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At least 1,009 records · Page 56Linked to original sources
PubMed1998

[A decision-support system for hematology].

Thrombocytopenia and following bleeding at the treatment of hematological malignancies is a dangerous complication. The indication of thrombocyte transfusion is the key point for the therapy and proylaxy of bleeding. The all problem is divided into two parts. Evaluation of the risk of bleeding (80% of decision), estimation of the risk of aloimunization and risk of connected with the transfusion (20% decision). For now we are concentrated to the evaluation of the risk of bleeding. In the first stage we are concentrated to statistical evaluation of values to define factors possibly highering the risk of bleeding. Factors were determined with help of two test, GUHA method and using literature. For recognized factors were trained 3 layer neuron nets with a non-linear method pack propagation. After that an application was developed to determine the risk of bleeding for a routine use in clinical practice.

Decision Making, Computer-Assisted↗
PubMed2000

TraumaSCAN: assessing penetrating trauma with geometric and probabilistic reasoning.

This paper presents TraumaSCAN, a prototype computer system for assessing the effects of penetrating trauma to the chest and abdomen. TraumaSCAN combines geometric reasoning about potentially injured anatomic structures with (probabilistic) diagnostic reasoning about the consequences of these injuries. We also present results obtained from testing TraumaSCAN retrospectively on 26 actual gunshot wound cases.

Bayes Theorem↗
PubMed2002

Comparing machine learning classifiers for diagnosing glaucoma from standard automated perimetry.

PURPOSE: To determine which machine learning classifier learns best to interpret standard automated perimetry (SAP) and to compare the best of the machine classifiers with the global indices of STATPAC 2 and with experts in glaucoma. METHODS: Multilayer perceptrons (MLP), support vector machines (SVM), mixture of Gaussian (MoG), and mixture of generalized Gaussian (MGG) classifiers were trained and tested by cross validation on the numerical plot of absolute sensitivity plus age of 189 normal eyes and 156 glaucomatous eyes, designated as such by the appearance of the optic nerve. The authors compared performance of these classifiers with the global indices of STATPAC, using the area under the ROC curve. Two human experts were judged against the machine classifiers and the global indices by plotting their sensitivity-specificity pairs. RESULTS: MoG had the greatest area under the ROC curve of the machine classifiers. Pattern SD (PSD) and corrected PSD (CPSD) had the largest areas under the curve of the global indices. MoG had significantly greater ROC area than PSD and CPSD. Human experts were not better at classifying visual fields than the machine classifiers or the global indices. CONCLUSIONS: MoG, using the entire visual field and age for input, interpreted SAP better than the global indices of STATPAC. Machine classifiers may augment the global indices of STATPAC.

Diagnosis, Computer-Assisted↗
PubMed2001

[Genetic algorithms and its application to spectral analysis].

Genetic algorithm derived from the principle of natural selection and the concepts of genetics is a global search method, which is not only highly effective but also parallel. Its essential theory, operating method, application to spectral analysis and trend of development are reviewed with 66 references.

Algorithms↗
PubMed2005

National plan for reliable tuberculosis laboratory services using a systems approach. Recommendations from CDC and the Association of Public Health Laboratories Task Force on Tuberculosis Laboratory Services.

Since the mid-1990s, public health laboratories have improved tuberculosis (TB) test performance, which has contributed to the resumption of the decline in TB incidence in the United States. However, to eliminate TB in the United States, further improvements are needed in laboratory services to support TB treatment, prevention, and control. A critical step is the development of an integrated system that ensures prompt and reliable laboratory testing and flow of information among laboratorians, clinicians, and TB-control officials. Challenges to developing such a system include 1) establishing lines of communication among laboratorians, clinicians, and TB-control officials; 2) expediting reporting of laboratory results, which can avoid delayed or inappropriate treatment and missed opportunities to prevent transmission; 3) developing evidence-based recommendations for use of new laboratory technologies; 4) maintaining staff proficiency in light of declining numbers of specimens to test, workforce shortages, and loss of laboratory expertise; and 5) upgrading laboratory information systems and connecting all partners. The report of the Association of Public Health Laboratories Task Force presents a framework to improve the future of TB laboratory services and describes the role of the laboratory in TB treatment and control, Task Force processes, general principles and benchmarks, and steps for the dissemination of the Task Force recommendations. This MMWR expands on the Task Force report by describing specific actions and performance measures to guide development and implementation of an integrated system for providing TB laboratory services. CDC and the Association of Public Health Laboratories have developed these guidelines so that laboratorians, clinicians, public health officials, administrators, and funding entities can work together to ensure that health-care providers and TB-control officials have the information needed to treat TB patients, prevent TB transmission, and ultimately eliminate TB in the United States.

Clinical Laboratory Information Systems↗
PubMed2005

[Study on technique of thermal imaging and soft-sensing].

The differences of body surface temperature reflect the changes of the status of body tissues. In this regard, detecting and forecasting the changes of the surface temperature is the objective of the technique of medical thermal diagnosis, and how to diagnose the disease earlier with the use of thermal images is a common problem in the field of medical diagnostics and biological engineering. The authors put forward that utilizing the soft-sensing techniques in the field of engineering will be a good solution.

Body Temperature↗
PubMed2006

[Medical image segmentation techniques].

Medical image segmentation is an important application of image segmentation. However it is the bottleneck that restrains medical image application in clinical practice. In this paper, the aim and significance of medical image segmentation are discussed, the development of medical image segmentation techniques is sketched, and a review of the medical image segmentation techniques is given.

Algorithms↗
PubMed1995

Image analysis and machine learning applied to breast cancer diagnosis and prognosis.

