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At least 451 records · Page 25Linked to original sources

PubMed2005

Balancing efficiency of health services and equity of access in remote areas in Greece.

Data envelopment analysis (DEA) was used to investigate the efficiency of a set of small-scaled Greek hospitals known as hospital-health centers (HHCs). These facilities naturally provide primary and secondary care but are also expected to function as health centers addressing mostly preventive medicine, hygiene and other public health issues. They are located in remote rural areas and serve the relatively small local populations. This study aimed to obtain insight on their productive efficiency in light of their particular role. The sample consisted of 17 from the 18 units existing in the Greek NHS. Variables chosen to characterize production were numbers of doctors, nurses and beds as inputs, and admissions, outpatient visits and preventive medical services as outputs. The DEA model was input oriented, allowed for constant returns to scale and units were ranked according to a benchmarking approach. Analyses were performed with and without the preventive medicine variable and the results demonstrated technical inefficiencies 26.77 and 25.13%, respectively. Location appeared to affect performance, with remote units, e.g. on small islands, more inefficient. This raises the question if correcting reduced efficiency compromises equity of service access for highly dependent populations. Moreover, we observed superior performance of units additionally offering preventive medical services. This generates another question as to the role these facilities should play in our currently changing health care system.

Efficiency, Organizational↗

PubMed2007

Toward optimal recording of surgical complications: concurrent tracking compared to the discharge data set.

BACKGROUND: Information extracted from the hospital discharge data set is used increasingly for outcomes research and for benchmarking hospital and provider performance. The accuracy of these data in detecting vascular complications has never been validated. METHODS: We compared morbidity and mortality data derived from the hospital discharge data set to similar data recorded concurrently by our Surgical Activity Tracking System (SATS) for 1 year on the vascular surgery service. RESULTS: Of 798 total admissions, no complications were detected by either system in 598 admissions (75%). In 200 admissions (25%), there were 335 complications, including 24 deaths (3.0%), that occurred either in-hospital or within 30 days of the date of operation or the date of discharge for nonoperative admissions. Of the 335 complications, 180 (53.7%) were recorded by both systems; the SATS missed 59 complications recorded in the hospital discharge data set (17.6%), whereas the hospital discharge data set missed 96 complications recorded in the SATS (28.7%, P = .003). Of the 289 in-hospital complications, the SATS recorded 230 (79.5%), whereas the hospital discharge data set recorded 229 (79.2%). Of the 24 deaths, the hospital discharge data set missed 6 that occurred after discharge but within the 30-day reporting period CONCLUSIONS: Both systems are not completely accurate for tracking inpatient complications. The SATS was more representative than the hospital discharge data set in capturing 30-day morbidity and mortality. An amalgamation of the 2 systems would provide more optimal tracking of complications.

Databases, Factual↗

PubMed2006

Promoting smoking cessation during hospitalization for coronary artery disease.

BACKGROUND: Quitting smoking is the most effective intervention to reduce mortality in patients with coronary artery disease who smoke. Guidelines for the treatment of tobacco dependency recommend that health care institutions develop plans to support the consistent and effective identification and treatment of tobacco users. The University of Ottawa Heart Institute (Ottawa, Ontario) has implemented an institutional program to identify and treat all smokers admitted to the Institute. OBJECTIVES: The objectives of the present paper are to describe core elements of this program and present data concerning its reach and effectiveness. PROGRAM DESCRIPTION: The goal of the program is to increase the number of smokers who are abstinent from smoking six months after a coronary artery disease-related hospitalization. Core elements of the program include: documentation of smoking status at hospital admission; inclusion of cessation intervention on patient care maps; individualized, bedside counselling by a nurse counsellor; the appropriate and timely use of nicotine replacement therapy; automated telephone follow-up; referral to outpatient cessation resources; and training of medical residents and nursing staff. Program reach and effectiveness were measured over a one-year period. RESULTS: Between April 2003 and March 2004, almost 1300 smokers were identified at admission, and 91% received intervention to help them quit smoking. At six-month follow-up, 44% were smoke-free. CONCLUSIONS: Hospitalization for coronary artery disease provides an important opportunity to intervene with smokers when their motivation to quit is high. An institutional approach reinforces the importance of smoking cessation in this patient population and increases the rate of smoking cessation. Posthospitalization quit rates should be a benchmark of cardiac program performance.

Coronary Disease↗

PubMed2002

Geometrical modelling of Ohmic conductance in ion channels.

