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PubMed1992

Mathematical characterization of Chaos Game Representation. New algorithms for nucleotide sequence analysis.

Chaos Game Representation (CGR) can recognize patterns in the nucleotide sequences, obtained from databases, of a class of genes using the techniques of fractal structures and by considering DNA sequences as strings composed of four units, G, A, T and C. Such recognition of patterns relies only on visual identification and no mathematical characterization of CGR is known. The present report describes two algorithms that can predict the presence or absence of a stretch of nucleotides in any gene family. The first algorithm can be used to generate DNA sequences represented by any point in the CGR. The second algorithm can simulate known CGR patterns for different gene families by setting the probabilities of occurrence of different di- or trinucleotides by a trial and error process using some guidelines and approximate rules-of-thumb. The validity of the second algorithm has been tested by simulating sequences that can mimic the CGRs of vertebrate non-oncogenes, proto-oncogenes and oncogenes. These algorithms can provide a mathematical basis of the CGR patterns obtained using nucleotide sequences from databases.

Algorithms↗

PubMed1992

Dynamic programming algorithms for biological sequence comparison.

Efficient dynamic programming algorithms are available for a broad class of protein and DNA sequence comparison problems. These algorithms require computer time proportional to the product of the lengths of the two sequences being compared [O(N2)] but require memory space proportional only to the sum of these lengths [O(N)]. Although the requirement for O(N2) time limits use of the algorithms to the largest computers when searching protein and DNA sequence databases, many other applications of these algorithms, such as calculation of distances for evolutionary trees and comparison of a new sequence to a library of sequence profiles, are well within the capabilities of desktop computers. In particular, the results of library searches with rapid searching programs, such as FASTA or BLAST, should be confirmed by performing a rigorous optimal alignment. Whereas rapid methods do not overlook significant sequence similarities, FASTA limits the number of gaps that can be inserted into an alignment, so that a rigorous alignment may extend the alignment substantially in some cases. BLAST does not allow gaps in the local regions that it reports; a calculation that allows gaps is very likely to extend the alignment substantially. Although a Monte Carlo evaluation of the statistical significance of a similarity score with a rigorous algorithm is much slower than the heuristic approach used by the RDF2 program, the dynamic programming approach should take less than 1 hr on a 386-based PC or desktop Unix workstation. For descriptive purposes, we have limited our discussion to methods for calculating similarity scores and distances that use gap penalties of the form g = rk. Nevertheless, programs for the more general case (g = q+rk) are readily available. Versions of these programs that run either on Unix workstations, IBM-PC class computers, or the Macintosh can be obtained from either of the authors.

Algorithms↗

PubMed1990

Hypermedia and randomized algorithms for medical expert systems.

KNET is an environment for constructing probabilistic, knowledge-intensive systems within the axiomatic framework of decision theory. The KNET architecture defines a complete separation between the hypermedia user interface on the one hand, and the representation and management of expert opinion on the other. KNET offers a choice of algorithms for probabilistic inference. We and our coworkers have used KNET to build consultation systems for lymph-node pathology, bone-marrow transplantation therapy, clinical epidemiology, and alarm management in the intensive-care unit. Most important, KNET contains a randomized approximation scheme (RAS) for the difficult and almost certainly intractable problem of Bayesian inference. Our algorithm can, in many circumstances, perform efficient approximate inference in large and richly interconnected models of medical diagnosis. In this article, we describe the architecture of KNET, construct a randomized algorithm for probabilistic inference, and analyze the algorithm's performance. Finally, we characterize our algorithms' empiric behavior and explore its potential for parallel speedups. From design to implementation, then, KNET demonstrates the crucial interaction between theoretical computer science and medical informatics.

Algorithms↗

PubMed1992

BIO-SPEAD: a parallel computing environment to accelerate development of biologic signal processing algorithms.

We have created BIO-SPEAD (pronounced speed), a BIOlogical Signal Processing Environment for Algorithm Development. BIO-SPEAD is designed to accelerate development of complex algorithms which integrate information derived from single or multiple physiologic waveforms. BIO-SPEAD currently performs all of the basic analyses of several arterial blood pressure waveforms, and allows the user to utilize the results of those low-level analyses for development of more complex algorithms. We utilized a parallel programming architecture called the Process Trellis which keeps the different tasks, or processes, within BIO-SPEAD independent of each other. Additionally, we have developed a graphics interface to enable the user to visualize the waveform under analysis, the low-level system analysis, and the internal workings of the algorithm under development. The system has been used for several algorithm development projects and has demonstrated its utility.

Algorithms↗

PubMed1992

Development and validation of a logistic regression-derived algorithm for estimating the incremental probability of coronary artery disease before and after exercise testing.

