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Results for “Performance benchmarking”

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PubMed1998

Managed care and the delivery of primary care to the elderly and the chronically ill.

OBJECTIVE: To analyze primary care staffing in HMOs and to review the literature on primary care organization and performance in managed care organizations, with an emphasis on the delivery of primary care to the elderly and chronically ill. DATA SOURCES/STUDY SETTING: Analysis of primary care staffing: InterStudy HMO census data on primary care (n = 1,956) and specialist (n = 1,777) physician staffing levels from 1991 through 1995. Primary care organization and performance for the chronically ill and elderly were analyzed using a review of published research. STUDY DESIGN: For the staffing-level models, the number of primary care and specialist physicians per 100,000 enrollees was regressed on HMO characteristics (HMO type [group, staff, network, mixed], HMO enrollment, federal qualification, profit status, national affiliation) and community characteristics (per capita income, population density, service area size, HMO competition). For the review of organization and performance, literature published was summarized in a tabular format. PRINCIPAL FINDINGS: The analysis of physician staffing shows that group and staff HMOs have fewer primary care and specialist physicians per 100,000 enrollees than do network and mixed HMOs, which have fewer than IPAs. Larger HMOs use fewer physicians per 100,000 enrollees than smaller HMOs. Federally qualified HMOs have fewer primary care and specialist physicians per 100,000 enrollees. For-profit, nationally affiliated, and Blue Cross HMOs have more primary care and specialist physicians than do local HMOs. HMOs in areas with high per capita income have more PCPs per 100,000 and a greater proportion of PCPs in the panel. HMO penetration decreases the use of specialists, but the number of HMOs increases the use of primary care and specialist physicians in highly competitive markets. Under very competitive conditions, HMOs appear to compete by increasing access to both PCPs and specialists, with a greater emphasis on access to specialists. The review of research on HMO performance suggests that access to PCPs is better in MCOs. But access to specialists and hospitals is lower and more difficult in MCOs than FFS. Data do not suggest that processes of care, given access, are different in MCOs and FFS. MCO enrollees are more satisfied with financial aspects of a health plan and less satisfied with other aspects of health plan organization. There are potential problems with outcomes, with some studies finding greater declines among the chronically ill in MCOs than FFS. We found a variety of innovative care programs for the elderly, based on two fundamentally different approaches: organization around primary care or organizing around specialty care. Differences between the performance of the two approaches cannot be evaluated because of the small amount of research done. It is difficult to say how well particular programs perform and if they can be replicated. The innovative programs described in the literature tend to be benchmark programs developed by HMOs with a strong positive reputation.

Aged↗

PubMed2006

A polynomial time solvable formulation of multiple sequence alignment.

Since traditional multiple alignment formulations are NP-hard, heuristics are commonly employed to find acceptable alignments with no guaranteed performance bound. This causes a substantial difficulty in understanding what the resulting alignment means and in assessing the quality of these alignments. We propose an alternative formulation of multiple alignment based on the idea of finding a multiple alignment of k sequences which preserves k - 1 pairwise alignments as specified by edges of a given tree. Although it is well known that such a preserving alignment always exists, it did not become a mainstream method for multiple alignment since it seems that a lot of information is lost from ignoring pairwise similarities outside the tree. In contrast, by using pairwise alignments that incorporate consistency information from other sequences, we show that it is possible to obtain very good accuracy with the preserving alignment formulation. We show that a reasonable objective function to use is to find the shortest preserving alignment, and, by a reduction to a graph-theoretic problem, that the problem of finding the shortest preserving multiple alignment can be solved in polynomial time. We demonstrate the success of this approach on three sets of benchmark multiple alignments by using consistency-based pairwise alignments from the first stage of two of the best performing progressive alignment algorithms TCoffee and ProbCons and replace the second heuristic progressive step of these algorithms by the exact preserving alignment step. We apply this strategy to TCoffee and show that our approach outperforms TCoffee on two of the three test sets. We apply the strategy to a variant of ProbCons with no iterative refinements and show that our approach achieves similar or better accuracy except on one test set. We also compare our performance to ProbCons with iterative refinements and show that our approach achieves similar or better accuracy on many subcategories even without further refinements. The most important advantage of the preserving alignment formulation is that we are certain that we can solve the problem in polynomial time without using a heuristic. A software program implementing this approach (PSAlign) is available at http://faculty.cs.tamu.edu/shsze/psalign.