In an Ohmic model, channel conductivity can be described in terms of the geometry of a conducting cable. The essential features of such devices are the arc length of the curve describing the channel's longitudinal path, and the cross-sectional areas transversal to this curve. In a first approximation, conducting channels can be represented by an average molecular shape with estimated lengths and cross-sectional areas. Whereas the physical shortcomings of this approach are known, its accuracy limitations in practice have not been established. In this work, we discuss an improved model for the channel's shape, one that allows us to gauge how much of the Ohmic conductivity can be assigned purely to geometrical features. In the present algorithm, we investigate all regions inside the pore that are accessible to ions using various choices for the molecular surface of the inner channel. We discuss the agreement with experimental conductances in the case of 12 channels (cholera toxin B-subunit pentamer, Staphylococcus aureus alpha-hemolysin, Streptomyces lividans KcsA channel, seven porins, gramicidin A, and phospholamban). Our results can be regarded as a benchmark for the best performance that can be expected from a geometrical model of conductance. Consequently, significant deviations from experimental trends can safely be assigned to non-geometrical factors, namely the specific composition of the ion channel and the detailed electrostatic interactions between the channel and a particular ion.

Algorithms↗

PubMedDate not supplied

Retention and viability characteristics of mammalian cells in an acoustically driven polymer mesh.

A processing approach for the collection and retention of mammalian cells within a high porosity polyester mesh having millimeter-sized pores has been studied. Cell retention occurs via energizing the mesh with a low intensity, resonant acoustic field. The resulting acoustic field induces the interaction of cells with elements of the mesh or with each other and effectively prevents the entrainment of cells in the effluent stream. Experiments involving aqueous suspensions of polystyrene particles were used to provide benchmark data on the performance of the acoustic retention cell. Experiments using mouse hybridoma cells showed that retention densities of over 1.5 x 10(8) cell/mL could be obtained. In addition, the acoustic field was shown to produce a negligible effect on cell viability for short-term exposure.

Acoustics↗

PubMed2026

META-DIFF: a k-mer-based pipeline that detects differentially abundant sequences in metagenomics whole genome sequencing.

Traditional case-control metagenomic studies are constrained by their dependence on taxonomic and functional databases. Because annotation occurs before differential analysis, they are limited to known elements and keep function and taxonomy separate. Although binning strategies have emerged to reconstruct genomes and mitigate this issue, they still require an assembly step, preventing the use of all available sequencing data. Here, we introduce META-DIFF, a pipeline based on differentially abundant k-mers independently of any prior annotation. From those k-mers, it reconstructs longer sequences and provides biological context, as well as the best set of unitigs to discriminate between conditions. Across both taxonomy-centric and functionally-centric benchmarks, it showed robust performance and displayed great reproducibility. It also behaved more conservatively than did other univariate methodologies, i.e. it maintained a high precision at the expense of recall, particularly in conditions of low fold-change and limited sequencing depth. The efficacy of META-DIFF was further validated through its application to a real-world colorectal cancer dataset, which produced both confirmatory and novel results compared with those of previous publications. The pipeline is able to exploit all reads and identify differentially abundant elements, including unknown DNA, prior to annotation. With the guidelines provided, META-DIFF provides users with great exploratory power to unravel microbiome changes.

Metagenomics↗

PubMed2005

Functional and basis set dependence of K-edge shake-up spectra of molecules.

A straightforward approach for computing the K-edge shake-up spectra of molecules based on equivalent core-hole linear response theory at both Hartree-Fock and density functional theory levels is proposed. Benchmark calculations have been performed to explore its sensitivity to different types of functionals and basis sets for the carbon 1s shake-up spectra of benzene and metal-free phthalocyanine (H2Pc). A very good agreement with previous theoretical and experimental works for the benzene molecule has been obtained for all the functionals and basis sets tested. Electron correlation is found to be essential for a good description of the H2Pc system, whose experimental C 1s shake-up spectrum is best reproduced by the hybrid density functional.

Journal Article↗

PubMed2002

Entry one: striving for best practice in professional assessment.

The aim of this study was to develop a best practice model of professional assessment to ensure efficient and effective delivery of home-based services to frail and disabled elders. In 2000, an innovative model of professional assessment was introduced by one of Australia's largest providers of home-based care in order to reduce multiple assessments and to reduce the utilisation of assessment as a gatekeeping tool for limiting access to services. Data was analysed from a random sample of 1500 clients drawn from a population of 5000 as well as through the use of a survey tool administered to the Organisation's assessment staff and other key stakeholders. Results revealed that, contrary to popular belief, carer advocacy plays a significant role in the professional assessment process to the point that clients with carers received significantly more services and service time that clients without such support. However, if not monitored, assessment can also be used as a gate-keeping tool as opposed to one that can provide significant benefits to the consumers through comprehensive need articulation. We argue that the "professional" approach does not preclude empowerment and that assessment should not be used as a gate-keeping tool.

