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Добірка наукової літератури з теми "Medicine Statistic"

Автор: Grafiati

Опубліковано: 11 березня 2023

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Статті в журналах з теми "Medicine Statistic"

Chen, Dechang, Zhenqiu Liu, Xiaobin Ma, and Dong Hua. "Selecting Genes by Test Statistics." Journal of Biomedicine and Biotechnology 2005, no. 2 (2005): 132–38. http://dx.doi.org/10.1155/jbb.2005.132.

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Gene selection is an important issue in analyzing multiclass microarray data. Among many proposed selection methods, the traditional ANOVA F test statistic has been employed to identify informative genes for both class prediction (classification) and discovery problems. However, the F test statistic assumes an equal variance. This assumption may not be realistic for gene expression data. This paper explores other alternative test statistics which can handle heterogeneity of the variances. We study five such test statistics, which include Brown-Forsythe test statistic and Welch test statistic. Their performance is evaluated and compared with that of F statistic over different classification methods applied to publicly available microarray datasets.

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Carpenter, C. R. "Kappa statistic." Canadian Medical Association Journal 173, no. 1 (July 5, 2005): 15–16. http://dx.doi.org/10.1503/cmaj.1041742.

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Juurlink, D. N. "Kappa statistic." Canadian Medical Association Journal 173, no. 1 (July 5, 2005): 16. http://dx.doi.org/10.1503/cmaj.1041744.

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Allan, G. M. "Kappa statistic." Canadian Medical Association Journal 173, no. 1 (July 5, 2005): 16–17. http://dx.doi.org/10.1503/cmaj.1050010.

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McGinn, T. "Kappa statistic." Canadian Medical Association Journal 173, no. 1 (July 5, 2005): 17. http://dx.doi.org/10.1503/cmaj.1050048.

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Curran-Everett, Douglas. "Explorations in statistics: hypothesis tests and P values." Advances in Physiology Education 33, no. 2 (June 2009): 81–86. http://dx.doi.org/10.1152/advan.90218.2008.

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Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This second installment of Explorations in Statistics delves into test statistics and P values, two concepts fundamental to the test of a scientific null hypothesis. The essence of a test statistic is that it compares what we observe in the experiment to what we expect to see if the null hypothesis is true. The P value associated with the magnitude of that test statistic answers this question: if the null hypothesis is true, what proportion of possible values of the test statistic are at least as extreme as the one I got? Although statisticians continue to stress the limitations of hypothesis tests, there are two realities we must acknowledge: hypothesis tests are ingrained within science, and the simple test of a null hypothesis can be useful. As a result, it behooves us to explore the notions of hypothesis tests, test statistics, and P values.

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Fletcher, J. "Choosing a statistic." BMJ 339, jul08 2 (July 8, 2009): b2702. http://dx.doi.org/10.1136/bmj.b2702.

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Phillips, G. "A worrying statistic." BMJ 312, no. 7046 (June 22, 1996): 1586. http://dx.doi.org/10.1136/bmj.312.7046.1586.

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Berry, C. C. "The kappa statistic." JAMA: The Journal of the American Medical Association 268, no. 18 (November 11, 1992): 2513–14. http://dx.doi.org/10.1001/jama.268.18.2513.

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Holmes, A. P., R. C. Blair, J. D. G. Watson, and I. Ford. "Nonparametric Analysis of Statistic Images from Functional Mapping Experiments." Journal of Cerebral Blood Flow & Metabolism 16, no. 1 (January 1996): 7–22. http://dx.doi.org/10.1097/00004647-199601000-00002.

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The analysis of functional mapping experiments in positron emission tomography involves the formation of images displaying the values of a suitable statistic, summarising the evidence in the data for a particular effect at each voxel. These statistic images must then be scrutinised to locate regions showing statistically significant effects. The methods most commonly used are parametric, assuming a particular form of probability distribution for the voxel values in the statistic image. Scientific hypotheses, formulated in terms of parameters describing these distributions, are then tested on the basis of the assumptions. Images of statistics are usually considered as lattice representations of continuous random fields. These are more amenable to statistical analysis. There are various shortcomings associated with these methods of analysis. The many assumptions and approximations involved may not be true. The low numbers of subjects and scans, in typical experiments, lead to noisy statistic images with low degrees of freedom, which are not well approximated by continuous random fields. Thus, the methods are only approximately valid at best and are most suspect in single-subject studies. In contrast to the existing methods, we present a nonparametric approach to significance testing for statistic images from activation studies. Formal assumptions are replaced by a computationally expensive approach. In a simple rest-activation study, if there is really no activation effect, the labelling of the scans as “active” or “rest” is artificial, and a statistic image formed with some other labelling is as likely as the observed one. Thus, considering all possible relabellings, a p value can be computed for any suitable statistic describing the statistic image. Consideration of the maximal statistic leads to a simple nonparametric single-threshold test. This randomisation test relies only on minimal assumptions about the design of the experiment, is (almost) exact, with Type I error (almost) exactly that specified, and hence is always valid. The absence of distributional assumptions permits the consideration of a wide range of test statistics, for instance, “pseudo” t statistic images formed with smoothed variance images. The approach presented extends easily to other paradigms, permitting nonparametric analysis of most functional mapping experiments. When the assumptions of the parametric methods are true, these new nonparametric methods, at worst, provide for their validation. When the assumptions of the parametric methods are dubious, the nonparametric methods provide the only analysis that can be guaranteed valid and exact.