Fine needle aspiration (FNA) accuracy is limited by, among other factors, the subjective interpretation of the aspirate. We have increased breast FNA accuracy by coupling digital image analysis methods with machine learning techniques. Additionally, our mathematical approach captures nuclear features ("grade") that are prognostically more accurate than are estimates based on tumor size and lymph node status. An interactive computer system evaluates, diagnoses and determines prognosis based on nuclear features derived directly from a digital scan of FNA slides. A consecutive series of 569 patients provided the data for the diagnostic study. A 166-patient subset provided the data for the prognostic study. An additional 75 consecutive, new patients provided samples to test the diagnostic system. The projected prospective accuracy of the diagnostic system was estimated to be 97% by 10-fold cross-validation, and the actual accuracy on 75 new samples was 100%. The projected prospective accuracy of the prognostic system was estimated to be 86% by leave-one-out testing.

Biopsy, Needle↗
PubMed1997

Coherency and connectivity in oscillating neural networks: linear partialization analysis.

This paper studies the relation between the functional synaptic connections between two artificial neural networks and the correlation of their spiking activities. The model neurons had realistic non-oscillatory dynamic properties and the networks showed oscillatory behavior as a result of their internal synaptic connectivity. We found that both excitation and inhibition cause phase locking of the oscillating activities. When the two networks excite each other the oscillations synchronize with zero phase lag, whereas mutual inhibition between the networks resulted in an anti-phase (half period phase difference) synchronization. Correlations between the activities of the two networks can also be caused by correlated external inputs driving the systems (common input). Our analysis shows that when the networks exhibit oscillatory behavior and the rate of the common input is smaller than a characteristic network oscillator frequency, the cross-correlation functions between the activities of two systems still carry information about the mutual synaptic connectivity. This information can be retrieved with linear partialization, removing the influence of the common input. We further explored the network responses to periodic external input. We found that when the input is of a frequency smaller than a certain threshold, the network responds with bursts at the same frequency as the input. Above the threshold, the network responds with a fraction of the input frequency. This frequency threshold, characterizing the oscillatory properties of the network, is also found to determine the limit to which linear partialization works.

Computer Simulation↗
PubMed1996

Improving prediction of protein secondary structure using structured neural networks and multiple sequence alignments.

The prediction of protein secondary structure by use of carefully structured neural networks and multiple sequence alignments has been investigated. Separate networks are used for predicting the three secondary structures alpha-helix, beta-strand, and coil. The networks are designed using a priori knowledge of amino acid properties with respect to the secondary structure and the characteristic periodicity in alpha-helices. Since these single-structure networks all have less than 600 adjustable weights, overfitting is avoided. To obtain a three-state prediction of alpha-helix, beta-strand, or coil, ensembles of single-structure networks are combined with another neural network. This method gives an overall prediction accuracy of 66.3% when using 7-fold cross-validation on a database of 126 nonhomologous globular proteins. Applying the method to multiple sequence alignments of homologous proteins increases the prediction accuracy significantly to 71.3% with corresponding Matthew's correlation coefficients C alpha = 0.59, C beta = 0.52, and Cc = 0.50. More than 72% of the residues in the database are predicted with an accuracy of 80%. It is shown that the network outputs can be interpreted as estimated probabilities of correct prediction, and, therefore, these numbers indicate which residues are predicted with high confidence.

Amino Acid Sequence↗
PubMed2003

Neural network training with global optimization techniques.

This paper presents an approach of using Simulated Annealing and Tabu Search for the simultaneous optimization of neural network architectures and weights. The problem considered is the odor recognition in an artificial nose. Both methods have produced networks with high classification performance and low complexity. Generalization has been improved by using the backpropagation algorithm for fine tuning. The combination of simple and traditional search methods has shown to be very suitable for generating compact and efficient networks.

Algorithms↗
PubMed2004

Global robust stability for shunting inhibitory CNNs with delays.

In this paper, the problem of global robust stability for shunting inhibitory cellular neural networks (SICNNs) is studied. A sufficient condition guaranteeing the network's global robust stability is established. The result can easily be used to verify globally robust stable networks. An example is given to illustrate that the conditions of our results are feasible.

Animals↗
PubMed1995

Utilization of neural network in the elaboration of an evaluation scale for pain in cerebral palsy.

An interesting aspect of neural networks is shown in the elaboration of an evaluation scale for pain in cerebral palsy with severe mental retardation. Because of the diversity of cases, the number of items had to be limited in the final step of statistical validation. Classical analysis on prior data did not allow to decide whether the variability in results is more likely due to the type of disability (i.e., the possibility of pain expression) than to the actual presence of pain. A neural network was used to find implicit relations between the data, with the advantage of having total control on the variables' status by applying variations in the network architecture. This allowed for the rapid identification more significant item combinations as a function of degree of relationship to pain in cerebral palsy.

Adolescent↗
PubMed2001

DIAMED: a probabilistic diagnostic aid system on the web.

DIAMED is a system to assist the physicians in the diagnostic process using probabilistic networks as knowledge representation. These networks make it possible to reason on medical data by applying Bayesian methods and to take into account uncertainties of the facts in the resolution of the clinical cases. The proposed model re-uses knowledge contained in an existing knowledge base (ADM). An interface of DIAMED developed on a Web server remotely assists the experts of each medical specialty in updating and validating the knowledge base. Most of the data processing is automated while being based on information preexistent in the ADM base : Constitution of lexicons starting from the existing dictionaries of the ADM system, are then used to work out the requests for selection and update of the knowledge base. One of its assets resides in its pseudo-segmented structure in several layers. The propagation of information is thus limited to only one part of the probabilistic network and calculations are therefore limited.

Artificial Intelligence↗