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PubMed1997

Medical causes on stillbirth certificates in England and Wales: distribution and results of hierarchical classifications tested by the Office for National Statistics.

OBJECTIVE: To produce a classification of stillbirths registered in England and Wales compatible with a previously described classification for neonatal deaths; to compare national data for intrapartum stillbirths with those for the remaining stillbirths; and to report on stillbirths with a gestational age of 24 to 27 completed weeks first made registrable on 1 October 1992. DESIGN: Algorithms were developed and tested to derive hierarchical cause classifications making use of multiple causes mentioned on stillbirth certificates. RESULTS: The additional information available since 1986 on cause and time of death of stillbirths, classified in a hierarchical fashion allows a more meaningful interpretation of the available information on the causes of stillbirth than was previously possible and does not perturb ongoing trends. Antepartum deaths without a classifiable cause contributed the majority: between 1992 and 1994 they accounted for 43% if mentions of asphyxial conditions are regarded as classifiable causes, and 82% if not considered as causal. Stillbirths described as occurring intrapartum are consistently of higher gestational age and birthweight than the remainder, lending validity to the time of death given. CONCLUSIONS: The national use of a classification including reported time of death of the fetus and mentions of asphyxial conditions is justifiable, providing a distinction is made between associated mentions and causal conditions. Better and more complete clinical information on stillbirth certificates will contribute further to understanding of their causes.

Algorithms↗
PubMed2005

Combining neural network and genetic algorithm for prediction of lung sounds.

Recognition of lung sounds is an important goal in pulmonary medicine. In this work, we present a study for neural networks-genetic algorithm approach intended to aid in lung sound classification. Lung sound was captured from the chest wall of The subjects with different pulmonary diseases and also from the healthy subjects. Sound intervals with duration of 15-20 s were sampled from subjects. From each interval, full breath cycles were selected. Of each selected breath cycle, a 256-point Fourier Power Spectrum Density (PSD) was calculated. Total of 129 data values calculated by the spectral analysis are selected by genetic algorithm and applied to neural network. Multilayer perceptron (MLP) neural network employing backpropagation training algorithm was used to predict the presence or absence of adventitious sounds (wheeze and crackle). We used genetic algorithms to search for optimal structure and training parameters of neural network for a better predicting of lung sounds. This application resulted in designing of optimum network structure and, hence reducing the processing load and time.

Algorithms↗
PubMed2004

Classification of ecstasy tablets using trace metal analysis with the application of chemometric procedures and artificial neural network algorithms.

This work is concerned with an investigation into the practicalities of using ICP-MS data obtained from the analysis of ecstasy tablets to provide linkage information from seizure to seizure. The generated data was analysed using different statistical techniques, namely principal component analysis, Hierarchical clustering and artificial neural networks. The relative merits of these different techniques are discussed.

Algorithms↗
PubMed1997

[When is surgery indicated for treatment of urinary stress incontinence?].

Stress incontinence is the most frequent type of urinary incontinence in women. Its diagnosis and therapy is a so far not quite resolved problem. In particular conservative treatment and the decision-taking algorithm leading, after conservative treatment has failed, to selection of a suitable operation has undergone changes during the past 10 years. Classification of different risk groups of patients, the decision-taking algorithm, the most frequent mistakes and errors during conservative treatment, indication and implementation of surgery incl. the most frequent complications are the subject of the submitted paper.

Female↗
PubMed2006

Information extraction from sound for medical telemonitoring.

Today, the growth of the aging population in Europe needs an increasing number of health care professionals and facilities for aged persons. Medical telemonitoring at home (and, more generally, telemedicine) improves the patient's comfort and reduces hospitalization costs. Using sound surveillance as an alternative solution to video telemonitoring, this paper deals with the detection and classification of alarming sounds in a noisy environment. The proposed sound analysis system can detect distress or everyday sounds everywhere in the monitored apartment, and is connected to classical medical telemonitoring sensors through a data fusion process. The sound analysis system is divided in two stages: sound detection and classification. The first analysis stage (sound detection) must extract significant sounds from a continuous signal flow. A new detection algorithm based on discrete wavelet transform is proposed in this paper, which leads to accurate results when applied to nonstationary signals (such as impulsive sounds). The algorithm presented in this paper was evaluated in a noisy environment and is favorably compared to the state of the art algorithms in the field. The second stage of the system is sound classification, which uses a statistical approach to identify unknown sounds. A statistical study was done to find out the most discriminant acoustical parameters in the input of the classification module. New wavelet based parameters, better adapted to noise, are proposed in this paper. The telemonitoring system validation is presented through various real and simulated test sets. The global sound based system leads to a 3% missed alarm rate and could be fused with other medical sensors to improve performance.