Algorithms↗

PubMedDate not supplied

Maintenance of skin integrity as a clinical indicator of nursing care.

OBJECTIVE: To assess maintenance of skin integrity in hospitalized patients as a clinical indicator of quality nursing care. DESIGN: Descriptive correlational study. SETTING: 17 acute care urban and rural hospitals in Texas. PARTICIPANTS: 723 hospitalized patients from 33 medical-surgical units. MAIN OUTCOME MEASURES: Pressure ulcers since admission and nursing care practices per unit. MAIN RESULTS: Overall prevalence of pressure ulcers since admission (4.7%) was lower than previously reported for acute care settings. Prevalence of pressure ulcers in hospitalized patients (10%) was higher than found in the Texas Nurses Association feasibility study (3.7%). The skin integrity ratio was strongly correlated for several unit variables, including number of beds per unit (r = 0.623) and average daily census per unit (r = 0.909). Benchmarking data across units showed that units with subjects that maintained skin integrity had a lower percentage of patients assessed on admission and performed daily assessments less frequently than did the units with subjects that did not maintain skin integrity. In addition, the units with subjects that maintained skin integrity classified fewer patients as at risk for pressure ulcer development and did not implement a skin care protocol for these patients. CONCLUSION: Patients who developed a pressure ulcer after admission were older and had more risk for pressure ulcers than those who maintained skin integrity. Benchmarking data detected various differences in nursing care. Significant relationships between study variables demonstrate the importance of assessing clinical indicators to monitor nursing care. Outcomes such as skin integrity, pressure ulcer since admission, and nosocomial ratio represent the quality of nursing care.

Adolescent↗

PubMed2024

Assessment of Gene Set Enrichment Analysis using curated RNA-seq-based benchmarks.

Pathway enrichment analysis is a ubiquitous computational biology method to interpret a list of genes (typically derived from the association of large-scale omics data with phenotypes of interest) in terms of higher-level, predefined gene sets that share biological function, chromosomal location, or other common features. Among many tools developed so far, Gene Set Enrichment Analysis (GSEA) stands out as one of the pioneering and most widely used methods. Although originally developed for microarray data, GSEA is nowadays extensively utilized for RNA-seq data analysis. Here, we quantitatively assessed the performance of a variety of GSEA modalities and provide guidance in the practical use of GSEA in RNA-seq experiments. We leveraged harmonized RNA-seq datasets available from The Cancer Genome Atlas (TCGA) in combination with large, curated pathway collections from the Molecular Signatures Database to obtain cancer-type-specific target pathway lists across multiple cancer types. We carried out a detailed analysis of GSEA performance using both gene-set and phenotype permutations combined with four different choices for the Kolmogorov-Smirnov enrichment statistic. Based on our benchmarks, we conclude that the classic/unweighted gene-set permutation approach offered comparable or better sensitivity-vs-specificity tradeoffs across cancer types compared with other, more complex and computationally intensive permutation methods. Finally, we analyzed other large cohorts for thyroid cancer and hepatocellular carcinoma. We utilized a new consensus metric, the Enrichment Evidence Score (EES), which showed a remarkable agreement between pathways identified in TCGA and those from other sources, despite differences in cancer etiology. This finding suggests an EES-based strategy to identify a core set of pathways that may be complemented by an expanded set of pathways for downstream exploratory analysis. This work fills the existing gap in current guidelines and benchmarks for the use of GSEA with RNA-seq data and provides a framework to enable detailed benchmarking of other RNA-seq-based pathway analysis tools.

Humans↗

PubMed2026

Benchmarking of Reference-Based Tools for Strain-Level Resolution of Plant Microbiome.

