined hierarchical conceptual categories. METHODS AND MATERIAL: Given a set of hierarchically categorized documents in the training stage the learning algorithm exploits the hierarchical structure of the concepts in order to overcome the low number of training examples. The classification task is thus decomposed into a continuous decision process, unlike searching within a decision tree, which follows the concept hierarchy and makes a single decision at each node on the path, multiple paths can be chosen. Classification is based on applying a similarity function at each concept. Several evaluation measures were used, based on the intended use of the hierarchy. In addition, conservative and aggressive stop-criterion strategies for stopping the search through the concept hierarchy were formulated. An evaluation of the approach, including several training methods and multiple evaluation measures, has been performed using a training set of 1136 guidelines from the National Guideline Clearing House set. RESULTS: Based on a test collection consisting of 1038 clinical practice guidelines (CPGs) classified along two hierarchies, of roughly 5000 concepts, in which each CPG was classified by a mean of 10 concepts, a variable precision was observed from 44% to 60% depending on the settings of the training methods. CONCLUSION: These results demonstrate the feasibility of the approach, especially when considering the low ratio of guidelines to classification indices (concepts) in the evaluation data set used here.

Algorithms↗

PubMed2002

Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks.

To increase the ability of ultrasonographic technology for the differential diagnosis of solid breast tumors, we describe a novel computer-aided diagnosis (CADx) system using neural networks for classification of breast tumors. Tumor regions and surrounding tissues are segmented from the physician-located region-of-interest (ROI) images by applying our proposed segmentation algorithm. Cooperating with the segmentation algorithm, three feasible features, including variance contrast, autocorrelation contrast and distribution distortion of wavelet coefficients, were extracted from the ROI images for further classification. A multilayered perceptron (MLP) neural network trained using error back-propagation algorithm with momentum was then used for the differential diagnosis of breast tumors on sonograms. In the experiment, 242 cases (including benign breast tumors from 161 patients and carcinomas from 82 patients) were sampled with k-fold cross-validation (k = 10) to evaluate the performance. The receiver operating characteristic (ROC) area index for the proposed CADx system is 0.9396 +/- 0.0183, the sensitivity is 98.77%, the specificity is 81.37%, the positive predictive value is 72.73% and the negative predictive value is 99.24%. Experimental results showed that our diagnosis model performed very well for breast tumor diagnosis.

Breast Diseases↗

PubMed1995

Segmentation of mammograms using multiple linked self-organizing neural networks.

A possible first stage in the analysis of the mammographic scene is its segmentation into four major components: background (the nonbreast area), pectoral muscle, fibroglandular region (parenchyma), and adipose region. An algorithm has been developed for this task. It is based on the classification of a feature vector constructed from statistical measures of texture calculated at two window sizes. Separate self-organizing neural networks are trained on sample data taken from each of the four regions. The feature vectors from the entire mammogram are then classified with the trained networks linked via a decision logic. To overcome the variability of texture between mammograms the algorithm uses data from a mammogram to classify itself in a staged approach consisting of several binary decisions. The training regions for each successive stage are determined from geometric information produced by the previous stages. The dataset in the study consisted of thirty (fifteen pairs) digitized normal mammograms of variable radiographic appearance. As a measure of performance, the outlines of the parenchyma were compared to those drawn by a radiologist experienced in reading mammograms. Comparison of the areas and perimeters generated by the human and computer observers gives a relationship with correlation coefficients of 0.74 and 0.59 for each measure, respectively. The overlapping areas of the parenchymas segmented by the observers normalized by the combined area was also calculated for each case. The mean and standard deviation of this measure was 0.69 +/- 0.12.

Adipose Tissue↗

PubMed2002

Computational analysis of crystallization trials.

A system for the automatic categorization of the results of crystallization experiments generated by robotic screening is presented. Images from robotically generated crystallization screens are taken at preset time intervals and analyzed by the computer program Crystal Experiment Evaluation Program (CEEP). This program attempts to automatically categorize the individual crystal experiments into a number of simple classes ranging from clear drop to mountable crystal. The algorithm first selects features from the images via edge detection and texture analysis. Classification is achieved via a self-organizing neural net generated from a set of hand-classified images used as a training set. New images are then classified according to this neural net. It is demonstrated that incorporation of time-series information may enhance the accuracy of classification. Preliminary results from the screening of the proteome of Thermotoga maritima are presented showing the utility of the system.

Algorithms↗

PubMed2005

An adaptive multirate algorithm for acquisition of fluorescence microscopy data sets.

We propose an algorithm for adaptive efficient acquisition of fluorescence microscopy data sets using a multirate (MR) approach. We simulate acquisition as part of a larger system for protein classification based on their subcellular location patterns and, thus, strive to maintain the achieved level of classification accuracy as much as possible. This problem is similar to image compression but unique due to additional restrictions, namely causality; we have access only to the information scanned up to that point. While we do want to acquire fewer samples with as low distortion as possible to achieve compression, our goal is to do so while affecting the overall classification accuracy as little as possible. We achieve this by using an adaptive MR scanning scheme which samples the regions of the image area that hold the most pertinent information. Our results show that we can achieve significant compression which we can then use to aquire faster or to increase space resolution of our data set, all while minimally affecting the classification accuracy of the entire system.

Algorithms↗

PubMed2000

Fuzzy EMG classification for prosthesis control.

