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At least 217 records · Page 12Linked to original sources
PubMed2005

POSBIOTM-NER: a trainable biomedical named-entity recognition system.

SUMMARY: POSBIOTM-NER is a trainable biomedical named-entity recognition system. POSBIOTM-NER can be automatically trained and adapted to new datasets without performance degradation, using CRF (conditional random field) machine learning techniques and automatic linguistic feature analysis. Currently, we have trained our system on three different datasets. GENIA-NER was trained based on GENIA Corpus, GENE-NER based on BioCreative data and GPCR-NER based on our own POSBIOTM/NE corpus, respectively, which would be used in GPCR-related pathway extraction.

Algorithms↗
PubMed2005

A robust meta-classification strategy for cancer diagnosis from gene expression data.

One of the major challenges in cancer diagnosis from microarray data is to develop robust classification models which are independent of the analysis techniques used and can combine data from different laboratories. We propose a meta-classification scheme which uses a robust multivariate gene selection procedure and integrates the results of several machine learning tools trained on raw and pattern data. We validate our method by applying it to distinguish diffuse large B-cell lymphoma (DLBCL) from follicular lymphoma (FL) on two independent datasets: the HuGeneFL Affmetrixy dataset of Shipp et al. (www. genome.wi.mit.du/MPR /lymphoma) and the Hu95Av2 Affymetrix dataset (DallaFavera's laboratory, Columbia University). Our meta-classification technique achieves higher predictive accuracies than each of the individual classifiers trained on the same dataset and is robust against various data perturbations. We also find that combinations of p53 responsive genes (e.g., p53, PLK1 and CDK2) are highly predictive of the phenotype.

Algorithms↗
PubMed2004

Generalisability in economic evaluation studies in healthcare: a review and case studies.

OBJECTIVES: To review, and to develop further, the methods used to assess and to increase the generalisability of economic evaluation studies. DATA SOURCES: Electronic databases. REVIEW METHODS: Methodological studies relating to economic evaluation in healthcare were searched. This included electronic searches of a range of databases, including PREMEDLINE, MEDLINE, EMBASE and EconLit, and manual searches of key journals. The case studies of a decision analytic model involved highlighting specific features of previously published economic studies related to generalisability and location-related variability. The case-study involving the secondary analysis of cost-effectiveness analyses was based on the secondary analysis of three economic studies using data from randomised trials. RESULTS: The factor most frequently cited as generating variability in economic results between locations was the unit costs associated with particular resources. In the context of studies based on the analysis of patient-level data, regression analysis has been advocated as a means of looking at variability in economic results across locations. These methods have generally accepted that some components of resource use and outcomes are exchangeable across locations. Recent studies have also explored, in cost-effectiveness analysis, the use of tests of heterogeneity similar to those used in clinical evaluation in trials. The decision analytic model has been the main means by which cost-effectiveness has been adapted from trial to non-trial locations. Most models have focused on changes to the cost side of the analysis, but it is clear that the effectiveness side may also need to be adapted between locations. There have been weaknesses in some aspects of the reporting in applied cost-effectiveness studies. These may limit decision-makers' ability to judge the relevance of a study to their specific situations. The case study demonstrated the potential value of multilevel modelling (MLM). Where clustering exists by location (e.g. centre or country), MLM can facilitate correct estimates of the uncertainty in cost-effectiveness results, and also a means of estimating location-specific cost-effectiveness. The review of applied economic studies based on decision analytic models showed that few studies were explicit about their target decision-maker(s)/jurisdictions. The studies in the review generally made more effort to ensure that their cost inputs were specific to their target jurisdiction than their effectiveness parameters. Standard sensitivity analysis was the main way of dealing with uncertainty in the models, although few studies looked explicitly at variability between locations. The modelling case study illustrated how effectiveness and cost data can be made location-specific. In particular, on the effectiveness side, the example showed the separation of location-specific baseline events and pooled estimates of relative treatment effect, where the latter are assumed exchangeable across locations. CONCLUSIONS: A large number of factors are mentioned in the literature that might be expected to generate variation in the cost-effectiveness of healthcare interventions across locations. Several papers have demonstrated differences in the volume and cost of resource use between locations, but few studies have looked at variability in outcomes. In applied trial-based cost-effectiveness studies, few studies provide sufficient evidence for decision-makers to establish the relevance or to adjust the results of the study to their location of interest. Very few studies utilised statistical methods formally to assess the variability in results between locations. In applied economic studies based on decision models, most studies either stated their target decision-maker/jurisdiction or provided sufficient information from which this could be inferred. There was a greater tendency to ensure that cost inputs were specific to the target jurisdiction than clinical parameters. Methods to assess generalisability and variability in economic evaluation studies have been discussed extensively in the literature relating to both trial-based and modelling studies. Regression-based methods are likely to offer a systematic approach to quantifying variability in patient-level data. In particular, MLM has the potential to facilitate estimates of cost-effectiveness, which both reflect the variation in costs and outcomes between locations and also enable the consistency of cost-effectiveness estimates between locations to be assessed directly. Decision analytic models will retain an important role in adapting the results of cost-effectiveness studies between locations. Recommendations for further research include: the development of methods of evidence synthesis which model the exchangeability of data across locations and allow for the additional uncertainty in this process; assessment of alternative approaches to specifying multilevel models to the analysis of cost-effectiveness data alongside multilocation randomised trials; identification of a range of appropriate covariates relating to locations (e.g. hospitals) in multilevel models; and further assessment of the role of econometric methods (e.g. selection models) for cost-effectiveness analysis alongside observational datasets, and to increase the generalisability of randomised trials.