Strain-level identification of each microbe is crucial for understanding its role in the host. Most of the existing tools have primarily been evaluated on human metagenomic datasets, whereas the plant microbiome exhibits greater diversity and complexity and thus poses a challenge in the strain-level resolution of individual microbes. In this study, we conducted a comprehensive benchmarking of available reference-based tools for strain-level resolution of the plant microbiome. We evaluated seven tools on various performance parameters, like computational requirements, F1-score and relative abundances using synthetic datasets comprising microbes known to have strong associations with plants as well as real plant microbiome datasets. Our results demonstrated a better performance of StrainScan on the synthetic data, achieving higher F1-score and more accurate relative abundance estimates as compared to other tools, but its performance declined gradually with increasing strain diversity. However, StrainGE and StrainScan exhibited competitive performance on real plant metagenome data. Overall, though StrainGE exhibited better performance, it was more computationally expensive. However, StrainScan performed better in detecting low-abundance strains. Our findings suggest the comparative suitability of the available tools for the strain-level analysis of plant metagenome data and highlight the need for the development of more efficient and accurate taxonomic classifiers capable of handling the complex plant metagenome data while maintaining computational efficiency.

Microbiota↗

PubMed1994

Measurements of the electron dose distribution near inhomogeneities using a plastic scintillation detector.

PURPOSE: Accurate measurement of the electron dose distribution near an inhomogeneity is difficult with traditional dosimeters which themselves perturb the electron field. We tested the performance of a new high resolution, water-equivalent plastic scintillation detector which has ideal properties for this application. METHODS AND MATERIALS: A plastic scintillation detector with a 1 mm diameter, 3 mm long cylindrical sensitive volume was used to measure the dose distributions behind standard benchmark inhomogeneities in water phantoms. The plastic scintillator material is more water equivalent than polystyrene in terms of its mass collision stopping power and mass scattering power. Measurements were performed for beams of electrons having initial energies of 6 and 18 MeV at depths from 0.2-4.2 cm behind the inhomogeneities. RESULTS: The detector reveals hot and cold spots behind heterogeneities at resolutions equivalent to typical film digitizer spot sizes. Plots of the dose distributions behind air, aluminum, lead, and formulations for cortical and inner bone-equivalent materials are presented. CONCLUSION: The plastic scintillation detector is suited for measuring the electron dose distribution near an inhomogeneity.

Electrodes↗

PubMedDate not supplied

Variation in medical care: time for action.

Variations research has broken new ground by moving analysis of Medicare data from the community to the hospital level. The California study of hospitals further illustrates the power of comparing performance to demonstrate the levels of efficiency that can be achieved. WellPoint, replicating the Dartmouth approach with its commercial data, is partnering with medical specialty societies to make data "actionable" and move beyond benchmarking to help providers alter practice patterns. The shared goal is to achieve a material reduction in practice variation throughout health care. Key elements of their partnership include establishing national databases, reducing the administrative burden for pay-for-performance programs and using evidence-based metrics.

California↗

PubMedDate not supplied

Highly accurate and consistent method for prediction of helix and strand content from primary protein sequences.

OBJECTIVE: One of interesting computational topics in bioinformatics is prediction of secondary structure of proteins. Over 30 years of research has been devoted to the topic but we are still far away from having reliable prediction methods. A critical piece of information for accurate prediction of secondary structure is the helix and strand content of a given protein sequence. Ability to accurately predict content of those two secondary structures has a good potential to improve accuracy of prediction of the secondary structure. Most of the existing methods use composition vector to predict the content. Their underlying assumption is that the vector can be used to provide functional mapping between primary sequence and helix/strand content. While this is true for small sets of proteins we show that for larger protein sets such mapping are inconsistent, i.e. the same composition vectors correspond to different contents. To this end, we propose a method for prediction of helix/strand content from primary protein sequences that is fundamentally different from currently available methods. METHODS AND MATERIAL: Our method is accurate and uses a novel approach to obtain information from primary sequence based on a composition moment vector, which is a measure that includes information about both composition of a given primary sequence and the position of amino acids in the sequence. In contrast to the composition vector, we show that it provides functional mapping between primary sequence and the helix/strand content. RESULTS: A set of benchmarks involving a large protein dataset consisting of over 11,000 protein sequences from Protein Data Bank was performed to validate the method. Prediction done by a neural network had average accuracy of 91.5% for the helix and 94.5% for the strand contents. We also show that using the new measure results in about 40% reduction of error rates when compared with the composition vector results. CONCLUSIONS: The developed method has much better accuracy when compared with other existing methods, as shown on a large body of proteins, in contrast to other reported results that often target small sets of specific protein types, such as globular proteins.