Activities of Daily Living↗
PubMed1998

Feature selection for genetic sequence classification.

MOTIVATION: Most of the existing methods for genetic sequence classification are based on a computer search for homologies in nucleotide or amino acid sequences. The standard sequence alignment programs scale very poorly as the number of sequences increases or the degree of sequence identity is <30%. Some new computationally inexpensive methods based on nucleotide or amino acid compositional analysis have been proposed, but prediction results are still unsatisfactory and depend on the features chosen to represent the sequences. RESULTS: In this paper, a feature selection method based on the Gamma (or near-neighbour) test is proposed. If there is a continuous or smooth map from feature space to the classification target values, the Gamma test gives an estimate for the mean-squared error of the classification, despite the fact that one has no a priori knowledge of the smooth mapping. We can search a large space of possible feature combinations for a combination which gives a smallest estimated mean-squared error using a genetic algorithm. The method was used for feature selection and classification of the large subunits of rRNA according to RDP (Ribosomal Database Project) phylogenetic classes. The sequences were represented by dinucleotide frequency distribution. The nearest-neighbour criterion has been used to estimate the predictive accuracy of the classification based on the selected features. For examples discussed, we found that the classification according to the first nearest neighbour is correct for 80% of the test samples. If we consider the set of the 10 nearest neighbours, then 94% of the test samples are classified correctly. AVAILABILITY: The principal novel component of this method is the Gamma test and this can be downloaded compiled for Unix Sun 4, Windows 95 and MS-DOS from http://www.cs.cf.ac.uk/ec/ CONTACT: s.margetts@cs.cf.ac.uk

Algorithms↗
PubMedDate not supplied

Comparison of health state utilities using community and patient preference weights derived from a survey of patients with HIV/AIDS.

OBJECTIVES: The authors compare health state utilities derived from a national survey of patients with HIV/AIDS to represent community-based preferences with utilities derived from the same survey representing patient preferences; explore the relationships between these utilities and the dimensions of the SF-6D health state classification; and examine the implications of differences in the source of utilities for a cost-effectiveness analysis of early treatment of patients with HIV/AIDS. METHODS: The authors derived community-based standard gamble (SG) utilities using an algorithm developed for the SF-6D health state classification system. The authors derived patient SG utilities from HIV/AIDS patient rating scale self-assessments using a power transformation. Data were from the HIV Cost and Services Utilization Study, a probability sample of 2864 HIV-infected adults receiving care in the United States in 1996. RESULTS: Patient SG utilities were higher than community SG utilities by 4% to 9% (0.979 vs. 0.937, 0.910 vs. 0.841, and 0.845 vs. 0.778; P < 0.001 for all comparisons). In regression analyses, patient SG utilities were less influenced by physical functioning, pain, and mental health dimensions of the SF-6D. The base case results of a cost-effectiveness analysis comparing early antiretroviral therapy to deferred therapy were unaffected by the choice between community ($20,100 per quality-adjusted life year) and patient ($18,400 per quality-adjusted life year) perspectives. The impact of the choice of utilities remained small in sensitivity analyses that varied the influence of treatment side effects on utilities and the initial symptom status of patients. CONCLUSION: There are differences between community and patient utilities for HIV/AIDS health states, although even when treatment side effects are important, these differences may not affect cost-effectiveness ratios.

Acquired Immunodeficiency Syndrome↗
PubMed2001

Automatic analysis and classification of surface electromyography.

In this paper, parametric modeling of surface electromyography (EMG) algorithms that facilitates automatic SEMG feature extraction and artificial neural networks (ANN) are combined for providing an integrated system for the automatic analysis and diagnosis of myopathic disorders. Three paradigms of ANN were investigated: the multilayer backpropagation algorithm, the self-organizing feature map algorithm and a probabilistic neural network model. The performance of the three classifiers was compared with that of the old Fisher linear discriminant (FLD) classifiers. The results have shown that the three ANN models give higher performance. The percentage of correct classification reaches 90%. Poorer diagnostic performance was obtained from the FLD classifier. The system presented here indicates that surface EMG, when properly processed, can be used to provide the physician with a diagnostic assist device.

Algorithms↗
PubMed1986

An algorithm for the treatment of unstable thoracolumbar fractures.

This article describes a fracture classification used at the University of Texas Medical Branch that takes into account the fracture mechanics and attempts to match the requirements of the fracture to the strong points of a particular spinal implant. The present algorithm for the treatment of thoracolumbar fractures is presented, and its efficacy is demonstrated by comparing it with an evolutionary group of spinal fractures cases

Biomechanical Phenomena↗
PubMed1997

Classification of normal and abnormal electrogastrograms using multilayer feedforward neural networks.