OBJECTIVES: Our goals were to develop and validate a multivariate algorithm for estimating the incremental probability of the presence of coronary artery disease. BACKGROUND: Multivariate methods, including logistic regression analysis, have been extensively applied to diagnostic exercise testing. However, few previous studies have included both an incremental design and external validation. METHODS: A retrospective collection of clinical, exercise test and catheterization data was performed involving four U.S. referral medical centers. All patients had no prior history of coronary disease and had undergone coronary angiography < or = 3 months after exercise stress testing. An algorithm was developed in one center (590 patients with a 41% prevalence of coronary artery disease) with the use of logistic regression analysis and was validated in the other three centers (1,234 patients, 70% prevalence). The algorithm incorporated pretest variables (age, gender, symptoms, diabetes, cholesterol), exercise electrocardiographic (ECG) variables (mm of ST segment depression, ST slope, peak heart rate, metabolic equivalents [METs], exercise angina) and one thallium variable. Discrimination was measured with receiver operating characteristic curve analysis. Calibration (that is, reliability) was assessed from a comparison of probability estimates and the actual prevalence of disease. RESULTS: The overall incremental receiver operating characteristic curve areas for the validation group were pretest, -0.738 +/- 0.016; postexercise ECG, 0.78 (SE 0.017); and postthallium, 0.82 (SE 0.016); p < 0.01 for both increments. Within the three validation institutions, the institution with a disease prevalence closest to that of the derivation institution had the best incremental receiver operating characteristic curve areas. There was a stepwise incremental improvement in calibration especially from exercise ECG to thallium testing. CONCLUSIONS: An incremental multivariate algorithm derived in one center reliably estimated disease probability in patients from three other centers. The incremental value of testing was best demonstrated when the derivation and validation groups had a similar disease prevalence. This algorithm may be useful in decision making that relates to the diagnosis of coronary disease.

Algorithms↗

PubMed1992

Slope filtered pointwise correlation dimension algorithm and its evaluation with prefibrillation heart rate data.

Various studies have shown that a low variability in heart rate is associated with increased risk of ventricular fibrillation. Low chaotic (correlation) dimension in the heart rate also appears to predict fibrillation risk. However, these results have been based on intergroup comparisons and have not been found useful for predicting when a patient may fibrillate with any degree of sensitivity, specificity, or temporal accuracy. There are two primary limitations in using dimensional analysis to predict imminent fibrillation. The first is that the standard algorithms (for correlation dimension) assume stationarity of the system. The second limitation is that these algorithms require 10,000-50,000 data points to achieve good accuracy. Thus, even if stationarity were not an issue, there would be a lag of 2.4-12 hours to warn of impending fibrillation. An algorithm has been developed to calculate an accurate pointwise correlation dimension of heart rate data. The slope filtered pointwise correlation dimension algorithm requires as few as 1,000 points of data. Using this algorithm, it was found that the correlation dimension dropped from 2.50 +/- 0.81 to 1.07 +/- 0.18 in the minute before fibrillation in conscious pigs with an occluded coronary artery. In clinical studies, Holter tapes from patients that had suffered fatal fibrillation were also analyzed along with healthy controls and nonfibrillation ventricular patients. The fibrillation patients all had excursions of low dimension (less than 1.5), while the majority of the others did not. In the minutes before fibrillation, the correlation dimension dropped to a steady range of 0.8-1.3. Drops in the slope filtered pointwise correlation dimension appear to predict fibrillation in animals and patients.

Algorithms↗

PubMedDate not supplied

A second-generation computer-based edge detection algorithm for short-axis, two-dimensional echocardiographic images: accuracy and improvement in interobserver variability.

The present study tested the hypothesis that a second-generation endocardial edge detection algorithm that used a priori endocardial and epicardial information would improve accuracy and reduce the variability of border definition. Five nonexpert observers utilized the version 2 algorithm on 20 cycles of two-dimensional short-axis images (five excellent, seven good, and eight poor quality studies stored digitally from a previously reported project). Manually defined areas by five recognized experts on these 20 cardiac cycles were considered to be "true areas." Areas defined by the experts with version 1 of the algorithm were also used for comparison. Regression of the version 2 areas with mean, manually defined excellent quality areas yielded a similar correlation (r = 0.985) to that reported between the manual and the version 1 areas (r = 0.986). For all 20 cycles in the series, however, the correlation between version 2 and the manually defined areas was lower (r = 0.952) than that of the same correlation with version 1 areas (r = 0.980). For all studies the interobserver variability (percent area difference) was +/- 14.4% for manually defined borders, +/- 11.1% for version 1-defined borders, and +/- 7.7% for version 2-defined borders. No difference in variability was observed for excellent quality studies (+/- 5.3% versus 5.2%) between version 1 and version 2 areas. However, the version 2 algorithm significantly reduced interobserver variability for good and poor quality studies (+/- 8.4% to 7.6%, p less than 0.025, and 16.3% to 9.1%, p less than 0.05, respectively). We concluded that: the version 2 algorithm provided accuracy and significantly reduced the variability of area measurement in good and poor quality studies and that epicardial information was important to the improvement by providing wall thickness information to assist in filling areas of dropout and avoidance of intracavitary structures.