Amino Acid Sequence↗

PubMed1999

A study of the application sharing capabilities in telemedicine.

The main aim of this study was to find out if the image format (TIFF or JPEG) influenced the time delay for transferring radiological images by the application sharing tool of a desktop videoconferencing system. The second task of the study was to define a procedure that optimized the time delay to load and remotely visualize the images. The results were achieved by applying a test procedure called 'benchmark protocol'. The videoconferencing system used for the test was Intel ProShare 200 v2.0. The image transfer was performed by a BRI ISDN connection. We showed that the image format had no significant influence on the time delay. We presented an optimal procedure for image transfer. Furthermore, store and forward procedures with simple file transfer were shown to be inferior to the use of application sharing. For radiological image transfer we recommend to use lossless file formats and application sharing with the image already loaded in because this method achieves the lowest time delays.

Computer Communication Networks↗

PubMed1998

Hospital profitability for a surgeon's common procedures predicts the surgeon's overall profitability for the hospital.

STUDY OBJECTIVE: To evaluate whether a hospital's profitability for a surgeon's common procedures predicts the surgeon's overall profitability for the hospital. DESIGN: Observational study. SETTING: Community and university-affiliated tertiary hospital with 21,903 surgical procedures performed per year. PATIENTS: 7,520 patients having surgery performed by one of 46 surgeons. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Financial data were obtained for all patients cared for by all the surgeons who performed at least ten cases of one of the hospital's six most common procedures. A surgeon's overall profitability for the hospital was measured using his or her contribution margin ratio (i.e., total revenue for all of the surgeon's patients divided by total variable cost for the patients). Contribution margin was calculated twice: once with all of a surgeon's patients, and second, limiting consideration to those patients who underwent one of the six common procedures. The common procedures accounted for 22 +/- 15% of the 46 surgeons' overall caseload, 29 +/- 10% of their patients' hospital costs, and 30 +/- 12% of the hospital revenue generated by the surgeons. Hospital contribution margin ratios ranged from 1.4 to 4.2. Contribution margin ratios for common procedures and contribution margin ratios for all patients were correlated (tau = 0.58, n = 46, p < 0.0001). CONCLUSIONS: Even though most surgical cases were for uncommon procedures, a surgeon's hospital profitability on common procedures predicted the surgeon's overall financial performance. Perioperative incentive programs based on common surgical procedures (clinical pathways) are likely to accurately reflect a surgeon's financial performance on their other surgeries.

Benchmarking↗

PubMedDate not supplied

Neighborhood behavior of in silico structural spaces with respect to in vitro activity spaces--a benchmark for neighborhood behavior assessment of different in silico similarity metrics.

In a previous work, we have introduced Neighborhood Behavior (NB) criteria for calculated molecular similarity metrics, based on the analysis of in vitro activity spaces that simultaneously monitor the responses of a compound with respect to an entire panel of biologically relevant receptors and enzymes. Now, these novel NB criteria will be used as a benchmark for the comparison of different in silico molecular similarity metrics, addressing the following topics: (1) the relative performance of 2D vs 3D descriptors, (2) the importance of the similarity scoring function for a given descriptor set, and (3) binary or Fuzzy Pharmacophore Fingerprints-can they capture the similarity of the spatial distribution of pharmacophoric groups despite different molecular connectivity? It was found that fuzzy pharmacophore descriptors (FBPA) displayed an optimal NB and, unlike their binary counterparts, were successful in evidencing pharmacophore pattern similarity independently of topological similarity. Topological FBPA, identical to the former except for the use of topological instead of 3D atom pair distances, display a somehow weaker, but significant NB. Metrics based on "classical" global 2D and 3D molecular descriptors and a Dice scoring function also performed well. The choice of the similarity scoring function is therefore as important as the choice of the appropriate molecular descriptors.