Coronary Disease↗
PubMed2003

Estimating the mean effect size in meta-analysis: bias, precision, and mean squared error of different weighting methods.

Although use of the standardized mean difference in meta-analysis is appealing for several reasons, there are some drawbacks. In this article, we focus on the following problem: that a precision-weighted mean of the observed effect sizes results in a biased estimate of the mean standardized mean difference. This bias is due to the fact that the weight given to an observed effect size depends on this observed effect size. In order to eliminate the bias, Hedges and Olkin (1985) proposed using the mean effect size estimate to calculate the weights. In the article, we propose a third alternative for calculating the weights: using empirical Bayes estimates of the effect sizes. In a simulation study, these three approaches are compared. The mean squared error (MSE) is used as the criterion by which to evaluate the resulting estimates of the mean effect size. For a meta-analytic dataset with a small number of studies, the MSE is usually smallest when the ordinary procedure is used, whereas for a moderate or large number of studies, the procedures yielding the best results are the empirical Bayes procedure and the procedure of Hedges and Olkin, respectively.

Behavioral Research↗
PubMed2003

'Event tree' analysis of out-of-hospital cardiac arrest data: confirming the importance of bystander CPR.

OBJECTIVE: The British National Service Framework (NSF) for heart disease commended the 'Utstein style' for auditing out-of-hospital cardiac arrests. The NSF also set standards for pre-hospital treatment and response times. To increase the flexibility of Utstein, an 'event tree' technique is proposed as an audit tool. Event trees consist of nodes and branches on which numbers, percentages or probability values are entered. METHODS: Using the London Ambulance Service's (LAS) 1997 database on 3,759 out-of-hospital cardiac arrests, 2,772 arrests witnessed by lay bystanders or unwitnessed were analysed focusing on bystander cardiopulmonary resuscitation (BCPR) and response times. RESULTS: The Utstein template showed that witnessed arrests in ventricular fibrillation (VF) or ventricular tachycardia (VT) who had received BCPR achieved a return of spontaneous circulation (ROSC) in the field significantly more often than non-BCPR recipients-26 versus 16% (P=0.006). But the likelihood of being admitted to a hospital bed, and discharged alive, was only marginally better for BCPR recipients. To examine the influence of BCPR on the presenting rhythm an event tree showed that in 48% of witnessed BCPR cases the presenting rhythm was VF/VT, whereas, for witnessed non-BCPR cases, 27% were in VF/VT (P<0.0001). With unwitnessed arrests, 31% of BCPR cases were in VF/VT compared with 18% for non-BCPR cases (P<0.0001). Call to scene time was less than 8 min for 66% of all VF/VT arrests. CONCLUSION: The event trees, when combined with the Utstein template, demonstrated the importance of examining comprehensively datasets for both witnessed and unwitnessed cardiac arrests when monitoring performance standards. The analyses also emphasised the relevance of community programmes in Greater London for teaching basic life saving skills.

Allied Health Personnel↗
PubMed2004

Bayes without priors.