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PubMed1991

An algorithm for the DNA sequence generation from k-tuple word contents of the minimal number of random fragments.

An algorithm is described for generation of the long sequence written in a four letter alphabet from the constituent k-tuple words in the minimal number of separate, randomly defined fragments of the starting sequence. It is primarily intended for use in sequencing by hybridization (SBH) process- a potential method for sequencing human genome DNA (Drmanac et al., Genomics 4, pp. 114-128, 1989). The algorithm is based on the formerly defined rules and informative entities of the linear sequence. The algorithm requires neither knowledge on the number of appearances of a given k-tuple in sequence fragments, nor the information on which k-tuple words are on the ends of a fragment. It operates with the mixed content of k-tuples of the various lengths. The concept of the algorithm enables operations with the k-tuple sets containing false positive and false negative k-tuples. The content of the false k-tuples primarily affects the completeness of the generated sequence, and its correctness in the specific cases only. The algorithm can be used for the optimization of SBH parameters in the simulation experiments, as well as for the sequence generation in the real SBH experiments on the genomic DNA.

Algorithms↗

PubMed1992

Evaluation of a 3D reconstruction algorithm for multi-slice PET scanners.

A fully 3D reconstruction algorithm based on filtered backprojection was evaluated for the reconstruction of data obtained with multi-slice positron emission tomography (PET) scanners which have had the septa removed. This algorithm uses forward-projection through the reconstructed images of a 2D subset of the data to complete the 3D dataset thus satisfying the condition of shift invariance. This is followed by 3D filtered backprojection. Axial sampling was doubled by combining adjacent polar angles, thus improving reconstructed axial resolution. The algorithm was tested using real and simulated datasets and gave high quality reconstructions without artifacts over a wide range of imaging conditions. Events are placed accurately throughout the imaging volume as determined by measurements with a MRI/PET registration phantom. The forward-projection step leads to degradation in image resolution due to insufficient axial and transaxial sampling. This effect is amplified if multiple iterations of the algorithm are used, with little decrease in image noise. Changing the filter employed in the initial 2D reconstruction can be used to alter the noise and resolution characteristics of the 3D images. This algorithm has proved very robust at reconstructing 3D PET data and is relatively fast. Those small problems which exist can be attributed to detector sampling problems, especially in the axial direction, which is a consequence of the geometry of these scanners, which are designed primarily for 2D data acquisition.

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PubMed1989

Development and evaluation of spectral classification algorithms for fluorescence guided laser angioplasty.

Laser angioplasty, or the ablation of atherosclerotic plaque using laser energy, has tremendous potential to expand the scope of nonsurgical treatment of obstructive vascular disease. Clinical laser angioplasty, however, has been hindered by an unacceptable risk of vessel perforation. Laser-induced fluorescence spectroscopy can discriminate atherosclerotic from normal artery and may therefore be capable of guiding selective plaque ablation. To assess the feasibility of utilizing spectral information to discriminate arterial tissue type, several classification algorithms were developed and evaluated. Arterial fluorescence spectra from 350 to 700 nm were obtained from 100 human aortic specimens. Seven spectral classification algorithms were developed with the following techniques: multivariate linear regression, stepwise multivariate linear regression, principal components analysis, decision plane analysis, Bayes decision theory, principal peak ratio, and spectral width. The classification ability of each algorithm was evaluated by its application to the training set and to a validation set containing 82 additional spectra. All seven spectral classification algorithms prospectively classified atherosclerotic and normal aorta with an accuracy greater than 80 percent (range: 82-96 percent). Laser angioplasty systems incorporating spectral classification algorithms may therefore be capable of detection and selective ablation of atherosclerotic plaque.

Algorithms↗

PubMed1991

A fast and simple algorithm for the calculation of convective heat transfer by large vessels in three-dimensional inhomogeneous tissues.

