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PubMed2006

Seizure detection algorithm for neonates based on wave-sequence analysis.

OBJECTIVE: The description and evaluation of the performance of a new real-time seizure detection algorithm in the newborn infant. METHODS: The algorithm includes parallel fragmentation of EEG signal into waves; wave-feature extraction and averaging; elementary, preliminary and final detection. The algorithm detects EEG waves with heightened regularity, using wave intervals, amplitudes and shapes. The performance of the algorithm was assessed with the use of event-based and liberal and conservative time-based approaches and compared with the performance of Gotman's and Liu's algorithms. RESULTS: The algorithm was assessed on multi-channel EEG records of 55 neonates including 17 with seizures. The algorithm showed sensitivities ranging 83-95% with positive predictive values (PPV) 48-77%. There were 2.0 false positive detections per hour. In comparison, Gotman's algorithm (with 30s gap-closing procedure) displayed sensitivities of 45-88% and PPV 29-56%; with 7.4 false positives per hour and Liu's algorithm displayed sensitivities of 96-99%, and PPV 10-25%; with 15.7 false positives per hour. CONCLUSIONS: The wave-sequence analysis based algorithm displayed higher sensitivity, higher PPV and a substantially lower level of false positives than two previously published algorithms. SIGNIFICANCE: The proposed algorithm provides a basis for major improvements in neonatal seizure detection and monitoring.

Algorithms↗

PubMed2006

Comparative assessment of statistical brain MR image segmentation algorithms and their impact on partial volume correction in PET.

Magnetic resonance imaging (MRI)-guided partial volume effect correction (PVC) in brain positron emission tomography (PET) is now a well-established approach to compensate the large bias in the estimate of regional radioactivity concentration, especially for small structures. The accuracy of the algorithms developed so far is, however, largely dependent on the performance of segmentation methods partitioning MRI brain data into its main classes, namely gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). A comparative evaluation of three brain MRI segmentation algorithms using simulated and clinical brain MR data was performed, and subsequently their impact on PVC in 18F-FDG and 18F-DOPA brain PET imaging was assessed. Two algorithms, the first is bundled in the Statistical Parametric Mapping (SPM2) package while the other is the Expectation Maximization Segmentation (EMS) algorithm, incorporate a priori probability images derived from MR images of a large number of subjects. The third, here referred to as the HBSA algorithm, is a histogram-based segmentation algorithm incorporating an Expectation Maximization approach to model a four-Gaussian mixture for both global and local histograms. Simulated under different combinations of noise and intensity non-uniformity, MR brain phantoms with known true volumes for the different brain classes were generated. The algorithms' performance was checked by calculating the kappa index assessing similarities with the "ground truth" as well as multiclass type I and type II errors including misclassification rates. The impact of image segmentation algorithms on PVC was then quantified using clinical data. The segmented tissues of patients' brain MRI were given as input to the region of interest (RoI)-based geometric transfer matrix (GTM) PVC algorithm, and quantitative comparisons were made. The results of digital MRI phantom studies suggest that the use of HBSA produces the best performance for WM classification. For GM classification, it is suggested to use the EMS. Segmentation performed on clinical MRI data show quite substantial differences, especially when lesions are present. For the particular case of PVC, SPM2 and EMS algorithms show very similar results and may be used interchangeably. The use of HBSA is not recommended for PVC. The partial volume corrected activities in some regions of the brain show quite large relative differences when performing paired analysis on 2 algorithms, implying a careful choice of the segmentation algorithm for GTM-based PVC.

Algorithms↗

PubMed1999

Use of sexually transmitted disease risk assessment algorithms for selection of intrauterine device candidates.