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Дисертації з теми "Medicine Statistic"

Harite, Shibani. "Evaluation of 10-fold cross validation and prediction error sums of squares statistic for population pharmacokinetic model validation." Scholarly Commons, 2003. https://scholarlycommons.pacific.edu/uop_etds/585.

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It was the objective of the current study to evaluate the ability of 10-fold crossvalidation and prediction error sum of squares (PRESS) statistic to identify population pharmacokinetic models (PPKM) that were estimated from data without influence observations versus PPKMs from data containing influence observations. The evaluation of 10-fold cross validation and PRESS statistic from Leave-one-out cross-validation for PPK model validation was performed in 3 Phases. In Phase 1 model parameters (theta and clearance) were estimated for datasets with and without influence observations. It was found that influence observations caused an over-estimation of the model parameters. In Phase II the statistics from 10-fold and leave-one-out cross validation methods were used to detect models developed from influence data. The metrics of choice are RATIOK and RATIOPR statistics that can be used to identify models developed from influence data and these metrics may then find applicability across differing drugs and models. A cut-off value of 1.05 for RATIOK and RATIOPR was proposed as a discrete breakpoint to classify models that were generated from influence data versus noninfluence data. In Phase III data analysis was carried out using logistic regression and the sensitivity and specificity of Leave-one-out and 10-fold cross-validation methods were evaluated. It was found that RATIOK and RATIOPR were significant predictors when used individually in the model. Multicollinearity was detected when RATIOK and RATIOPR were present in the model at the same time. In terms of sensitivity and specificity both 10-fold cross validation and leave-one-out cross validation showed similar performance.

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Tillmar, Andreas. "Populations and Statistics in Forensic Genetics." Doctoral thesis, Linköpings universitet, Institutionen för klinisk och experimentell medicin, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-54742.

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DNA has become a powerful forensic tool for solving cases such as linking a suspect to a crime scene, resolving biological relationship issues and identifying disaster victims. Traditionally, DNA investigations mainly involve two steps; the establishment of DNA profiles from biological samples and the interpreta-tion of the evidential weight given by theses DNA profiles. This thesis deals with the latter, with focus on models for assessing the weight of evidence and the study of parameters affecting these probability figures. In order to calculate the correct representative weight of DNA evidence, prior knowledge about the DNA markers for a relevant population sample is required. Important properties that should be studied are, for example, how frequently certain DNA-variants (i.e. alleles) occur in the population, the differences in such frequencies between subpopulations, expected inheritance patterns of the DNA markers within a family and the forensic efficiency of the DNA markers in casework. In this thesis we aimed to study important population genetic parameters that influence the weight of evidence given by a DNA-analysis, as well as models for proper consideration of such parameters when calculating the weight of evi-dence in relationship testing. We have established a Swedish frequency database for mitochondrial DNA haplotypes and a haplotype frequency database for markers located on the X-chromosome. Furthermore, mtDNA haplotype frequencies were used to study the genetic variation within Sweden, and between Swedish and other European populations. No genetic substructure was found in Sweden, but strong similari-ties with other western European populations were observed. Genetic properties such as linkage and linkage disequilibrium could be im-portant when using X-chromosomal markers in relationship testing. This was true for the set of markers that we studied. In order to account for this, we pro-posed a model for how to take linkage and linkage disequilibrium into account when calculating the weight of evidence provided by X-chromosomal analysis. Finally, we investigated the risk of erroneous decisions when using DNA in-vestigations for family reunification. We showed that the risk is increased due to uncertainties regarding population allele frequencies, consanguinity and compet-ing close relationship between the tested individuals. Additional information and the use of a refined model for the alternative hypotheses reduced the risk of making erroneous decisions. In summary, as a result of the work on this thesis, we can use mitochondrial DNA and X-chromosome markers in order to resolve complex relationship in-vestigations. Moreover, the reliability of likelihood estimates has been increased by the development of models and the study of relevant parameters affecting probability calculations.

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Manrai, Arjun Kumar. "Statistical foundations for precision medicine." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/97826.