A fast and simple algorithm has been presented for the calculation of time-dependent temperature distributions in inhomogeneous vascularized tissue. Three-dimensional anatomical data of tissues and vessel structures are decomposed into elementary cubic nodes by a special digitizing routine with vessels represented by connected strings of vessel nodes. Vessel cross sections may be irregular shaped and/or tapered. Conductive and convective heat transfer was calculated through use of the heat balance technique on each cubic node resulting in an explicit finite difference computational scheme. Employing a three time level scheme, the Fourier stability criterion is circumvented allowing arbitrary time steps to be defined in the algorithm. Time steps as large as 100 times the Fourier restricted one still result in stable and convergent calculations of the stationary temperature distribution. Vessels with different flows and diameters are incorporated by performing a vessel specific second discretization step in time. Using the new algorithm as a mathematical tool the thermal equilibration length of vessel segments have been established under a broad range of geometrical and flow conditions. Validation followed from comparing transient and stationary temperature distributions derived by the proposed algorithm to those from an accurate cylindrical numerical model. Predicted values for the thermal equilibration lengths are compared to an analytical expression and phantom experiments. The algorithm is incorporated in a thermal model being the main part of our hyperthermia treatment planning system.

Algorithms↗

PubMed1992

A new pacemaker algorithm for continuous capture verification and automatic threshold determination: elimination of pacemaker afterpotential utilizing a triphasic charge balancing system.

A new pacemaker algorithm designed to automatically verify pacemaker capture and determine pacing threshold by detection of a stimulus evoked potential was studied in 20 patients undergoing permanent pacemaker implantation. To eliminate pacing stimulus afterpotential and detect an evoked response, a hardware feedback circuit and a software template matching algorithm were used to produce a triphasic charge-balanced pacing pulse. After charge balancing the pacing lead, a residual artifact is measured. A capture window is defined as the area integral of the first 24 msec of the evoked depolarization, and a capture threshold as one third the amplitude of the capture window. The maximum allowable residual artifact is one eighth the amplitude of the capture window. Once the stimulus afterpotential is eliminated and the evoked response detected, capture threshold is automatically and continuously determined and the algorithm adds a 0.8-V safety margin to the pacemaker output. This algorithm was run automatically and after simulated loss of capture, produced by manually decreasing pacer output below threshold, in the bipolar (13 patients) and unipolar (20 patients) pacing modes. In each patient loss of capture was immediately detected. The data were consistent (P = NS) between algorithm runs. During unipolar pacing the area integral of the first 24 msec of the evoked response was 412 +/- 137 versus 413 +/- 144 and the residual artifact 5.8 +/- 4.8 versus 8.1 +/- 7.5. The resulting ratio (signal/noise) of the two parameters was 150 +/- 141 versus 145 +/- 181. Automatically determined threshold was 0.69 +/- 0.43 V versus 0.69 +/- 0.42.(ABSTRACT TRUNCATED AT 250 WORDS)

Aged↗

PubMedDate not supplied

Application of the EM algorithm to radiographic images.

The expectation maximization (EM) algorithm has received considerable attention in the area of positron emitted tomography (PET) as a restoration and reconstruction technique. In this paper, the restoration capabilities of the EM algorithm when applied to radiographic images is investigated. This application does not involve reconstruction. The performance of the EM algorithm is quantitatively evaluated using a "perceived" signal-to-noise ratio (SNR) as the image quality metric. This perceived SNR is based on statistical decision theory and includes both the observer's visual response function and a noise component internal to the eye-brain system. For a variety of processing parameters, the relative SNR (ratio of the processed SNR to the original SNR) is calculated and used as a metric to compare quantitatively the effects of the EM algorithm with two other image enhancement techniques: global contrast enhancement (windowing) and unsharp mask filtering. The results suggest that the EM algorithm's performance is superior when compared to unsharp mask filtering and global contrast enhancement for radiographic images which contain objects smaller than 4 mm.

Algorithms↗

PubMed1989

A medical algorithm for detecting physical disease in psychiatric patients.

An algorithm for screening psychiatric patients for physical disease was empirically derived from a comprehensive assessment of 509 patients in California's mental health system. The first 343 patients were used to develop the algorithm, and the remaining 166 were used as a test group. Calculations were made for several versions of the algorithm, and the data were compared with the diagnoses listed in the patients' admission mental health record. The algorithmic procedure was more accurate and more cost-effective than the medical evaluation procedures used by the state mental health system. When applied to the test group, the algorithm detected up to 90 percent of patients who had an active, important physical disease at a cost of $156 per patient. The mental health system had detected 58 percent of test-group patients with a disease at a cost of $230 per patient.

Algorithms↗

PubMed1987

Algorithmic diagnosis of jaundice.