This paper proposes a fuzzy approach to classify single-site electromyograph (EMG) signals for multifunctional prosthesis control. While the classification problem is the focus of this paper, the ultimate goal is to improve myoelectric system control performance, and classification is an essential step in the control. Time segmented features are fed to a fuzzy system for training and classification. In order to obtain acceptable training speed and realistic fuzzy system structure, these features are clustered without supervision using the Basic Isodata algorithm at the beginning of the training phase, and the clustering results are used in initializing the fuzzy system parameters. Afterwards, fuzzy rules in the system are trained with the back-propagation algorithm. The fuzzy approach was compared with an artificial neural network (ANN) method on four subjects, and very similar classification results were obtained. It is superior to the latter in at least three points: slightly higher recognition rate; insensitivity to overtraining; and consistent outputs demonstrating higher reliability. Some potential advantages of the fuzzy approach over the ANN approach are also discussed.

Algorithms↗

PubMed2004

Wavelet-based neural network analysis of ophthalmic artery Doppler signals.

In this study, ophthalmic artery Doppler signals were recorded from 115 subjects, 52 of whom had ophthalmic artery stenosis while the rest were healthy controls. Results were classified using a wavelet-based neural network. The wavelet-based neural network model, employing the multilayer perceptron, was used for analysis of ophthalmic artery Doppler signals. A multilayer perceptron neural network (MLPNN) trained with the Levenberg-Marquardt algorithm was used to detect stenosis in ophthalmic arteries. In order to determine the MLPNN inputs, spectral analysis of ophthalmic artery Doppler signals was performed using wavelet transform. The MLPNN was trained, cross validated, and tested with training, cross validation, and testing sets, respectively. All data sets were obtained from ophthalmic arteries of healthy subjects and subjects suffering from ophthalmic artery stenosis. The correct classification rate was 97.22% for healthy subjects, and 96.77% for subjects having ophthalmic artery stenosis. The classification results showed that the MLPNN trained with the Levenberg-Marquardt algorithm was effective to detect ophthalmic artery stenosis.

Adult↗

PubMed1986

An algorithm for the management of scoliosis.

Scoliosis is a lateral and rotary deformity of the spine that is often found in children. Treatment of this deformity is based on the principle of early recognition and prevention, although surgical correction may be warranted in the case of progressive curves. There is no scientific evidence that spinal manipulative therapy (SMT) has any effect on curve progression in patients with idiopathic scoliosis; however, there is clinical evidence that SMT is a useful treatment for those patients who have an associated mechanical backache. This paper reviews the classification, natural history, pathogenesis, and clinical and radiological assessment of scoliosis. An algorithm for the management of scoliosis by chiropractors is presented, and illustrative cases from a scoliosis clinic in a university hospital are used to reinforce important clinical principles.

Adolescent↗

PubMed2002

Clinical utility of CD23 and FMC7 antigen coexistent expression in B-cell lymphoproliferative disorder subclassification.

BACKGROUND: CD23 and FMC7 are normal B-cell antigens utilized during diagnostic immunophenotyping of suspected lymphoproliferative disorders. However, the diagnostic utility of coexistent antigenic expression patterns with simultaneous two-color staining and flow cytometric analysis has not been studied extensively. METHODS: Using multiparameter flow cytometry, we evaluated the expression pattern of FMC7 and CD23 in 218 cases of B-cell lymphoma from blood and bone marrow specimens. RESULTS: The CD23(+)/FMC7(-) pattern was the most common pattern in patients with chronic lymphocytic leukemia and related variants. The widest variation of patterns was found in patients with follicular cell lymphoma, large cell lymphoma, and Waldenström's macroglobulinemia, a lymphoplasmacytoid disorder, although most cases expressed the CD23-/FMC7(+) pattern. The CD23 and FMC7 antigen, along with the CD5 coexpression pattern, provides critical adjunctive data. These data allow accurate classification of the majority of cases, thereby providing a key aspect of a reliable diagnostic algorithm. The CD23 and FMC7 antigen expression pattern, along with selected other antigens, was predictive of subtypes in >95% of lymphoproliferative cases and narrowed the differential diagnosis in the remaining cases. CONCLUSION: The flow cytometric CD23/FMC7 expression pattern achieved by multicolor immunophenotyping facilitates accurate and reproducible classification of B-cell lymphomas and has diagnostic utility.

Algorithms↗

PubMed2006

Fold recognition via a tree.

Recently, we developed a pairwise structural alignment algorithm using realistic structural and environmental information (SAUCE). In this paper, we at first present an automatic fold hierarchical classification based on SAUCE alignments. This classification enables us to build a fold tree containing different levels of multiple structural profiles. Then a tree-based fold search algorithm is described. We applied this method to a group of structures with sequence identity less than 35% and did a series of leave one out tests. These tests are approximately comparable to fold recognition tests on superfamily level. Results show that fold recognition via a fold tree can be faster and better at detecting distant homologues than classic fold recognition methods.

Algorithms↗

PubMed2004

Fast iterative gene clustering based on information theoretic criteria for selecting the cluster structure.

Grouping of genes into clusters according to their expression levels is important for deriving biological information, e.g., on gene functions based on microarray and other related analyses. The paper introduces the selection of the number of clusters based on the minimum description length (MDL) principle for the selection of the number of clusters in gene expression data. The main feature of the new method is the ability to evaluate in a fast way the number of clusters according to the sound MDL principle, without exhaustive evaluations over all possible partitions of the gene set. The estimation method can be used in conjunction with various clustering algorithms. A recent clustering algorithm using principal component analysis, the "gene shaving" (GS) procedure, can be modified to make use of the new MDL estimation method, replacing the Gap statistics originally used in GS algorithm. The resulting clustering algorithm is shown to perform better than GS-Gap and CEM (classification expectation maximization), in the simulations using artificial data. The proposed method is applied to B-cell differentiation data, and the resulting clusters are compared with those found by self-organizing maps (SOM).

Algorithms↗