Sexually transmitted diseases (STD) are an important contraindication for intrauterine device (IUD) insertion. Nevertheless, laboratory testing for STD is not possible in many settings. The objective of this study is to evaluate the use of risk assessment algorithms to predict STD and subsequent IUD-related complications among IUD candidates. Among 615 IUD users in Kenya, the following algorithms were evaluated: 1) an STD algorithm based on US Agency for International Development (USAID) Technical Working Group guidelines: 2) a Centers for Disease Control and Prevention (CDC) algorithm for management of chlamydia; and 3) a data-derived algorithm modeled from study data. Algorithms were evaluated for prediction of chlamydial and gonococcal infection at 1 month and complications (pelvic inflammatory disease [PID], IUD removals, and IUD expulsions) over 4 months. Women with STD were more likely to develop complications than women without STD (19% vs 6%; risk ratio = 2.9; 95% CI 1.3-6.5). For STD prediction, the USAID algorithm was 75% sensitive and 48% specific, with a positive likelihood ratio (LR+) of 1.4. The CDC algorithm was 44% sensitive and 72% specific, LR+ = 1.6. The data-derived algorithm was 91% sensitive and 56% specific, with LR+ = 2.0 and LR- = 0.2. Category-specific LR for this algorithm identified women with very low (< 1%) and very high (29%) infection probabilities. The data-derived algorithm was also the best predictor of IUD-related complications. These results suggest that use of STD algorithms may improve selection of IUD users. Women at high risk for STD could be counseled to avoid IUD, whereas women at moderate risk should be monitored closely and counseled to use condoms.

Adult↗

PubMed2003

Prospective grading of graft-versus-host disease after unrelated donor marrow transplantation: a grading algorithm versus blinded expert panel review.

In conjunction with a randomized trial of T-cell depletion versus conventional graft-versus-host disease (GVHD) prophylaxis, we assessed GVHD grading by comparing the transplant center 100-day score, a clinically calculated algorithm, and a blinded expert panel review (PR). Weekly skin, gut, and liver clinical staging; clinically verified differential diagnosis; biopsy information; cyclosporine levels; and initiation of treatment were reviewed and graded according to the consensus GVHD grading method modified by a prospectively determined grading algorithm that specified liver and gut downstaging if a differential diagnosis in that organ was identified. Transplant center (TC) determination of maximum grade was compared with the algorithm-calculated grade and the final expert PR. Of 404 patients reviewed, the TC grade concurred with the calculated algorithm grade in 72% (the algorithm upgraded 18% and downgraded 10%), whereas the TC grade agreed with the PR in 77% (the PR upgraded 12% and downgraded 11%). The calculated algorithm grade was nearly fully (92%) concordant with the final PR grade (the PR upgraded 0.7% and downgraded 7%). Blinded, duplicate reviews for quality control (n = 108) agreed with the initial review in 89% of cases. Algorithm and/or PR review reduced the TC-reported incidence of grade II (28% to 23%) and increased grade III (11% to 20%), whereas grade 0 (41% to 42%), grade I (13% to 12%), and grade IV (7% to 6%) were invariant. Recalculation of the algorithm grading without differential diagnosis downstaging reduced agreement with the TC to a small extent. The original algorithm changed 51 (13%) of 404 from grade 0 to II into grade III or IV or vice versa; calculation without the downgrade modified 44 cases (11%). Maximum acute GVHD grade had a major effect on 2-year disease-free survival, but assignment by TC, calculated algorithm, or final PR grade had little effect on survival within grades or grade categories 0 through II versus III or IV. We conclude that detailed and expert PR yields GVHD scoring that is internally consistent and reproducible with 89% concordance. Weekly recording of GVHD stage along with a calculated grading algorithm acknowledging differential diagnoses results in a final and maximum grade nearly fully concordant with the expert blinded PR. Multicenter prospective GVHD scoring using all available weekly staging and differential diagnosis data can be reliably assessed with a clinically relevant algorithm. This approach can thereby reduce investigator bias, facilitate comparison between centers, and perhaps eliminate the need for an expert PR. This technique should be used in future prospective studies of GVHD prophylaxis.

Adolescent↗

PubMed2024

Algorithms for the identification of prevalent diabetes in the All of Us Research Program validated using polygenic scores.