Aged↗

PubMed2025

scACCorDiON: a clustering approach for explainable patient level cell-cell communication graph analysis.

MOTIVATION: Combining single-cell sequencing with ligand-receptor (LR) analysis paves the way for the characterization of cell communication events in complex tissues. In particular, directed weighted graphs naturally represent cell-cell communication events. However, current computational methods cannot yet analyze sample-specific cell-cell communication events, as measured in single-cell data produced in large patient cohorts. Cohort-based cell-cell communication analysis presents many challenges, such as the nonlinear nature of cell-cell communication and the high variability given by the patient-specific single-cell RNAseq datasets. RESULTS: Here, we present scACCorDiON (single-cell Analysis of Cell-Cell Communication in Disease clusters using Optimal transport in Directed Networks), an optimal transport algorithm exploring node distances on the Markov Chain as the ground metric between directed weighted graphs. Benchmarking indicates that scACCorDiON performs a better clustering of samples according to their disease status than competing methods that use undirected graphs. We provide a case study of pancreas adenocarcinoma, where scACCorDion detects a sub-cluster of disease samples associated with changes in the tumor microenvironment. Our study case corroborates that clusters provide a robust and explainable representation of cell-cell communication events and that the expression of detected LR pairs is predictive of pancreatic cancer survival. AVAILABILITY AND IMPLEMENTATION: The code of scACCorDiON is available at https://scaccordion.readthedocs.io/en/latest/. and https://doi.org/10.5281/zenodo.15267648. The survival analysis package can be found at https://github.com/CostaLab/scACCorDiON.su.

Humans↗

PubMed2004

DNAFSMiner: a web-based software toolbox to recognize two types of functional sites in DNA sequences.

UNLABELLED: DNAFSMiner (DNA Functional Sites Miner) is a web-based software toolbox to recognize functional sites in nucleic acid sequences. Currently in this toolbox, we provide two software: TIS Miner and Poly(A) Signal Miner. The TIS Miner can be used to predict translation initiation sites in vertebrate DNA/mRNA/cDNA sequences, and the Poly(A) Signal Miner can be used to predict polyadenylation [poly(A)] signals in human DNA sequences. The prediction results are better than those by literature methods on two benchmark applications. This good performance is mainly attributable to our unique learning method. DNAFSMiner is available free of charge for academic and non-profit organizations. AVAILABILITY: http://research.i2r.a-star.edu.sg/DNAFSMiner/ CONTACT: huiqing@i2r.a-star.edu.sg.

Algorithms↗

PubMed2005

CHORAL: a differential geometry approach to the prediction of the cores of protein structures.

MOTIVATION: Although the cores of homologous proteins are relatively well conserved, amino acid substitutions lead to significant differences in the structures of divergent superfamilies. Thus, the classification of amino acid sequence patterns and the selection of appropriate fragments of the protein cores of homologues of known structure are important for accurate comparative modelling. RESULTS: CHORAL utilizes a knowledge-based method comprising an amalgam of differential geometry and pattern recognition algorithms to identify conserved structural patterns in homologous protein families. Propensity tables are used to classify and to select patterns that most likely represent the structure of the core for a target protein. In our benchmark, CHORAL demonstrates a performance equivalent to that of MODELLER.

Algorithms↗

PubMed2025

Unsupervised multiscale clustering of single-cell transcriptomes to identify hierarchical structures of cell subtypes.

BACKGROUND: Cell clustering is an essential step in uncovering cellular architectures in single-cell RNA sequencing (scRNA-seq) data. However, the existing cell clustering approaches are not well designed to dissect complex structures of cellular landscapes at a finer resolution. RESULTS: Here, we develop a multiscale clustering (MSC) approach to construct a sparse cell-cell correlation network for unsupervised identification of de novo cell types and subtypes across multiple resolutions. Based upon simulated silver- and gold-standard data as well as real scRNA-seq data in diseases, MSC demonstrates significantly improved performance compared to established benchmark methods and reveals a biologically meaningful cell hierarchy to facilitate the discovery of novel disease-associated cell subtypes and mechanisms. CONCLUSIONS: We present MSC as a new single-cell multiscale clustering framework as a powerful tool for advancing discoveries in disease-associated cell populations using single-cell sequencing data.

Single-Cell Analysis↗

PubMed2006

(PS)2: protein structure prediction server.