BACKGROUND AND OBJECTIVES: Classical statistical inference has attained a dominant position in the expression and interpretation of empirical results in biomedicine. Although there have been critics of the methods of hypothesis testing, significance testing (P-values), and confidence intervals, these methods are used to the exclusion of all others. METHODS: An alternative metaphor and inferential computation based on credibility is offered here. RESULTS: It is illustrated in three datasets involving incidence rates, and its advantages over both classical frequentist inference and Bayesian inference, are detailed. CONCLUSION: The message is that for those who are unsatisfied with classical methods but cannot make the transition to Bayesianism, there is an alternative path.

Arizona↗
PubMed2004

Experimental evaluation of gamma fluence-rate predictions from Argon-41 releases to the atmosphere over a nuclear research reactor site.

An experimental study of radionuclide dispersion in the atmosphere has been conducted at the BR1 research reactor in Mol, Belgium. Artificially generated aerosols ('white smoke') were mixed with the routine releases of (41)Ar in the reactor's 60-m tall venting stack. The detailed plume geometry was measured with remote sensing (Lidar) of the aerosol plumes while surface radiation levels were measured under the plume using gamma detectors at downwind distances of up to 1500 m from the release point. A database was built with simultaneous measurements of plume geometry and radiation field from (41)Ar decay, together with in-situ measurements of meteorological parameters. The joint tracer/radiation experimental dataset has been subsequently used to evaluate the accuracy of predictions of dispersion parameters and gamma fluence rates obtained by the atmospheric dispersion and dose rate model RIMPUFF.

Air Pollutants, Radioactive↗
PubMed2002

Peptide mass fingerprinting peak intensity prediction: extracting knowledge from spectra.

Matrix-assisted laser desorption/ionization-time of flight mass spectrometry has become a valuable tool in proteomics. With the increasing acquisition rate of mass spectrometers, one of the major issues is the development of accurate, efficient and automatic peptide mass fingerprinting (PMF) identification tools. Current tools are mostly based on counting the number of experimental peptide masses matching with theoretical masses. Almost all of them use additional criteria such as isoelectric point, molecular weight, PTMs, taxonomy or enzymatic cleavage rules to enhance prediction performance. However, these identification tools seldom use peak intensities as parameter as there is currently no model predicting the intensities based on the physicochemical properties of peptides. In this work, we used standard datamining methods such as classification and regression methods to find correlations between peak intensities and the properties of the peptides composing a PMF spectrum. These methods were applied on a dataset comprising a series of PMF experiments involving 157 proteins. We found that the C4.5 method gave the more informative results for the classification task (prediction of the presence or absence of a peptide in a spectra) and M5' for the regression methods (prediction of the normalized intensity of a peptide peak). The C4.5 result correctly classified 88% of the theoretical peaks; whereas the M5' peak intensities had a correlation coefficient of 0.6743 with the experimental peak intensities. These methods enabled us to obtain decision and model trees that can be directly used for prediction and identification of PMF results. The work performed permitted to lay the foundations of a method to analyze factors influencing the peak intensity of PMF spectra. A simple extension of this analysis could lead to improve the accuracy of the results by using a larger dataset. Additional peptide characteristics or even PMF experimental parameters can also be taken into account in the datamining process to analyze their influence on the peak intensity. Furthermore, this datamining approach can certainly be extended to the tandem mass spectrometry domain or other mass spectrometry derived methods.

Acrylamide↗
PubMed2004

Adjusting for patient selection suggests the addition of docetaxel to 5-fluorouracil-cisplatin induction therapy may offer survival benefit in squamous cell cancer of the head and neck.

When induction chemotherapy is used in locally advanced squamous cell cancer of the head and neck (SCCHN), patients often receive cisplatin-5-fluorouracil (PF) followed by radical loco-regional therapy. Phase II studies of docetaxel-cisplatin-5-fluorouracil (TPF) induction therapy, with or without leucovorin (L), have achieved high survival rates versus those reported in phase III PF trials. However, the distribution of prognostic factors may vary between phase II and phase III study populations, making the extrapolation of phase II TPF/L results to phase III PF populations difficult. This study used a patient selection standardization method and Cox model to adjust for potential selection bias. Thus, the survival benefit from adding docetaxel into PF induction regimens in SCCHN could be more accurately assessed. The TPF/L dataset comprised 195 patients from six phase II trials. The PF dataset of 585 patients was derived from five large randomized trials included in the Meta-Analysis of Chemotherapy in Head and Neck Cancer (MACH-NC) database. TPF/L and PF datasets differed significantly concerning the distribution of several prognostic factors. Adjusting for these differences, the relative risk of death in the PF versus TPF/L datasets was 1.85 (95% confidence interval 1.37-2.49), corresponding to a 20% 2-year survival benefit (p < 0.0001). Sensitivity analyses confirmed that this improved 2-year survival rate of TPF/L over PF was robust, irrespective of the distribution of studied prognostic factors between treatment datasets. We conclude that this improved survival might be due either to docetaxel's pharmacologic effect or to uncontrolled prognostic factors.