The All of Us Research Program (AoU) is an initiative designed to gather a comprehensive and diverse dataset from at least one million individuals across the USA. This longitudinal cohort study aims to advance research by providing a rich resource of genetic and phenotypic information, enabling powerful studies on the epidemiology and genetics of human diseases. One critical challenge to maximizing its use is the development of accurate algorithms that can efficiently and accurately identify well-defined disease and disease-free participants for case-control studies. This study aimed to develop and validate type 1 (T1D) and type 2 diabetes (T2D) algorithms in the AoU cohort, using electronic health record (EHR) and survey data. Building on existing algorithms and using diagnosis codes, medications, laboratory results, and survey data, we developed and implemented algorithms for identifying prevalent cases of type 1 and type 2 diabetes. The first set of algorithms used only EHR data (EHR-only), and the second set used a combination of EHR and survey data (EHR+). A universal algorithm was also developed to identify individuals without diabetes. The performance of each algorithm was evaluated by testing its association with polygenic scores (PSs) for type 1 and type 2 diabetes. We demonstrated the feasibility and utility of using AoU EHR and survey data to employ diabetes algorithms. For T1D, the EHR-only algorithm showed a stronger association with T1D-PS compared to the EHR + algorithm (DeLong p-value = 3 × 10-5). For T2D, the EHR + algorithm outperformed both the EHR-only and the existing T2D definition provided in the AoU Phenotyping Library (DeLong p-values = 0.03 and 1 × 10-4, respectively), identifying 25.79% and 22.57% more cases, respectively, and providing an improved association with T2D PS. We provide a new validated type 1 diabetes definition and an improved type 2 diabetes definition in AoU, which are freely available for diabetes research in the AoU. These algorithms ensure consistency of diabetes definitions in the cohort, facilitating high-quality diabetes research.

Humans↗

PubMed2005

Analytical fan-beam and cone-beam reconstruction algorithms with uniform attenuation correction for SPECT.

In this paper, we developed an analytical fan-beam reconstruction algorithm that compensates for uniform attenuation in SPECT. The new fan-beam algorithm is in the form of backprojection first, then filtering, and is mathematically exact. The algorithm is based on three components. The first one is the established generalized central-slice theorem, which relates the 1D Fourier transform of a set of arbitrary data and the 2D Fourier transform of the backprojected image. The second one is the fact that the backprojection of the fan-beam measurements is identical to the backprojection of the parallel measurements of the same object with the same attenuator. The third one is the stable analytical reconstruction algorithm for uniformly attenuated Radon data, developed by Metz and Pan. The fan-beam algorithm is then extended into a cone-beam reconstruction algorithm, where the orbit of the focal point of the cone-beam imaging geometry is a circle. This orbit geometry does not satisfy Tuy's condition and the obtained cone-beam algorithm is an approximation. In the cone-beam algorithm, the cone-beam data are first backprojected into the 3D image volume; then a slice-by-slice filtering is performed. This slice-by-slice filtering procedure is identical to that of the fan-beam algorithm. Both the fan-beam and cone-beam algorithms are efficient, and computer simulations are presented. The new cone-beam algorithm is compared with Bronnikov's cone-beam algorithm, and it is shown to have better performance with noisy projections.

Algorithms↗

PubMed1995

Physical mapping by STS hybridization: algorithmic strategies and the challenge of software evaluation.

An important tool in the analysis of genomic sequences is the physical map. In this paper we examine the construction of physical maps from hybridization data between sequence tag sites (STS) probes and clones of genomic fragments. An algorithmic theory of the mapping process, a proposed performance evaluation procedure, and several new algorithmic strategies for mapping are given. A unifying theme for these developments is the idea of a "conservative extension." An algorithm, measure of algorithm quality, or description of physical map is a conservative extension if it is a generalization for data with errors of a corresponding concept in the error-free case. In our algorithmic theory we show that the nature of hybridization experiments imposes inherent limitations on the mapping information recorded in the experimental data. We prove that only certain types of mapping information can be reliably calculated by any algorithm. A test generator is then presented along with quantitative measures for determining how much of the possible information is being computed by a given algorithm. Weaknesses and strengths of these measures are discussed. Each of the new algorithms presented in this paper is based on combinatorial optimizations. Despite the fact that all the optimizations are NP-complete, we have developed algorithmic tools for the design of competitive approximation algorithms. We apply our performance evaluation program to our algorithms and obtain solid evidence that the algorithms are capable of retrieving high-level reliable mapping information.

Algorithms↗

PubMed1999

Evaluation of HIV serial and parallel serologic testing algorithms in Abidjan, Côte d'Ivoire.