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Анотація:

Thesis: Ph. D., Harvard-MIT Program in Health Sciences and Technology, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references.
Physicians must often diagnose their patients using disease archetypes that are based on symptoms as opposed to underlying pathophysiology. The growing concept of "precision medicine" addresses this challenge by recognizing the vast yet fractured state of biomedical data, and calls for a patient-centered view of data in which molecular, clinical, and environmental measurements are stored in large shareable databases. Such efforts have already enabled large-scale knowledge advancement, but they also risk enabling large-scale misuse. In this thesis, I explore several statistical opportunities and challenges central to clinical decision-making and knowledge advancement with these resources. I use the inherited heart disease hypertrophic cardiomyopathy (HCM) to illustrate these concepts. HCM has proven tractable to genomic sequencing, which guides risk stratification for family members and tailors therapy for some patients. However, these benefits carry risks. I show how genomic misclassifications can disproportionately affect African Americans, amplifying healthcare disparities. These findings highlight the value of diverse population sequencing data, which can prevent variant misclassifications by identifying ancestry informative yet clinically uninformative markers. As decision-making for the individual patient follows from knowledge discovery by the community, I introduce a new quantity called the "dataset positive predictive value" (dPPV) to quantify reproducibility when many research teams separately mine a shared dataset, a growing practice that mirrors genomic testing in scale but not synchrony. I address only a few of the many challenges of delivering sound interpretation of genetic variation in the clinic and the challenges of knowledge discovery with shared "big data." These examples nonetheless serve to illustrate the need for grounded statistical approaches to reliably use these powerful new resources.
by Arjun Kumar Manrai.
Ph. D.

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Valencia, Arboleda Carlos Felipe. "Contributions to statistical learning and its applications in personalized medicine." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49143.

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This dissertation, in general, is about finding stable solutions to statistical models with very large number of parameters and to analyze their asymptotic statistical properties. In particular, it is centered in the study of regularization methods based on penalized estimation. Those procedures find an estimator that is the result of an optimization problem balancing out the fitting to the data with the plausability of the estimation. The first chapter studies a smoothness regularization estimator for an infinite dimensional parameter in an exponential family model with functional predictors. We focused on the Reproducing Kernel Hilbert space approach and show that regardless the generality of the method, minimax optimal convergence rates are achieved. In order to derive the asymptotic analysis of the estimator, we developed a simultaneous diagonalization tool for two positive definite operators: the kernel operator and the operator defined by the second Frechet derivative of the expected data t functional. By using the proposed simultaneous diagonalization tool sharper bounds on the minimax rates are obtained. The second chapter studies the statistical properties of the method of regularization using Radial Basis Functions in the context of linear inverse problems. The regularization here serves two purposes, one is creating a stable solution for the inverse problem and the other is prevent the over-fitting on the nonparametric estimation of the functional target. Different degrees for the ill-posedness in the inversion of the operator A are considered: mildly and severely ill-posed. Also, we study different types for radial basis kernels classifieded by the strength of the penalization norm: Gaussian, Multiquadrics and Spline type of kernels. The third chapter deals with the problem of Individualized Treatment Rule (ITR) and analyzes the solution of it through Discriminant Analysis. In the ITR problem, the treatment assignment is done based on the particular patient's prognosis covariates in order to maximizes some reward function. Data generated from a random clinical trial is considered. Maximizing the empirical value function is an NP-hard computational problem. We consider estimating directly the decision rule by maximizing the expected value, using a surrogate function in order to make the optimization problem computationally feasible (convex programming). Necessary and sufficient conditions for Infinite Sample Consistency on the surrogate function are found for different scenarios: binary treatment selection, treatment selection with withholding and multi-treatment selection.

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Wegwarth, Odette [Verfasser]. "Statistical literacy in medicine : physicians’ and patients’ understanding of health statistics in cancer screening and prevention / Odette Wegwarth." Berlin : Medizinische Fakultät Charité - Universitätsmedizin Berlin, 2015. http://d-nb.info/1073869067/34.

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Shen, Yuanyuan. "Ordinal Outcome Prediction and Treatment Selection in Personalized Medicine." Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:17463982.

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In personalized medicine, two important tasks are predicting disease risk and selecting appropriate treatments for individuals based on their baseline information. The dissertation focuses on providing improved risk prediction for ordinal outcome data and proposing score-based test to identify informative markers for treatment selection. In Chapter 1, we take up the first problem and propose a disease risk prediction model for ordinal outcomes. Traditional ordinal outcome models leave out intermediate models which may lead to suboptimal prediction performance; they also don't allow for non-linear covariate effects. To overcome these, a continuation ratio kernel machine (CRKM) model is proposed both to let the data reveal the underlying model and to capture potential non-linearity effect among predictors, so that the prediction accuracy is maximized. In Chapter 2, we seek to develop a kernel machine (KM) score test that can efficiently identify markers that are predictive of treatment difference. This new approach overcomes the shortcomings of the standard Wald test, which is scale-dependent and only take into account linear effect among predictors. To do this, we propose a model-free score test statistics and implement the KM framework. Simulations and real data applications demonstrated the advantage of our methods over the Wald test. In Chapter 3, based on the procedure proposed in Chapter 2, we further add sparsity assumption on the predictors to take into account the real world problem of sparse signal. We incorporate the generalized higher criticism (GHC) to threshold the signals in a group and maintain a high detecting power. A comprehensive comparison of the procedures in Chapter 2 and Chapter 3 demonstrated the advantages and disadvantages of difference procedures under different scenarios.
Biostatistics

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Yong, Florence Hiu-Ling. "Quantitative Methods for Stratified Medicine." Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:17463130.