Journal Article↗

PubMed2006

A guide through present computational approaches for the identification of mammalian microRNA targets.

Computational microRNA (miRNA) target prediction is a field in flux. Here we present a guide through five widely used mammalian target prediction programs. We include an analysis of the performance of these individual programs and of various combinations of these programs. For this analysis we compiled several benchmark data sets of experimentally supported miRNA-target gene interactions. Based on the results, we provide a discussion on the status of target prediction and also suggest a stepwise approach toward predicting and selecting miRNA targets for experimental testing.

3' Untranslated Regions↗

PubMed2006

Interaction energies in hydrogen-bonded systems: a testing ground for subsystem formulation of density-functional theory.

The formalism based on the total energy bifunctional (E[rhoI,rhoII]) is used to derive interaction energies for several hydrogen-bonded complexes (water dimer, HCN-HF, H2CO-H2O, and MeOH-H2O). Benchmark ab initio data taken from the literature were used as a reference in the assessment of the performance of gradient-free [local density approximation (LDA)] and gradient-dependent [generalized gradient approximation (GGA)] approximations to the exchange-correlation and nonadditive kinetic-energy components of E[rhoI,rhoII]. On average, LDA performs better than GGA. The average absolute error of calculated LDA interaction energies amounts to 1.0 kJ/mol. For H2CO-H2O and H2O-H2O complexes, the potential-energy curves corresponding to the stretching of the intermolecular distance are also calculated. The positions of the minima are in a good agreement (less than 0.2 A) with the reference ab initio data. Both variational and nonvariational calculations are performed to assess the energetic effects associated with complexation-induced deformations of molecular electron densities.

Journal Article↗

PubMed2026

jsPCA: fast, scalable, and interpretable identification of spatial domains and variable genes across multi-slice and multi-sample spatial transcriptomics data.

MOTIVATION: Spatial transcriptomics technologies record genome-wide measurements of gene expression with high spatial resolution. These technologies generate large and high-dimensional datasets requiring efficient automated methods for their analysis. We introduce joint spatial PCA (jsPCA), a novel, fast, scalable and interpretable method for the automatic identification of spatial domains and variable genes in multi-slice and multi-sample spatial transcriptomics data. RESULTS: jsPCA relies on a simple mathematical formulation of a spatial covariance defined as the product of the gene expression covariance with the spatial autocorrelation. The principal components of this spatial covariance yield a biologically meaningful low-dimensional representation. From this representation, spatial domains are derived by simple clustering and spatially variable genes are identified directly from the principal component coefficients. A joint representation of multiple slices and samples without spatial alignment is obtained by computing common principal components via joint diagonalization. By leveraging data sparsity and non-convex manifold optimization, jsPCA leads to computing time in the order of seconds to minutes, substantially outperforming state-of-the-art approaches. We benchmarked jsPCA against 10 state-of-the-art methods on two reference databases. Our approach demonstrated excellent performance, comparable or better than state-of-the-art methods, while being much faster, interpretable, and scalable to very large datasets.

Journal Article↗

PubMed2026

Leveraging ONT move table values for signal aware variant calling.