OBJECTIVE: To evaluate HIV serologic testing algorithms based on a combination of three enzyme linked immunosorbent assays (ELISA) for the confirmation of HIV infection in Abidjan, Côte d'Ivoire, where HIV-2 and HIV-1 non-B subtypes are prevalent. METHODS: A total of 1069 human sera with known serologic status, in addition to a seroconversion and low titer antibody panel were initially tested by six ELISA to determine the sensitivity, specificity and delta values of the assays. On the basis of the performance of the assays, three ELISA (Enzygnost, ICE 1.0.2, and Vironostika) were selected for use in a parallel and serial testing algorithm in analyzing 8283 consecutively collected sera. In the parallel testing algorithm, sera concordantly reactive or non-reactive by Enzygnost and ICE 1.0.2 were considered as true positive or true negative, respectively. In the serial algorithm, sera reactive by Enzygnost were retested by ICE 1.0.2. Sera with discordant results were tested by Vironostika, and the results was considered definitive. All reactive sera, plus a random sample of negative sera were tested for confirmation by Peptilav. In addition, a random sample of reactive sera was tested by Western blot. RESULTS: All ELISA had 100% sensitivity; specificities ranged from 96.8 to 100%. Positive and negative delta values of the ELISA were high (range, 6.89 to 46.07 and -2.05 to -5.75, respectively). Of the 8283 sera, 2054 were considered true positives and were correctly classified by the parallel testing algorithm (sensitivity, 100%). Of the 6229 true negative sera, 6226 were negative by the parallel testing algorithm (specificity, 99.95%). The sensitivity of the serial algorithm was 99.96%, and specificity was 99.95%. None of the 250 concordant ELISA-negative sera in the algorithm that were randomly tested in Peptilav was positive; similarly, all of the 103 concordant ELISA-positive sera were confirmed by Western blot. The three-ELISA algorithm resulted in reagent cost-savings of at least 50% compared with the Peptilav-based algorithm. CONCLUSION: These results suggest that a combination of ELISA using different principles or antigens in a serial or parallel algorithm is an efficient and cost-effective alternative to the standard algorithm in areas where HIV-1 and HIV-2 are prevalent.

Algorithms↗

PubMed2005

Simulated annealing algorithm for the multiple sequence alignment problem: the approach of polymers in a random medium.

We propose a probabilistic algorithm to solve the multiple sequence alignment problem. The algorithm is a simulated annealing that exploits the representation of the multiple alignment between D sequences as a directed polymer in D dimensions. Within this representation we can easily track the evolution of the alignment through local moves of low computational cost. In contrast with other probabilistic algorithms proposed to solve this problem, our approach allows the creation and deletion of gaps without extra computational cost. The algorithm was tested by aligning proteins from the kinase family. When D=3 the results are consistent with those obtained using a complete algorithm. For D>3 where the complete algorithm fails, we show that our algorithm still converges to reasonable alignments. We also study the space of solutions obtained and show that depending on the number of sequences aligned the solutions are organized in different ways, suggesting a possible source of errors for progressive algorithms. Finally, we test our algorithm in artificially generated sequences and prove that it may perform better than progressive algorithms. Moreover, in those cases in which a progressive algorithm works better, its solution may be taken as an initial condition of our algorithm and, again, we obtain alignments with lower scores and more relevant from the biological point of view.

Algorithms↗

PubMed2001

A new algorithm for linear and nonlinear ARMA model parameter estimation using affine geometry.

A linear and nonlinear autoregressive (AR) moving average (MA) (ARMA) identification algorithm is developed for modeling time series data. The new algorithm is based on the concepts of affine geometry in which the salient feature of the algorithm is to remove the linearly dependent ARMA vectors from the pool of candidate ARMA vectors. For noiseless time series data with a priori incorrect model-order selection, computer simulations show that accurate linear and nonlinear ARMA model parameters can be obtained with the new algorithm. Many algorithms, including the fast orthogonal search (FOS) algorithm, are not able to obtain correct parameter estimates in every case, even with noiseless time series data, because their model-order search criteria are suboptimal. For data contaminated with noise, computer simulations show that the new algorithm performs better than the FOS algorithm for MA processes, and similarly to the FOS algorithm for ARMA processes. However, the computational time to obtain the parameter estimates with the new algorithm is faster than with FOS. Application of the new algorithm to experimentally obtained renal blood flow and pressure data show that the new algorithm is reliable in obtaining physiologically understandable transfer function relations between blood pressure and flow signals.