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Stratified medicine has tremendous potential to deliver more effective therapeutic intervention to improve public health. For practical implementation, reliable prediction models and clinically meaningful categorization of some comprehensible summary measures of individual treatment effect are vital elements to aid the decision-making process and bring stratified medicine to fruitful realization. We tackle the quantitative issues involved from three fronts : 1) prediction model building and selection; 2) reproducibility assessment; and 3) stratification. First, we propose a systematic model development strategy that integrates cross-validation and predictive accuracy measures in the prediction model building and selection process. Valid inference is made possible via internal holdout sample or external data evaluation to enhance generalizability of the selected prediction model. Second, we employ parametric or semi-parametric modeling to derive individual treatment effect scoring systems. We introduce a stratification algorithm with constrained optimization by utilizing dynamic programming and supervised-learning techniques to group patients into different actionable categories. We integrate the stratification and newly proposed prediction performance metric into the model development process. The methodologies are first presented in single treatment case, and then extended to two treatment cases. Finally, adapting the concept of uplift modeling, we provide a framework to identify the subgroup(s) with the most beneficial prospect; wasteful, harmful, and futile subgroups to save resources and reduce unnecessary exposure to treatment adverse effects. The proposals are illustrated by AIDS clinical study data and cardiology studies for non-censored and censored outcomes. The contribution of this dissertation is to provide an operational framework to bridge predictive modeling and decision making for more practical applications in stratified medicine.
Biostatistics

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Appleton, David Robertson. "Application of statistics to medicine, particularly the study of cell proliferation." Thesis, University of Edinburgh, 1990. http://hdl.handle.net/1842/27766.

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Asar, Özgür. "Longitudinal and survival statistical methods with applications in renal medicine." Thesis, Lancaster University, 2015. http://eprints.lancs.ac.uk/75552/.

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In this thesis, we develop statistical methodology to find solutions to contemporary problems in renal research. These problems include 1) assessing the association of the underlying kidney function and the risk of survival events, 2) early detection of progression towards renal failure amongst primary care patients, and 3) long-term influences of acute kidney injury occurrences on the subsequent kidney function. Joint modelling of longitudinal and time-to-event outcome and Cox model with time-varying covariate are considered to answer the first problem. Whilst parameters are estimated by maximum likelihood (ML) using an expectation-maximisation (EM) algorithm for the former model, by partial likelihood for the latter. Results show that Cox model underestimates the association parameter between the longitudinal and survival processes, and joint models correct this. A longitudinal model with a non-stationary stochastic process is developed for the second problem. Parameters are estimated by ML using a Fisher-Scoring algorithm. Based on the results of this model, we obtain the predictive distribution of meeting the clinical guideline for detecting progression. Results show that there are patients with very high probability and emerging behaviour of progression. By these probabilities, we aim to inform clinical decision-making. Another longitudinal model with a class of stationary stochastic processes and heavy tailed response distribution is developed for the third problem. Parameters are estimated by ML using an EM algorithm, and random effects are predicted using the conditional distribution of random effects given data. Results show that AKI might have serious impacts on kidney function such that on average the loss of kidney function doubles after having an AKI. Nonetheless, there are substantial between patient heterogeneity in terms of this influence. The R package lmenssp which enables inference for a range of mixed models with non-stationary stochastic processes is developed and its core features are presented.

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VIGNALI, GIULIA. "ANALISI STATISTICO-CRIMINOLOGICA DEGLI OMICIDI OCCORSI NEL DISTRETTO GIUDIZIARIO DI MILANO E MONZA NEGLI ANNI 2006-2021." Doctoral thesis, Università degli Studi di Milano, 2023. https://hdl.handle.net/2434/950355.