Antineoplastic Combined Chemotherapy Protocols↗
PubMed1998

A quantile plot for simultaneous representation of clinical and statistical attributes of probing change: application to early identification of the downhill patient.

When multiple periodontal sites are observed in patients over time there is an intention to identify those sites where there is important change, typically loss of attachment or increase in probing depth. A change may be declared if it: (a) exceeds a threshold level, and/or (b) is determined to be statistically significant (e.g. regression slope different from zero), perhaps after (c) that significance level has been corrected for multiple testing. These criteria are not often considered when clinical or research decisions are made and there is no universal protocol for their evaluation. A quantile (uniform probability) plot, modified to incorporate additional information, is proposed as a graphical method for the display of changes at multiple sites within a mouth. This plot identifies, for each site, clinical changes beyond a threshold, site-wise statistical significance and statistical significance adjusted for multiple testing. These alternative criteria for attachment change are, thereby, made explicit, providing a detailed evaluative context. In addition, this methodology permits incorporation of an estimation procedure for the number of sites for which the null hypothesis of no change is false. This statistic can provide evidence of progressive disease even when no site has significant clinical or statistical change and even if the average change is zero. Use of the quantile plot was elucidated by application to simulated data, and to a clinical dataset using a BASIC program to automate the computational process. In the clinical example presented, the approach appeared more effective in detecting periodontal change than traditional clinical and statistical criteria. Pending technical refinement, this graphical approach may represent a new tool for the early identification of the downhill patient.

Adult↗
PubMed2026

A note on a generalized single step theory for any number of hierarchical genomic matrices.

BACKGROUND: The Single Step algorithm allows combining information from genotyped and un-genotyped individuals, provided they are connected by a pedigree. However, current single step theory is limited to a single list of markers. RESULTS: We present a generalized single step (GSS) method that can accommodate any number of hierarchical molecular datasets (e.g. sequence, high and low density arrays) and pedigree, avoiding imputation. We prove that a similar efficient inversion algorithm exists. The method is recursive, starting with the highest marker density scenario. We illustrate the method with simulation and show that GSS can increase predictive accuracy compared to standard single step. R code is provided so that custom scenarios can be easily compared, either with simulated or real data. CONCLUSION: The method developed generalizes extant single step theory to any number of hierarchical molecular relationship matrices, broadening the scenarios where single step can be applied. A topic of particular interest can be ecology field data or human populations where pedigree is not available, but where samples sequenced and genotyped at different densities can exist. GSS can also be a useful tool to optimize allocation of genotyping and / or sequencing resources.

Algorithms↗
PubMedDate not supplied

The inadequacy of using means to compare medical costs of smokers and nonsmokers.

The costs associated with health risks have become a primary focus in the field of health promotion. Unfortunately, the nature and quality of medical cost data are not ideal for simple comparisons of health risk groups. Most health promotion professionals have had little or no training on how to interpret cost data and are thus unaware of the limitations and problems inherent in cost comparisons. This report illustrates the problems encountered when using cost data, using examples from a medical claims dataset. Specifically, the potential errors that result from comparing group means are shown. A set of alternatives for practitioners and researchers to consider when comparing costs for different groups are offered.

Adolescent↗
PubMed2001

Robustness and generalizability of smallest detectable difference in radiological progression.

The smallest detectable difference (SDD) reflects that component of a measure statistically attributable to error from the measurement process itself. As such it is an irreducible component of the inherent variability in measurements in clinical trials and will affect their design, whether randomized or observational. Even though the application of the SDD concept to assaying radiographs in rheumatoid arthritis is relatively new and not well understood, systematic work on the influences of radiographic SDD can be done. This report describes the effects of a number of clinical aspects of the disease and operational aspects of trials on the values of the SDD of radiographic progression data. We show that if conditions affecting SDD are known and kept constant across datasets, the SDD of radiological progression from one study may be generalizable to other studies. However, if any one condition varies, the SDD is distinctly unrobust and cannot be generalized to other studies.