Algorithms↗

PubMed2005

Data classification with radial basis function networks based on a novel kernel density estimation algorithm.

This paper presents a novel learning algorithm for efficient construction of the radial basis function (RBF) networks that can deliver the same level of accuracy as the support vector machines (SVMs) in data classification applications. The proposed learning algorithm works by constructing one RBF subnetwork to approximate the probability density function of each class of objects in the training data set. With respect to algorithm design, the main distinction of the proposed learning algorithm is the novel kernel density estimation algorithm that features an average time complexity of O(n log n), where n is the number of samples in the training data set. One important advantage of the proposed learning algorithm, in comparison with the SVM, is that the proposed learning algorithm generally takes far less time to construct a data classifier with an optimized parameter setting. This feature is of significance for many contemporary applications, in particular, for those applications in which new objects are continuously added into an already large database. Another desirable feature of the proposed learning algorithm is that the RBF networks constructed are capable of carrying out data classification with more than two classes of objects in one single run. In other words, unlike with the SVM, there is no need to resort to mechanisms such as one-against-one or one-against-all for handling datasets with more than two classes of objects. The comparison with SVM is of particular interest, because it has been shown in a number of recent studies that SVM generally are able to deliver higher classification accuracy than the other existing data classification algorithms. As the proposed learning algorithm is instance-based, the data reduction issue is also addressed in this paper. One interesting observation in this regard is that, for all three data sets used in data reduction experiments, the number of training samples remaining after a naive data reduction mechanism is applied is quite close to the number of support vectors identified by the SVM software. This paper also compares the performance of the RBF networks constructed with the proposed learning algorithm and those constructed with a conventional cluster-based learning algorithm. The most interesting observation learned is that, with respect to data classification, the distributions of training samples near the boundaries between different classes of objects carry more crucial information than the distributions of samples in the inner parts of the clusters.

Algorithms↗

PubMed2006

Parameter incremental learning algorithm for neural networks.

In this paper, a novel stochastic (or online) training algorithm for neural networks, named parameter incremental learning (PIL) algorithm, is proposed and developed. The main idea of the PIL strategy is that the learning algorithm should not only adapt to the newly presented input-output training pattern by adjusting parameters, but also preserve the prior results. A general PIL algorithm for feedforward neural networks is accordingly presented as the first-order approximate solution to an optimization problem, where the performance index is the combination of proper measures of preservation and adaptation. The PIL algorithms for the multilayer perceptron (MLP) are subsequently derived. Numerical studies show that for all the three benchmark problems used in this paper the PIL algorithm for MLP is measurably superior to the standard online backpropagation (BP) algorithm and the stochastic diagonal Levenberg-Marquardt (SDLM) algorithm in terms of the convergence speed and accuracy. Other appealing features of the PIL algorithm are that it is computationally as simple as the BP algorithm, and as easy to use as the BP algorithm. It, therefore, can be applied, with better performance, to any situations where the standard online BP algorithm is applicable.

Algorithms↗

PubMed2002

Evaluation of command algorithms for control of upper-extremity neural prostheses.

Five new command control algorithms were created to enable increased control over grasp force in upper-extremity neural prostheses. Most of these algorithms took advantage of the ability to lock or assign a steady command value to the hand neural prosthesis. Five able-bodied subjects tested the algorithms by using a shoulder controller that controlled a video-simulated hand to repeatedly complete a consistent evaluation task. A generalized estimating equations-based linear model was used to analyze the data. The algorithms were ranked via contrast analyses between the coefficient values from the linear model of the proportional control with lock algorithm, which is the algorithm presently used in neural prostheses, and each of the other algorithms. The algorithms that allowed adjustment of the command value after the hand was locked as well as algorithms that allowed a decrease in controller gain after the hand was locked performed better than the proportional control with lock algorithm. Algorithms that changed command as a function of time performed worse than the proportional control with lock algorithm. Further, the computer-based video simulator proved to be useful as a first-pass evaluation tool for neural prosthesis control.