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A retrospective analysis was conducted of the homicides that occurred in the area under the jurisdiction of the Public Prosecutors of Milan and Monza and were autopsied at the Institute of Forensic Medicine of Milan between 1 January 2006 and 31 December 2021. The data was collected from autopsy reports, interviews with the victims' families, clinical records and local newspapers. In total, of the 11,480 autopsies performed in the period of interest, 301 were found to be attributable to voluntary homicides. 94 (31.23%) crimes occurred within the family context, and 207 (68.77%) outside. The following were evaluated: the personal characteristics of the subjects involved (sex, age, geographical origin); the risk factors present in victims and killers; the characteristics of the event (place, modality). The results were then analyzed by differentiating and comparing the two groups (intra- and extra-family). Statistically significant differences emerged for many of the variables analysed. The people involved in family murders are on average older, almost always Italian and the involvement of women is greater than in the other group (although predominantly men are always involved). Great differences also concerned risk factors: much more often those who kill outside the family context have already committed previous crimes or are involved in illicit trafficking; within the family, however, psychic pathologies such as depression prevail among the perpetrators and illness/non-self-sufficiency among the victims. Finally, in addition to the reliable differences inherent in the place of the crime (those who kill a family member do it inside the shared house, in almost all cases), there was also a greater use of weapons (firearms or bladed weapons) in those who kill outside the family. It can thus be assumed that homicides inside and outside the family have few points in common, and that the typology of subjects involved is substantially different.

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Книги з теми "Medicine Statistic"

Statistics in medicine. 2nd ed. Burlington, MA: Elsevier Academic Press, 2006.

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Colton, Theodore. Statistics in medicine. 2nd ed. [Boston: Little, Brown, 1996.

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Statistics at square two: Understanding modern statistical applications in medicine. 2nd ed. Malden, Mass: BMJ Books/Blackwell Pub., 2006.

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Statistical reasoning in medicine: The intuitive p-value primer. 2nd ed. New York, NY: Springer, 2006.

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F, Dunstan, Pickles J, and Institute of Mathematics and Its Applications., eds. Statistics in medicine: Based on the proceedings of a conference on applications of statistics in medicine. Oxford [England]: Clarendon Press, 1991.

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Moyé, Lemuel A. Statistical Reasoning in Medicine. New York, NY: Springer New York, 2006. http://dx.doi.org/10.1007/978-0-387-46212-7.

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Moyé, Lemuel A. Statistical Reasoning in Medicine. New York, NY: Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4757-3292-4.

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Zhou, Xiao-hua. Statistical methods in diagnostic medicine. 2nd ed. Hoboken, N.J: Wiley, 2011.

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Xiao-hua, Zhou, Obuchowski Nancy A, and McClish Donna K, eds. Statistical methods in diagnostic medicine. New York: Wiley-Interscience, 2002.

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Statistical foundation of inference in medicine: Rationale, technique, strategy. 2nd ed. Rockville, Md: Kabel Publishers, 1993.

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Частини книг з теми "Medicine Statistic"

Nahler, Gerhard. "κ statistic." In Dictionary of Pharmaceutical Medicine, 103. Vienna: Springer Vienna, 2009. http://dx.doi.org/10.1007/978-3-211-89836-9_765.

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HK, Ramakrishna. "Evidence-Based Medicine." In Medical Statistics, 161–66. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1923-4_9.

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Everitt, Brian S. "statistics, statisticians, and medicine." In Chance Rules, 137–56. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-2164-7_10.

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Everitt, Brian. "Statistics, Statisticians and Medicine." In Chance Rules, 135–53. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-77415-2_12.

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Schöffl, Volker, Christoph Lutter, and G. Jones. "Injury Statistics." In Climbing Medicine, 13–25. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-72184-8_2.

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Turner, J. Rick. "Statistics." In Encyclopedia of Behavioral Medicine, 2136–37. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39903-0_1077.

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Turner, J. Rick. "Statistics." In Encyclopedia of Behavioral Medicine, 1878–79. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_1077.

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Allen, Brandon, Latha Ganti, and Bobby Desai. "Statistics." In Quick Hits in Emergency Medicine, 123–24. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7037-3_20.

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Boltz, Marie, Holly Rau, Paula Williams, Holly Rau, Paula Williams, Jane Upton, Jos A. Bosch, et al. "Inferential Statistics." In Encyclopedia of Behavioral Medicine, 1064. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_100887.

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Nahler, Gerhard. "descriptive statistics." In Dictionary of Pharmaceutical Medicine, 49. Vienna: Springer Vienna, 2009. http://dx.doi.org/10.1007/978-3-211-89836-9_372.

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Тези доповідей конференцій з теми "Medicine Statistic"

Liu, W. Y., I. E. Magnin, and G. Gimenez. "A statistic based approach for segmentation." In 1992 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1992. http://dx.doi.org/10.1109/iembs.1992.5762102.

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Liu, Magnin, and Gimenez. "A Statistic Based Approach For Segmentation." In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1992. http://dx.doi.org/10.1109/iembs.1992.590265.

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Shen, S., A. Sterr, and A. Szameitat. "A Template Effect Study on Voxel-Based Morphometry in Statistic Parametric Mapping." In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2005. http://dx.doi.org/10.1109/iembs.2005.1617118.

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Yang, Bo, Tingting Cao, Wenfeng Zheng, and Shan Liu. "Motion Tracking for Beating Heart Based on Sparse Statistic Pose Modeling." In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2018. http://dx.doi.org/10.1109/embc.2018.8512404.