A neural network approach is proposed for the automated classification of the normal and abnormal EGG. Two learning algorithms, the quasi-Newton and the scaled conjugate gradient method for the multilayer feedforward neural networks (MFNN), are introduced and compared with the error backpropagation algorithm. The configurations of the MFNN are determined by experiment. The raw EGG data, its power spectral data, and its autoregressive moving average (ARMA) modelling parameters are used as the input to the MFNN and compared with each other. Three indexes (the percent correct, sum-squared error and complexity per iteration) are used to evaluate the performance of each learning algorithm. The results show that the scaled conjugate gradient algorithm performs best, in that it is robust and provides a super-linear convergence rate. The power spectral representation and the ARMA modelling parameters of the EGG are found to be better types of the input to the network for this specific application, both yielding a percent correctness of 95% on the test set. Although the results are focused on the classification of the EGG, this paper should provide useful information for the classification of other biomedical signals.

Algorithms↗
PubMed2006

Digitized pathology: theory and experiences in automated tissue-based virtual diagnosis.

AIMS: To describe the theory and develop an automated virtual slide screening system. Theoretical considerations. Tissue-based diagnosis separates into (a) sampling procedure to allocate the slide area containing diagnostic information, and (b) evaluation of diagnosis from the selected area. Nyquist's theorem broadly applied in acoustics, serves to presetting the sampling accuracy. Tissue-based diagnosis relies on two different information systems: (a) texture, and (b) object information. Texture information can be derived by recursive formulas without image segmentation. Object information requires image segmentation and feature extraction. Both algorithms complete another to a "self-learning" classification system. METHODS: Non-overlapping compartments of the original virtual slide (image) are chosen at random with predefined error-rate (Nyquist's theorem). The standardized image compartments are subject for texture and object analysis. The recursive formula of texture analysis computes median gray values and local noise distribution. Object analysis includes automated measurements of immunohistochemically stained slides. The computations performed at different magnifications (x 2, x 4.5, x 10, x 20, x 40) are subject to multivariate statistically analysis and diagnosis classification. RESULTS: A total of 808 lung cancer cases of diagnoses groups: cohort (1) normal lung (318 cases) - cancer (490 cases); cancer subdivided: cohort (2) small cell lung cancer (10 cases) - non-small cell lung cancer (480 cases); non-small cell lung cancer subdivided: cohort (3) squamous cell carcinoma (318 cases) - adenocarcinoma (194 cases) - large cell carcinoma (70 cases) was analyzed. Cohorts (1) and (2) were classified correctly in 100%, cohort (3) in more than 95%. The selected area can be limited to 10% of the original image without increased error rate. A second approach included 233 breast tissue cases (105 normal, 128 breast carcinomas) and 88 lung tissue cases (58 normal, 38 cancer). Texture analysis revealed a correct classification with only 10 training set cases in >92% for both, breast and lung tissue. CONCLUSIONS: The developed system is a fast and reliable procedure to fulfill all requirements for an automated "pre-screening" of virtual slides in tissue-based diagnosis.

Algorithms↗
PubMed2007

Part 1. Automated change detection and characterization in serial MR studies of brain-tumor patients.

The goal of this study was to create an algorithm which would quantitatively compare serial magnetic resonance imaging studies of brain-tumor patients. A novel algorithm and a standard classify-subtract algorithm were constructed. The ability of both algorithms to detect and characterize changes was compared using a series of digital phantoms. The novel algorithm achieved a mean sensitivity of 0.87 (compared with 0.59 for classify-subtract) and a mean specificity of 0.98 (compared with 0.92 for classify-subtract) with regard to identification of voxels as changing or unchanging and classification of voxels into types of change. The novel algorithm achieved perfect specificity in seven of the nine experiments. The novel algorithm was additionally applied to a short series of clinical cases, where it was shown to identify visually subtle changes. Automated change detection and characterization could facilitate objective review and understanding of serial magnetic resonance imaging studies in brain-tumor patients.

Algorithms↗
PubMedDate not supplied

[An algorithm for synthesis of small-size autonomous devices for detection of medico-biological parameters].

Problems of synthesis of devices for detection of medico-biological parameters are considered. A classification of the devices and their functional units is proposed. An algorithm for synthesis of detectors of medico-biological parameters on the basis of multiple-use functional units is suggested. The algorithm is based on analysis of functional unit modifications providing optimization of the device characteristics.

Algorithms↗
PubMed2002

[Review: Segmentation and classification methods of 3D medical images].