Adult↗

PubMed2006

Fast voxel and polygon ray-tracing algorithms in intensity modulated radiation therapy treatment planning.

We present work on combining three algorithms to improve ray-tracing efficiency in radiation therapy dose computation. The three algorithms include: An improved point-in-polygon algorithm, incremental voxel ray tracing algorithm, and stereographic projection of beamlets for voxel truncation. The point-in-polygon and incremental voxel ray-tracing algorithms have been used in computer graphics and nuclear medicine applications while the stereographic projection algorithm was developed by our group. These algorithms demonstrate significant improvements over the current standard algorithms in peer reviewed literature, i.e., the polygon and voxel ray-tracing algorithms of Siddon for voxel classification (point-in-polygon testing) and dose computation, respectively, and radius testing for voxel truncation. The presented polygon ray-tracing technique was tested on 10 intensity modulated radiation therapy (IMRT) treatment planning cases that required the classification of between 0.58 and 2.0 million voxels on a 2.5 mm isotropic dose grid into 1-4 targets and 5-14 structures represented as extruded polygons (a.k.a. Siddon prisms). Incremental voxel ray tracing and voxel truncation employing virtual stereographic projection was tested on the same IMRT treatment planning cases where voxel dose was required for 230-2400 beamlets using a finite-size pencil-beam algorithm. Between a 100 and 360 fold cpu time improvement over Siddon's method was observed for the polygon ray-tracing algorithm to perform classification of voxels for target and structure membership. Between a 2.6 and 3.1 fold reduction in cpu time over current algorithms was found for the implementation of incremental ray tracing. Additionally, voxel truncation via stereographic projection was observed to be 11-25 times faster than the radial-testing beamlet extent approach and was further improved 1.7-2.0 fold through point-classification using the method of translation over the cross product technique.

Algorithms↗

PubMed2006

Methods for evaluating clustering algorithms for gene expression data using a reference set of functional classes.

BACKGROUND: A cluster analysis is the most commonly performed procedure (often regarded as a first step) on a set of gene expression profiles. In most cases, a post hoc analysis is done to see if the genes in the same clusters can be functionally correlated. While past successes of such analyses have often been reported in a number of microarray studies (most of which used the standard hierarchical clustering, UPGMA, with one minus the Pearson's correlation coefficient as a measure of dissimilarity), often times such groupings could be misleading. More importantly, a systematic evaluation of the entire set of clusters produced by such unsupervised procedures is necessary since they also contain genes that are seemingly unrelated or may have more than one common function. Here we quantify the performance of a given unsupervised clustering algorithm applied to a given microarray study in terms of its ability to produce biologically meaningful clusters using a reference set of functional classes. Such a reference set may come from prior biological knowledge specific to a microarray study or may be formed using the growing databases of gene ontologies (GO) for the annotated genes of the relevant species. RESULTS: In this paper, we introduce two performance measures for evaluating the results of a clustering algorithm in its ability to produce biologically meaningful clusters. The first measure is a biological homogeneity index (BHI). As the name suggests, it is a measure of how biologically homogeneous the clusters are. This can be used to quantify the performance of a given clustering algorithm such as UPGMA in grouping genes for a particular data set and also for comparing the performance of a number of competing clustering algorithms applied to the same data set. The second performance measure is called a biological stability index (BSI). For a given clustering algorithm and an expression data set, it measures the consistency of the clustering algorithm's ability to produce biologically meaningful clusters when applied repeatedly to similar data sets. A good clustering algorithm should have high BHI and moderate to high BSI. We evaluated the performance of ten well known clustering algorithms on two gene expression data sets and identified the optimal algorithm in each case. The first data set deals with SAGE profiles of differentially expressed tags between normal and ductal carcinoma in situ samples of breast cancer patients. The second data set contains the expression profiles over time of positively expressed genes (ORF's) during sporulation of budding yeast. Two separate choices of the functional classes were used for this data set and the results were compared for consistency. CONCLUSION: Functional information of annotated genes available from various GO databases mined using ontology tools can be used to systematically judge the results of an unsupervised clustering algorithm as applied to a gene expression data set in clustering genes. This information could be used to select the right algorithm from a class of clustering algorithms for the given data set.