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Diaz-Parra, Antonio, Ursula Perez-Ramirez, Jesus Pacheco-Torres, Simone Pfarr, Wolfgang H. Sommer, David Moratal, and Santiago Canals. "Evaluating network brain connectivity in alcohol postdependent state using Network-Based Statistic." In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2017. http://dx.doi.org/10.1109/embc.2017.8036879.

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Heng Li, Zhiwen Liu, Xing An, and Yonggang Shi. "Multi-classification of cell deformation based on object alignment and run length statistic." In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2014. http://dx.doi.org/10.1109/embc.2014.6944347.

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Peng, Genchang, Mehrdad Nourani, Jay Harvey, and Hina Dave. "Feature Selection Using F-statistic Values for EEG Signal Analysis." In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society. IEEE, 2020. http://dx.doi.org/10.1109/embc44109.2020.9176434.

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Toppi, J., F. Babiloni, G. Vecchiato, F. Cincotti, F. De Vico Fallani, D. Mattia, S. Salinari, and L. Astolfi. "Testing the asymptotic statistic for the assessment of the significance of partial directed coherence connectivity patterns." In 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2011. http://dx.doi.org/10.1109/iembs.2011.6091243.

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Symonenko, R. V., and V. О. Kofanov. "The statistic investigation on approaches in selection different diagnostic methods in the treatment of patients with generalized periodontitis used by dentists. Results of anonymous questionnaires." In THE CONCEPT OF MODERN PHARMACY AND MEDICINE IN UKRAINE AND EU COUNTRIES. Baltija Publishing, 2021. http://dx.doi.org/10.30525/978-9934-26-113-8-13.

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Noguchi, Hiroshi, Maki Miyahara, Soo In Kang, Shuhei Noyori, Toshiaki Takahashi, Hiromi Sanada, and Taketoshi Mori. "Bayesian statistic model for nurse call data considering time-series, individual patient variabilities and massive zero-count call data." In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society. IEEE, 2020. http://dx.doi.org/10.1109/embc44109.2020.9176336.

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Звіти організацій з теми "Medicine Statistic"

Desai, Manisha, Maria Montez-Rath, Kris Kapphahn, Aya Mitani, Albee Ling, Maya Mathur, Natasha Purington, Ariadna Garcia, Vilija Joyce, and Doug Owens. Comparing Statistical Models That Predict if Patients Will Take a New Medicine as Directed. Patient-Centered Outcomes Research Institute® (PCORI), May 2020. http://dx.doi.org/10.25302/05.2020.me.13035989.

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gagne, Joshua, Moa Lee, Ajinkya Pawar, and Yaa-Hui Dong. Comparing Statistical Models That Predict if Patients Will Take a New Medicine as Directed. Patient-Centered Outcomes Research Institute® (PCORI), July 2020. http://dx.doi.org/10.25302/05.2020.me.130906274.

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Tang, Jiqin, Gong Zhang, Jinxiao Xing, Ying Yu, and Tao Han. Network Meta-analysis of Heat-clearing and Detoxifying Oral Liquid of Chinese Medicines in Treatment of Children’s Hand-foot-mouth Disease：a protocol for systematic review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, January 2022. http://dx.doi.org/10.37766/inplasy2022.1.0032.

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Review question / Objective: The type of study was clinical randomized controlled trial (RCT). The object of study is the patients with HFMD. There is no limit to gender and race. In the case of clear diagnosis standard, curative effect judgment standard and consistent baseline treatment, the experimental group was treated with pure oral liquid of traditional Chinese medicine(A: Fuganlin oral liquid, B: huangzhihua oral liquid, C: Lanqin oral liquid, D: antiviral oral liquid, E: Huangqin oral liquid, F: Pudilan oral liquid, G: Shuanghuanglian oral liquid.)and the control group was treated with ribavirin or any oral liquid of traditional Chinese medicine. The data were extracted by two researchers independently, cross checked and reviewed according to the pre-determined tables. The data extraction content is (1) Basic information (including the first author, published journal and year, research topic). (2) Relevant information (including number of cases, total number of cases, gender, age, intervention measures, course of treatment of the experimental group and the control group in the literature). (3) Design type and quality evaluation information of the included literature. (4) Outcome measures (effective rate, healing time of oral ulcer, regression time of hand and foot rash, regression time of fever, adverse reactions.). The seven traditional Chinese medicine oral liquids are comparable in clinical practice, but their actual clinical efficacy is lack of evidence-based basis. Therefore, the purpose of this study is to use the network meta-analysis method to integrate the clinical relevant evidence of direct and indirect comparative relationship, to make quantitative comprehensive statistical analysis and sequencing of different oral liquid of traditional Chinese medicine with the same evidence body for the treatment of the disease, and then to explore the advantages and disadvantages of the efficacy and safety of different oral liquid of traditional Chinese medicine to get the best treatment plan, so as to provide reference value and evidence-based medicine evidence for clinical optimization of drug selection. Condition being studied: Hand foot mouth disease (HFMD) is a common infectious disease in pediatrics caused by a variety of enteroviruses. Its clinical manifestations are mainly characterized by persistent fever, hand foot rash, oral herpes, ulcers, etc. Because it is often found in preschool children, its immune system development is not perfect, so it is very vulnerable to infection by pathogens and epidemic diseases, resulting in rapid progress of the disease. A few patients will also have neurogenic pulmonary edema Meningitis, myocarditis and other serious complications even lead to death, so effectively improve the cure rate, shorten the course of disease, prevent the deterioration of the disease as the focus of the study. In recent years, traditional Chinese medicine has played an important role in the research of antiviral treatment. Many clinical practices have confirmed that oral liquid of traditional Chinese medicine can effectively play the role of antiviral and improve the body's immunity.