This paper presents a survey of recent publications (published in 1990 or later) concerning segmentation and classification of medical images. These methods will be classified into six types: cluster (threshold), statistics methods, deformable contour, region growing, mathematics morphology, nonlinear methods (fuzzy segmentation, neural networks, genetic algorithm) and 3D model. Each of the major classes of image segmentation and classification techniques and several specific examples of each class of algorithm are described respectively in detail. At last, the developing trend of 3D medical image is also discussed.

Algorithms↗
PubMedDate not supplied

Empirically derived classification of coagulation disorders in 224 patients.

BACKGROUND: It is not known whether the current molecular classification of blood coagulation disorders into severe (0-1%), moderate (1-5%) and mild (5-40% factor activity remaining) corresponds to the actual clinical situation or is in the patients best interest. METHODS: A questionnaire-based study of 244 patients. Principal factor analysis was used to create a set of variables for classification, which was performed using K-means algorithm. The main variables were use of prophylactic treatment during the last five years and during the last 12 months, home treatment, bleeding, surgery, antibody inhibitors, use of cold medication, pain, use of analgesics, functional disability and physical activity level. RESULTS: The first five variables of the main outcome measures loaded to a factor reflecting bleeding (bleeding factor) and the last four to a pain factor; both factors produced a 3-cluster solution with severe, moderate and mild bleeding or pain. Overlap between the molecular, bleeding and pain classifications was not extensive. Only 16% of 81 patients with severe coagulation factor deficiency had severe musculoskeletal pain and disability. Furthermore, only 28.6% of the patients with severe von Willebrand's disease actually had a severe bleeding disorder. CONCLUSIONS: Molecular classification does not correlate very well with the severity of disease as reflected in bleeding and pain. This is due to better prognosis for patients on modern medical management. Appropriate patient classification is a basis for defining and managing patients' clinical problems.

Adolescent↗
PubMed2001

Logarithmic simulated annealing for X-ray diagnosis.

We present a new stochastic learning algorithm and first results of computational experiments on fragments of liver CT images. The algorithm is designed to compute a depth-three threshold circuit, where the first layer is calculated by an extension of the Perceptron algorithm by a special type of simulated annealing. The fragments of CT images are of size 119x119 with eight bit grey levels. From 348 positive (focal liver tumours) and 348 negative examples a number of hypotheses of the type w(1)x(1)+. . .;+w(n)x(n)>/=theta were calculated for n=14161. The threshold functions at levels two and three were determined by computational experiments. The circuit was tested on various sets of 50+50 additional positive and negative examples. For depth-three circuits, we obtained a correct classification of about 97%. The input to the algorithm is derived from the DICOM standard representation of CT images. The simulated annealing procedure employs a logarithmic cooling schedule c(k)=Gamma/ln(k+2), where Gamma is a parameter that depends on the underlying configuration space. In our experiments, the parameter Gamma is chosen according to estimations of the maximum escape depth from local minima of the associated energy landscape.

Algorithms↗
PubMed1997

Evaluation of an algorithm for integrated management of childhood illness in an area of Kenya with high malaria transmission.

In 1993, the World Health Organization completed the development of a draft algorithm for the integrated management of childhood illness (IMCI), which deals with acute respiratory infections, diarrhoea, malaria, measles, ear infections, malnutrition, and immunization status. The present study compares the performance of a minimally trained health worker to make a correct diagnosis using the draft IMCI algorithm with that of a fully trained paediatrician who had laboratory and radiological support. During the 14-month study period, 1795 children aged between 2 months and 5 years were enrolled from the outpatient paediatric clinic of Siaya District Hospital in western Kenya; 48% were female and the median age was 13 months. Fever, cough and diarrhoea were the most common chief complaints presented by 907 (51%), 395 (22%), and 199 (11%) of the children, respectively; 86% of the chief complaints were directly addressed by the IMCI algorithm. A total of 1210 children (67%) had Plasmodium falciparum infection and 1432 (80%) met the WHO definition for anaemia (haemoglobin < 11 g/dl). The sensitivities and specificities for classification of illness by the health worker using the IMCI algorithm compared to diagnosis by the physician were: pneumonia (97% sensitivity, 49% specificity); dehydration in children with diarrhoea (51%, 98%); malaria (100%, 0%); ear problem (98%, 2%); nutritional status (96%, 66%); and need for referral (42%, 94%). Detection of fever by laying a hand on the forehead was both sensitive and specific (91%, 77%). There was substantial clinical overlap between pneumonia and malaria (n = 895), and between malaria and malnutrition (n = 811). Based on the initial analysis of these data, some changes were made in the IMCI algorithm. This study provides important technical validation of the IMCI algorithm, but the performance of health workers should be monitored during the early part of their IMCI training.

Algorithms↗