Protein structure prediction provides valuable insights into function, and comparative modeling is one of the most reliable methods to predict 3D structures directly from amino acid sequences. However, critical problems arise during the selection of the correct templates and the alignment of query sequences therewith. We have developed an automatic protein structure prediction server, (PS)2, which uses an effective consensus strategy both in template selection, which combines PSI-BLAST and IMPALA, and target-template alignment integrating PSI-BLAST, IMPALA and T-Coffee. (PS)2 was evaluated for 47 comparative modeling targets in CASP6 (Critical Assessment of Techniques for Protein Structure Prediction). For the benchmark dataset, the predictive performance of (PS)2, based on the mean GTD_TS score, was superior to 10 other automatic servers. Our method is based solely on the consensus sequence and thus is considerably faster than other methods that rely on the additional structural consensus of templates. Our results show that (PS)2, coupled with suitable consensus strategies and a new similarity score, can significantly improve structure prediction. Our approach should be useful in structure prediction and modeling. The (PS)2 is available through the website at http://ps2.life.nctu.edu.tw/.

Amino Acid Sequence↗

PubMed2004

Self-organizing nets for optimization.

Given some optimization problem and a series of typically expensive trials of solution candidates sampled from a search space, how can we efficiently select the next candidate? We address this fundamental problem by embedding simple optimization strategies in learning algorithms inspired by Kohonen's self-organizing maps and neural gas networks. Our adaptive nets or grids are used to identify and exploit search space regions that maximize the probability of generating points closer to the optima. Net nodes are attracted by candidates that lead to improved evaluations, thus, quickly biasing the active data selection process toward promising regions, without loss of ability to escape from local optima. On standard benchmark functions, our techniques perform more reliably than the widely used covariance matrix adaptation evolution strategy. The proposed algorithm is also applied to the problem of drag reduction in a flow past an actively controlled circular cylinder, leading to unprecedented drag reduction.

Neural Networks, Computer↗

PubMed2004

Falcon: neural fuzzy control and decision systems using FKP and PFKP clustering algorithms.

Neural fuzzy networks proposed in the literature can be broadly classified into two groups. The first group is essentially fuzzy systems with self-tuning capabilities and requires an initial rule base to be specified prior to training. The second group of neural fuzzy networks, on the other hand, is able to automatically formulate the fuzzy rules from the numerical training data. Examples are the Falcon-ART, and the POPFNN family of networks. A cluster analysis is first performed on the training data and the fuzzy rules are subsequently derived through the proper connections of these computed clusters. This correspondence proposes two new networks: Falcon-FKP and Falcon-PFKP. They are extensions of the Falcon-ART network, and aimed to overcome the shortcomings faced by the Falcon-ART network itself, i.e., poor classification ability when the classes of input data are very similar to each other, termination of training cycle depends heavily on a preset error parameter, the fuzzy rule base of the Falcon-ART network may not be consistent Nauck, there is no control over the number of fuzzy rules generated, and learning efficiency may deteriorate by using complementarily coded training data. These deficiencies are essentially inherent to the fuzzy ART, clustering technique employed by the Falcon-ART network. Hence, two clustering techniques--Fuzzy Kohonen Partitioning (FKP) and its pseudo variant PFKP, are synthesized with the basic Falcon structure to compute the fuzzy sets and to automatically derive the fuzzy rules from the training data. The resultant neural fuzzy networks are Falcon-FKP and Falcon-PFKP, respectively. These two proposed networks have a lean and efficient training algorithm and consistent fuzzy rule bases. Extensive simulations are conducted using the two networks and their performances are encouraging when benchmarked against other neural and neural fuzzy systems.

Journal Article↗

PubMed2006

FITSK: online local learning with generic fuzzy input Takagi-Sugeno-Kang fuzzy framework for nonlinear system estimation.