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Volkova, Nataliia P., Nina O. Rizun, and Maryna V. Nehrey. Data science: opportunities to transform education. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3241.

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The article concerns the issue of data science tools implementation, including the text mining and natural language processing algorithms for increasing the value of high education for development modern and technologically flexible society. Data science is the field of study that involves tools, algorithms, and knowledge of math and statistics to discover knowledge from the raw data. Data science is developing fast and penetrating all spheres of life. More people understand the importance of the science of data and the need for implementation in everyday life. Data science is used in business for business analytics and production, in sales for offerings and, for sales forecasting, in marketing for customizing customers, and recommendations on purchasing, digital marketing, in banking and insurance for risk assessment, fraud detection, scoring, and in medicine for disease forecasting, process automation and patient health monitoring, in tourism in the field of price analysis, flight safety, opinion mining etc. However, data science applications in education have been relatively limited, and many opportunities for advancing the fields still unexplored.

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Gindi, Renee. Health, United States, 2019. Centers for Disease Control and Prevention (U.S.), 2021. http://dx.doi.org/10.15620/cdc:100685.

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Health, United States, 2019 is the 43rd report on the health status of the nation and is submitted by the Secretary of the Department of Health and Human Services to the President and the Congress of the United States in compliance with Section 308 of the Public Health Service Act. This report was compiled by the National Center for Health Statistics (NCHS) of the Centers for Disease Control and Prevention (CDC). The Health, United States series presents an annual overview of national trends in key health indicators. The 2019 report presents trends and current information on selected measures of morbidity, mortality, health care utilization and access, health risk factors, prevention, health insurance, and personal health care expenditures in a 20-figure chartbook. The Health, United States, 2019 Chartbook is supplemented by several other products including Trend Tables, an At-a-Glance table, and Appendixes available for download on the Health, United States website at: https://www.cdc.gov/nchs/hus/ index.htm. The Health, United States, 2019 Chartbook contains 20 figures and 20 tables on health and health care in the United States. Examining trends in health informs the development, implementation, and evaluation of health policies and programs. The first section (Figures 1–13) focuses on health status and determinants: life expectancy, infant mortality, selected causes of death, overdose deaths, suicide, maternal mortality, teen births, preterm births, use of tobacco products, asthma, hypertension, heart disease and cancer, and functional limitations. The second section (Figures 14–15) presents trends in health care utilization: use of mammography and colorectal tests and unmet medical needs. The third section (Figures 16–17) focuses on health care resources: availability of physicians and dentists. The fourth section (Figures 18–20) describes trends in personal health care expenditures, health insurance coverage, and supplemental insurance coverage among Medicare beneficiaries. The Highlights section summarizes major findings from the Chartbook. Suggested citation: National Center for Health Statistics. Health, United States, 2019. Hyattsville, MD. 2021.

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Brown, Yolanda, Twonia Goyer, and Maragaret Harvey. Heart Failure 30-Day Readmission Frequency, Rates, and HF Classification. University of Tennessee Health Science Center, December 2020. http://dx.doi.org/10.21007/con.dnp.2020.0002.