Extensive clinical and clinical chemical information was collected from 1002 jaundiced patients. By applying Bayes' theorem and logistic discriminant analysis, a diagnostic algorithm was developed based upon 21 of the 107 variables collected. This algorithm permitted a probabilistic classification of jaundiced patients into four diagnostic categories: acute non-obstructive, chronic non-obstructive, benign obstructive and malignant obstructive jaundice. Of the 985 patients with a final diagnosis a correct probabilistic diagnosis (obstruction vs. non-obstruction) was suggested by the algorithm in 867 patients (88%). Adopting a probability limit of 0.80, 683 patients (69%) were correctly classified, 34 patients (3.5%) were wrongly so, and 268 patients (27%) could not be classified with a probability above 0.80 (doubtful cases). The algorithm was also tested in a further series of 110 jaundiced patients and found to perform equally well: 88 patients classified, 22 patients remaining doubtful. Patients with doubtful diagnoses should be referred to a non-invasive test such as ultrasound examination, whereas patients with definite diagnoses can be referred to invasive tests (liver biopsy, direct cholangiography) as appropriate. The diagnostic algorithm seems to be a valuable aid for the preliminary differential diagnosis of the jaundiced patient and can be used in the planning of a diagnostic strategy for the individual patient.

Algorithms↗

PubMed1992

[Calculation algorithm of three-dimensional absorbed dose distribution due to in vivo administration of nuclides for radiotherapy].

In the in vivo administration of radionuclides for radiotherapy including radioimmunotherapy, an algorithm is proposed for the purpose of calculating three-dimensional absorbed dose distributions of tumors and adjacent tissues, and neighboring organs. The absorbed dose distribution due to the algorithm is given by convolution of the three-dimensional dose matrix for a unit cubic voxel containing unit cumulated activity, with the three-dimensional matrix of the cumulated activity distribution given by the same voxel size above. The dose calculation algorithm does not depend upon the source size, the source shape, and the nonuniform and irregular activity distribution. In addition, it can exceedingly decrease computation time compared to other calculation algorithms. Computer simulations were performed using the MIRD thyroid phantom for 32P, 90Y, 131I, 186Re, and 188Re that appear promising for radioimmunotherapy, and their results verified the validity of the proposed calculation algorithm and high accuracy of the calculations.

Algorithms↗

PubMed1992

Computer-assisted analysis of mixtures (C.A.MAM): statistical algorithms.

This paper presents various algorithmic approaches for computing the maximum likelihood estimator of the mixing distribution of a one-parameter family of densities and provides a unifying computer-oriented concept for the statistical analysis of unobserved heterogeneity (i.e., observations stemming from different subpopulations) in a univariate sample. The case with unknown number of population subgroups as well as the case with known number of population subgroups, with emphasis on the first, is considered in the computer package C.A.MAN (Computer Assisted Mixture Analysis). It includes an algorithmic menu with choices of the EM algorithm, the vertex exchange algorithm, a combination of both, as well as the vertex direction method. To ensure reliable convergence, a step-length menu is provided for the three latter methods, each achieving monotonicity for the direction of choice. C.A.MAN has the option to work with restricted support size-that is, the case when the number of components is known a priori. In the latter case, the EM algorithm is used. Applications of mixture modelling in medical problems are discussed.

Algorithms↗

PubMed1991

Clinical evaluation of an algorithm for the interpretation of hyperamylasemia.

Total amylase concentration in serum continues to be widely determined in the diagnosis of acute pancreatic disease. Accumulated experience has made clear, however, that this determination has distinct limitations. Consequently, the knowledge of the origin of hyperamylasemia may have an important influence on treatment, hospitalization, and extent of clinical investigations. We undertook a logical and systematic approach to the interpretation of hyperamylasemia through the use of an algorithm that can be applied in clinical situations without the need for the integration of radiologic procedures or clinical data. The proposed algorithm was tested for effectiveness in 97 consecutive hospitalized patients with hyperamylasemia (amylase level greater than twice the upper reference limit) for a 2-year period. The majority (52.5%) of these patients had acute pancreatitis. The algorithm assigned the correct diagnostic categories in 95.8% of cases, with a disagreement between patient diagnosis and algorithm-generated diagnosis in only four cases. These four patients (two with acute biliary disease, one with bacterial peritonitis, and one with chronic renal failure) had pancreatic lipase values greater than five times the upper reference limit, so that the algorithm classified their condition as acute pancreatitis. The clinical trial indicated that the proposed decision tree, which requires only knowledge of biochemical data that are readily available, is useful in the evaluation of elevated amylase activity and facilitates arrival at a definitive diagnosis.

Acute Disease↗