Algorithms↗

PubMed1989

High-spatial-frequency (bone) algorithm improves quality of standard CT of the thorax.

The high-spatial-frequency (bone) reconstruction algorithm has been shown to be superior to the standard algorithm in the assessment of thin-section images of the lung parenchyma. We compared the high-spatial-frequency and standard algorithms in the evaluation of normal and abnormal lung parenchyma and mediastinum on conventional 10-mm-collimation CT scans. Three observers, blinded to the reconstruction algorithm used, compared both algorithms on identical images in 31 patients with a variety of diseases. Lung parenchymal detail was improved with the bone algorithm in 94% of scans (p less than .001). The definition of mediastinal contents with the bone algorithm was judged equal or superior to the standard algorithm in 94% of images (p less than .001). Scanning of a line-pair-resolution phantom demonstrated a 28% improvement in image resolution with the bone algorithm. Although the bone algorithm resulted in an increase in visible noise, the overall visual quality of the images was considered equal to or greater than that of those reconstructed on the standard algorithm in 100% of parenchymal (p less than .001) and 85% of mediastinal (p less than .001) images. We conclude that routine use of the bone algorithm results in improved spatial resolution and definition of pulmonary parenchymal detail without appreciably degrading the overall visual quality of mediastinal images.

Algorithms↗

PubMed1999

The Texas Medication Algorithm Project: report of the Texas Consensus Conference Panel on Medication Treatment of Major Depressive Disorder.

BACKGROUND: This article describes the development of consensus medication algorithms for the treatment of patients with major depressive disorder in the Texas public mental health system. To the best of our knowledge, the Texas Medication Algorithm Project (TMAP) is the first attempt to develop and prospectively evaluate consensus-based medication algorithms for the treatment of individuals with severe and persistent mental illnesses. The goals of the algorithm project are to increase the consistency of appropriate treatment of major depressive disorder and to improve clinical outcomes of patients with the disorder. METHOD: A consensus conference composed of academic clinicians and researchers, practicing clinicians, administrators, consumers, and families was convened to develop evidence-based consensus algorithms for the pharmacotherapy of major depressive disorder in the Texas mental health system. After a series of presentations and panel discussions, the consensus panel met and drafted the algorithms. RESULTS: The panel consensually agreed on algorithms developed for both nonpsychotic and psychotic depression. The algorithms consist of systematic strategies to define appropriate treatment interventions and tactics to assure optimal implementation of the strategies. Subsequent to the consensus process, the algorithms were further modified and expanded iteratively to facilitate implementation on a local basis. CONCLUSION: These algorithms serve as the initial foundation for the development and implementation of medication treatment algorithms for patients treated in public mental health systems. Specific issues related to adaptation, implementation, feasibility testing, and evaluation of outcomes with the pharmacotherapeutic algorithms will be described in future articles.

Algorithms↗

PubMed1999

[Clinical algorithms in the treatment of status epilepticus in children].

The clinical algorithm is a text format that is specially suited for presenting a sequence of clinical decisions, for teaching clinical decision making, and for guiding patient care. Clinical algorithms are compared as to their clinical usefulness with decision analysis. We have tried to make clinical algorithm for managing status epilepticus in children that can be applicable to our conditions. Most of the algorithms that are made on this subject include drugs and procedures that are not available at our hospital. We identified performance requirement, defined the set of problems to be solved as well as who would solve them, developed drafts in several versions and put them in the discussion with experts in this field. Algorithm was tested and revised and graphical acceptability was achieved. In the algorithm we tried to clearly define how the clinician should make the decision and to be provided with appropriate feedback. In one year period of experience in working we found this algorithm very useful in managing status epilepticus in children, as well as in teaching young doctors the specifities of algorithms and this specific issue. Their feedback is that they find that it provides the framework for facilitating thinking about clinical problems. Sometimes we hear objection that algorithms may not apply to a specific patient. This objection is based on misunderstanding how algorithms are used and should be corrected by a proper explanation of their use. We conclude that methods should be sought for writing clinical algorithms that represent expert consensus. A clinical algorithm can then be written for many areas of medical decision making that can be standardized. Medical practice would then be presented to students more effectively, accurately and understood better.

Algorithms↗