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30 Day Hospital Readmission Rates, Frequencies, and Heart Failure Classification for Patients with Heart Failure Background Congestive heart failure (CHF) is the leading cause of mortality, morbidity, and disability worldwide among patients. Both the incidence and the prevalence of heart failure are age dependent and are relatively common in individuals 40 years of age and older. CHF is one of the leading causes of inpatient hospitalization readmission in the United States, with readmission rates remaining above the 20% goal within 30 days. The Center for Medicare and Medicaid Services imposes a 3% reimbursement penalty for excessive readmissions including those who are readmitted within 30 days from prior hospitalization for heart failure. Hospitals risk losing millions of dollars due to poor performance. A reduction in CHF readmission rates not only improves healthcare system expenditures, but also patients’ mortality, morbidity, and quality of life. Purpose The purpose of this DNP project is to determine the 30-day hospital readmission rates, frequencies, and heart failure classification for patients with heart failure. Specific aims include comparing computed annual re-admission rates with national average, determine the number of multiple 30-day re-admissions, provide descriptive data for demographic variables, and correlate age and heart failure classification with the number of multiple re-admissions. Methods A retrospective chart review was used to collect hospital admission and study data. The setting occurred in an urban hospital in Memphis, TN. The study was reviewed by the UTHSC Internal Review Board and deemed exempt. The electronic medical records were queried from July 1, 2019 through December 31, 2019 for heart failure ICD-10 codes beginning with the prefix 150 and a report was generated. Data was cleaned such that each patient admitted had only one heart failure ICD-10 code. The total number of heart failure admissions was computed and compared to national average. Using age ranges 40-80, the number of patients re-admitted withing 30 days was computed and descriptive and inferential statistics were computed using Microsoft Excel and R. Results A total of 3524 patients were admitted for heart failure within the six-month time frame. Of those, 297 were re-admitted within 30 days for heart failure exacerbation (8.39%). An annual estimate was computed (16.86%), well below the national average (21%). Of those re-admitted within 30 days, 50 were re-admitted on multiple occasions sequentially, ranging from 2-8 re-admissions. The median age was 60 and 60% male. Due to the skewed distribution (most re-admitted twice), nonparametric statistics were used for correlation. While graphic display of charts suggested a trend for most multiple re-admissions due to diastolic dysfunction and least number due to systolic heart failure, there was no statistically significant correlation between age and number or multiple re-admissions (Spearman rank, p = 0.6208) or number of multiple re-admissions and heart failure classification (Kruskal Wallis, p =0.2553).

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Perceptions of community pharmacists, patent and proprietary medicine vendors, and their clients regarding quality of family planning services: The IntegratE Project. Population Council, 2021. http://dx.doi.org/10.31899/rh17.1016.

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The IntegratE Project is a four-year initiative (2017–21) implemented by the Population Council and partners that seeks to increase access to contraceptive methods by involving the private sector (community pharmacists [CPs] and patent and proprietary medicine vendors [PPMVs]) in family planning (FP) service delivery in Lagos and Kaduna States, Nigeria. The project aims to establish a regulatory system with the Pharmacists Council of Nigeria to ensure that CPs and PPMVs provide quality FP services, comply with FP regulations, and report service statistics to the Health Information Management System (HMIS). To achieve this, the project is implementing: a pilot three-tiered accreditation system for PPMVs; a supervisory model to ensure standard drug-stocking practices; building the capacity of CPs and PPMVs to provide a wider range of FP services and data report to the HMIS. This brief focuses on quality of care received by women voluntarily seeking FP services from CPs and PPMVs. CPs and PPMVs and their clients appear to be satisfied with the FP services offered by CPs and PPMVs; on-going learning opportunities, and a supportive supervision system that is properly coordinated should be sufficient to maintain the quality of services offered by CPs and PPMVs.

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Evidence Synthesis and Meta-Analysis for Drug Safety. Council for International Organizations of Medical Sciences (CIOMS), 2016. http://dx.doi.org/10.56759/lela7055.

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At any point in the drug development process, systematic reviews and meta-analysis can provide important information to guide the future path of the development programme and any actions that might be needed in the post-marketing setting. This report gives the rationale for why and when a meta-analysis should be considered, all in the context of regulatory decision-making, and the tasks, data collection, and analyses that need to be carried out to inform those decisions. -- There is increasing demand by decision-makers in health care, the biopharmaceutical industry, and society at large to have access to the best available evidence on benefits and risks of medicinal products. The best strategy will take an overview of all the evidence and where it is possible and sensible, combine the evidence and summarize the results. For efficacy, the outcomes generally use the same or very similar predefined events for each of the trials to be included. Most regulatory guidance and many Cochrane Collaboration reviews have usually given more attention to assessment of benefits, while issues around combining evidence on harms have not been as well-covered. However, the (inevitably) unplanned nature of the data on safety makes the process more difficult. -- Combining evidence on adverse events (AEs), where these were not the focus of the original studies, is more challenging than combining evidence on pre-specified benefits. This focus on AEs represents the main contribution of the current CIOMS X report. The goal of the CIOMS X report is to provide principles on appropriate application of meta-analysis in assessing safety of pharmaceutical products to inform regulatory decision-making. This report is about meta-analysis in this narrow area, but the present report should also provide conceptually helpful points to consider for a wider range of applications, such as vaccines, medical devices, veterinary medicines or even products that are combinations of medicinal products and medical devices. -- Although some of the content of this report describes highly technical statistical concepts and methods (in particular Chapter 4), the ambition of the working group has been to make it comprehensible to non-statisticians for its use in clinical epidemiology and regulatory science. To that end, Chapters 3 and 4, which contain the main technical statistical aspects of the appropriate design, analysis and reporting of a meta-analysis of safety data are followed by Chapter 5 with a thought process for evaluating the findings of a meta-analysis and how to communicate these.

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