Existing Takagi-Sugeno-Kang (TSK) fuzzy models proposed in the literature attempt to optimize the global learning accuracy as well as to maintain the interpretability of the local models. Most of the proposed methods suffer from the use of offline learning algorithms to globally optimize this multi-criteria problem. Despite the ability to reach an optimal solution in terms of accuracy and interpretability, these offline methods are not suitably applicable to learning in adaptive or incremental systems. Furthermore, most of the learning methods in TSK-model are susceptible to the limitation of the curse-of-dimensionality. This paper attempts to study the criteria in the design of TSK-models. They are: 1) the interpretability of the local model; 2) the global accuracy; and 3) the system dimensionality issues. A generic framework is proposed to handle the different scenarios in this design problem. The framework is termed the generic fuzzy input Takagi-Sugeno-Kang fuzzy framework (FITSK). The FITSK framework is extensible to both the zero-order and the first-order FITSK models. A zero-order FITSK model is suitable for the learning of adaptive system, and the bias-variance of the system can be easily controlled through the degree of localization. On the other hand, a first-order FITSK model is able to achieve higher learning accuracy for nonlinear system estimation. A localized version of recursive least-squares algorithm is proposed for the parameter tuning of the first-order FITSK model. The local recursive least-squares is able to achieve a balance between interpretability and learning accuracy of a system, and possesses greater immunity to the curse-of-dimensionality. The learning algorithms for the FITSK models are online, and are readily applicable to adaptive system with fast convergence speed. Finally, a proposed guideline is discussed to handle the model selection of different FITSK models to tackle the multi-criteria design problem of applying the TSK-model. Extensive simulations were conducted using the proposed FITSK models and their learning algorithms; their performances are encouraging when benchmarked against other popular fuzzy systems.

Algorithms↗

PubMed2005

National audit of continence care: laying the foundation.

INTRODUCTION: National audit provides a basis for establishing performance against national standards, benchmarking against other service providers and improving standards of care. For effective audit, clinical indicators are required that are valid, feasible to apply and reliable. This study describes the methods used to develop clinical indicators of continence care in preparation for a national audit. AIM: To describe the methods used to develop and test clinical indicators of continence care with regard to validity, feasibility and reliability. METHOD: A multidisciplinary working group developed clinical indicators that measured the structure, process and outcome of care as well as case-mix variables. Literature searching, consensus workshops and a Delphi process were used to develop the indicators. The indicators were tested in 15 secondary care sites, 15 primary care sites and 15 long-term care settings. RESULTS: The process of development produced indicators that received a high degree of consensus within the Delphi process. Testing of the indicators demonstrated an internal reliability of 0.7 and an external reliability of 0.6. Data collection required significant investment in terms of staff time and training. CONCLUSION: The method used produced indicators that achieved a high degree of acceptance from health care professionals. The reliability of data collection was high for this audit and was similar to the level seen in other successful national audits. Data collection for the indicators was feasible to collect, however, issues of time and staffing were identified as limitations to such data collection. The study has described a systematic method for developing clinical indicators for national audit. The indicators proved robust and reliable in primary and secondary care as well as long-term care settings.

Delphi Technique↗

PubMed2006

Analytic IMRT dose calculations utilizing Monte Carlo to predict MLC fluence modulation.

A hybrid dose-computation method is designed which accurately accounts for multileaf collimator (MLC)-induced intensity modulation in intensity modulated radiation therapy (IMRT) dose calculations. The method employs Monte Carlo (MC) modeling to determine the fluence modulation caused by the delivery of dynamic or multisegmental (step-and-shoot) MLC fields, and a conventional dose-computation algorithm to estimate the delivered dose to a phantom or a patient. Thus, it determines the IMRT fluence prediction accuracy achievable by analytic methods in the limit that the analytic method includes all details of the MLC leaf transport and scatter. The hybrid method is validated and benchmarked by comparison with in-phantom film dose measurements, as well as dose calculations from two in-house, and two commercial treatment planning system analytic fluence estimation methods. All computation methods utilize the same dose algorithm to calculate dose to a phantom, varying only in the estimation of the MLC modulation of the incident photon energy fluence. Gamma analysis, with respect to measured two-dimensional (2D) dose planes, is used to benchmark each algorithm's performance. The analyzed fields include static and dynamic test patterns, as well as fields from ten DMLC IMRT treatment plans (79 fields) and five SMLC treatment plans (29 fields). The test fields (fully closed MLC, picket fence, sliding windows of different size, and leaf-tip profiles) cover the extremes of MLC usage during IMRT, while the patient fields represent realistic clinical conditions. Of the methods tested, the hybrid method most accurately reproduces measurements. For the hybrid method, 79 of 79 DMLC field calculations have gamma < 1 (3%/3 mm) for more than 95% of the points (per field) while for SMLC fields, 27 of 29 pass the same criteria. The analytic energy fluence estimation methods show inferior pass rates, with 76 of 79 DMLC and 24 of 29 SMLC fields having more than 95% of the test points with gamma < or = 1 (3%/3 mm). Paired one-way ANOVA tests of the gamma analysis results found that the hybrid method better predicts measurements in terms of both the fraction of points with gamma < or = 1 and the average gamma for both 2%/2 mm and 3%/3 mm criteria. These results quantify the enhancement in accuracy in IMRT dose calculations when MC is used to model the MLC field modulation.

Body Burden↗