Oxford Nanopore Technologies (ONT) sequencing enables long-range haplotype phasing and contiguous genome assembly but still exhibits elevated error rates that challenge small variant calling, particularly for insertions and deletions (Indels). While raw electrical signals contain rich information, existing signal-aware methods require computationally intensive processing of large signal files. Here, we present Clair3 v2, a method that leverages the ONT move table-a lightweight byproduct of basecalling that maps signal events to nucleotide positions-to improve variant calling accuracy. Clair3 v2 builds upon Clair3 and integrates signal-level dwelling time to significantly enhance variant calling performance. We also propose a genome position based circular buffer to incorporate dwelling time with minimal computational overhead. Benchmarking across six Genome in a Bottle samples demonstrates substantial improvements in variant calling accuracy. With HAC basecalling, Clair3 v2 achieves a mean SNP F1-score of 97.69% at 10 × depth (compared to 96.45% for baseline Clair3), and Indel F1 scores improved from 64.27% to 76.70%, while gains persisted at higher depths. The benefits were most pronounced for longer Indels and in complex genomic regions, where Indel F1 scores in long homopolymer regions improved from 14.3% to 45.2%. Benchmark results across various basecalling modes, samples, and coverage settings outperformed Clair3 baselines and other methods, including DeepVariant and Dorado Variant, and demonstrate the significant benefits of Clair3 v2. Furthermore, Clair3 v2 incurs negligible runtime compared to standard Clair3, making it practical for routine use.

Sequence Analysis, DNA↗

PubMed2006

Spritz: a server for the prediction of intrinsically disordered regions in protein sequences using kernel machines.

Intrinsically disordered proteins have long stretches of their polypeptide chain, which do not adopt a single native structure composed of stable secondary and tertiary structure in the absence of binding partners. The prediction of intrinsically disordered regions in proteins from sequence is increasingly becoming of interest, as the presence of many such regions in the complete genome sequences are discovered and important functional roles are associated with them. We have developed a machine learning approach based on two support vector machines (SVM) to discriminate disordered regions from sequence. The SVM are trained and benchmarked on two sets, representing long and short disordered regions. A preliminary version of Spritz was shown to perform consistently well at the recent biannual CASP-6 experiment [Critical Assessment of Techniques for Protein Structure Prediction (CASP), 2004]. The fully developed Spritz method is freely available as a web server at http://distill.ucd.ie/spritz/ and http://protein.cribi.unipd.it/spritz/.

Artificial Intelligence↗

PubMed2001

Pcons: a neural-network-based consensus predictor that improves fold recognition.

During recent years many protein fold recognition methods have been developed, based on different algorithms and using various kinds of information. To examine the performance of these methods several evaluation experiments have been conducted. These include blind tests in CASP/CAFASP, large scale benchmarks, and long-term, continuous assessment with newly solved protein structures. These studies confirm the expectation that for different targets different methods produce the best predictions, and the final prediction accuracy could be improved if the available methods were combined in a perfect manner. In this article a neural-network-based consensus predictor, Pcons, is presented that attempts this task. Pcons attempts to select the best model out of those produced by six prediction servers, each using different methods. Pcons translates the confidence scores reported by each server into uniformly scaled values corresponding to the expected accuracy of each model. The translated scores as well as the similarity between models produced by different servers is used in the final selection. According to the analysis based on two unrelated sets of newly solved proteins, Pcons outperforms any single server by generating approximately 8%-10% more correct predictions. Furthermore, the specificity of Pcons is significantly higher than for any individual server. From analyzing different input data to Pcons it can be shown that the improvement is mainly attributable to measurement of the similarity between the different models. Pcons is freely accessible for the academic community through the protein structure-prediction metaserver at http://bioinfo.pl/meta/.

Algorithms↗

PubMed2000

Boosting neural networks.

Boosting is a general method for improving the performance of learning algorithms. A recently proposed boosting algorithm, AdaBoost, has been applied with great success to several benchmark machine learning problems using mainly decision trees as base classifiers. In this article we investigate whether AdaBoost also works as well with neural networks, and we discuss the advantages and drawbacks of different versions of the AdaBoost algorithm. In particular, we compare training methods based on sampling the training set and weighting the cost function. The results suggest that random resampling of the training data is not the main explanation of the success of the improvements brought by AdaBoost. This is in contrast to bagging, which directly aims at reducing variance and for which random resampling is essential to obtain the reduction in generalization error. Our system achieves about 1.4% error on a data set of on-line handwritten digits from more than 200 writers. A boosted multilayer network achieved 1.5% error on the UCI letters and 8.1% error on the UCI satellite data set, which is significantly better than boosted decision trees.

Algorithms↗