Arthritis, Rheumatoid↗
PubMed1997

Evaluating the terminology requirements to support multi-disciplinary diabetes care.

Diabetes mellitus is a multi-system disease requiring lifetime multi-disciplinary care, which has proven individual and economic benefits. The delivery of service involves co-operation and communication between patient, carer and health care professionals, and systematic auditing of processes and outcomes. Sustained improvement necessitates regular data acquisition, aggregation and analysis. The terminology requirements to support patient-centred records and identified datasets are examined, and differences in purpose and scope highlighted. The many stakeholders involved in diabetes care have their own sublanguages and terminology requirements which need harmonising around a common core. The problems and solutions of accommodating these needs are explored in relation to the Read Thesaurus.

Databases, Factual↗
PubMed2000

Versailles minimal dataset for diagnosis of ALS: a distillate of the 2nd Consensus Conference on accelerating the diagnosis of ALS. Versailles 2nd Consensus Conference participants.

The 2nd Consensus Conference (Versailles) recommended that an ALS knowledge-base for initial healthcare providers, diagnosing neurologists and confirming neurologists should be defined to include a simplified version of diagnostic criteria less formal than the World Federation of Neurology El Escorial Revisted Criteria ('ALS diagnosis - An algorithm'), a set of rules concerning red flags which should increase the suspicion of ALS as the diagnosis and minimize the time between suspicion and referral for confirmation of diagnosis ('ALS axioms of referral'), as well as a site of symptom onset-specific checklist of minimal clinical examination, neuroimaging, electrodiagnostic, pulmonary function and laboratory test information required to confirm the diagnosis of ALS ('Versailles minimal dataset'). Although introductory discussions addressed the advantages and disadvantages of earlier diagnosis, false-positive or false-negative diagnosis, the frequency of follow-up and what potential biological markers to be followed, these issues will have to be further evaluated at future consensus conferences.

Diagnosis, Differential↗
PubMed1993

Final clustering.

Final statistical cluster analysis was performed on 51 target cells. These included three new complete datasets that were submitted to the workshop in time for the final cluster analysis. Two human cell lines that were used for the initial temporary clustering were omitted from the final targets. Most temporary clusters remained unchanged but several, previously loosely clustered, mAbs no longer formed clusters.

Animals↗
PubMed2004

Normalization of cDNA microarray data using wavelet regressions.

Normalization is an essential step in microarray data mining and analysis. For cDNA microarray data, the primary purpose of normalization is removing the intensity-dependent bias across different slides within an experimental group or between multiple groups. The locally weighted regression (lowess) procedure has been widely used for this purpose but can be comparatively time consuming when the dataset becomes relatively large. In this study, we applied wavelet regressions, a new smoothing method for recovering a regression function from data that is supposed to outperform other methods in many cases, such as spline or local polynomial fitting, to normalize two cDNA microarray datasets. Relative to the lowess procedure, we found that wavelet regressions not only produced reliable normalization results but also ran much faster. The computing speed represents one of the most important advantages over other algorithms, especially when one is interested in analyzing a large microarray experiment involving hundreds of slides.

Algorithms↗
PubMed2005

Toward resolution of cardiovascular health disparities in African Americans: design and methods of the Jackson Heart Study.

OBJECTIVE: The design, overall methods, and major phenotypes for the all-African-American Jackson Heart Study (JHS) are detailed. METHODS: Participants were enrolled from the three counties that make up the Jackson, Mississippi metropolitan area. Relatives of selected participants were recruited to develop a large, nested family cohort. Participants provided extensive medical and social history, had an array of physical and biochemical measurements and diagnostic procedures, and provided genomic DNA. RESULTS: Data and biologic materials have been collected from 5302 adult African Americans, including 1499 members of 291 families. Participants have a high prevalence of diabetes, hypertension, obesity, and related disorders. DISCUSSION: The JHS dataset and biologic materials (serum, DNA, and cryopreserved cells) offer a valuable resource for the study of diseases that are of particular importance to African Americans.

Adult↗