385 research outputs found
Review: Evidence-Based Management of Acute Heart Failure
No full textAcute heart failure (AHF) is a complex, heterogeneous clinical syndrome with high morbidity and mortality, incurring significant healthcare costs. Patients transition from home to the emergency department, the hospital and home again, and require decisions surrounding diagnosis, treatment and prognosis at each step of the way. The purpose of this review is to examine the epidemiology, etiologies and classifications of AHF, and specifically focus on practical information relevant to the clinician. We examine the mechanisms of decompensation relevant to clinical presentations, including precipitating factors, neuroendocrine interactions and inflammation, along with how consideration of these factors these may help select therapies for an individual patient. The prevalence and significance of end-organ manifestations like renal, gastrointestinal, respiratory and neurologic manifestations are discussed. We also highlight how the development of renal dysfunction relates to the choice of a variety of diuretics that may be useful in specific circumstances and review guideline-directed medical therapy. We discuss the practical use (and pitfalls) of a variety of evidence-based clinical scoring criteria available to risk stratify patients with AHF. Finally, evidence-based management of AHF is discussed, including both pharmacologic and nonpharmacologic therapies, including the lack of evidence for using old and new vasodilators and the recent evidence regarding initiation of newer therapies in hospital. Overall, we suggest that clinicians consider implementing the newer data in AHF and subject existing practice patterns and treatments to the same rigor as new therapies
Testing equality of several correlation matrices
No full textigualdad de varias matrices de correlación, puede ser considerado como unestadístico modificado del test de razón de verosimilitud cuando se muestreanpoblaciones normales multivariadas. Derivamos la distribución asintóticanula de L* en series que involucran variables independientes chi-cuadrado,mediante la expansión de L* en términos de otras variables aleatorias yluego invertir la expansión término a término. Se da también un ejemplopara mostrar el procedimiento a ser usado cuando se prueba igualdad dematrices de correlación mediante el estadístico L
A Method of Transformation for Generalized Hypergeometric Function 2F2
No full textBy employing an addition theorem for the confluent hypergeometric function, Paris R.B.[3], has obtained a Kummer-type transformation for a 2F2 (x) hypergeometric function with general parameters in the form of a sum of 2F2 (-x) functions. Recently, Choi Junesang and Rathie Arjun K.[1], has obtained the same result without using the addition theorem. The aim of this paper is to derive the result of Paris R.B.[3], with change in the general parameters without using the addition theorem in the line of Choi Junesang and Rathie Arjun K.[1]. Corresponding author E.mail:- [email protected], [email protected]
Adaptive channel queue routing on k-ary n-cubes
No full textThis paper introduces a new adaptive method, Channel Queue Routing (CQR), for load-balanced routing on k-ary n-cube interconnection networks. CQR estimates global congestion in the network from its channel queues while relying on the implicit network backpressure to transfer congestion information to these queues. It uses this estimate to decide the directions to route in each dimension. It further load balances the network by routing in the selected directions adaptively. The only other algorithm that uses global congestion in its routing decision is the Globally Adaptive Load-Balance (GAL) algorithm introduced in [13]. GAL performs better than any other known routing algorithm on a wide variety of throughput and latency metrics. However, there are four serious issues with GAL. First, it has very high latency once it starts routing traffic non-minimally. Second, it is slow to adapt to changes in traffic. Third, it requires a complex method to achieve stability. Finally, it is complex to implement. These issues are all related to GAL’s use of injection queue length to infer global congestion. CQR uses channel queues rather than injection queues to estimate global congestion. In doing so, it overcomes the limitations of GAL described above while matching its high performance on all the performance metrics described in [13]. CQR gives much lower latency than GAL at loads where non-minimal routing is required. It adapts rapidly to changes in traffic, is unconditionally stable, and is simple to implement
Making augmented human intelligence in medicine practical: A case study of treating major depressive disorder
No full textIndividualized medicine tailors diagnoses and treatment options on an individual patient basis. This is a paradigm shift from choosing a treatment based on highest reported efficacy in clinical trials, which is often not effective for all individuals. In this dissertation, we assert that treatment selection and management can be individualized when clinicians assessment of disease symptoms are augmented with a few analytically identified patient-specific measures (e.g., genomics, metabolomics) that are prognostic or predictive of treatment outcomes. Patient-derived biological, clinical and symptom measures are sufficiently complex, i.e., heterogeneous, noisy and high-dimensional. The question for research then becomes: “Which few among these large complex measures are sufficient to augment the clinician’s disease assessment and treatment logic to individualize treatment decisions?”
This dissertation introduces, ALMOND — Analytics and Machine Learning Framework for Actionable Intelligence from Clinical and Omics Data. As a case study, this dissertation describes how ALMOND addresses the unmet need for individualized medicine in treating major depressive disorder — the leading cause of medical disabilities worldwide. The biggest challenge in individualizing treatment of depression is in the heterogeneity of how depressive symptoms manifest between individuals, and in their varied response to the same treatment.
ALMOND comprises a systematic analytical workflow to individualize antidepressant treatment by addressing the challenge of heterogeneity of major depressive disorder. First, “right patients” are identified by stratifying patients using unsupervised learning, that serves as a foundation to associate their disease states with multiple pharmacological (drug-associated) measures. Second, “right drug” selection is shown to be feasible by demonstrating that psychiatrists’ depression severity assessments augmented with pharmacogenomic measures can accurately predict remission of depressive symptoms using supervised learning. Finally, probabilistic graphs provide early and easily interpretable prognoses at the “right time” to a psychiatrist by accounting for changes in routinely assessed depressive symptoms’ severity. By choosing antidepressants that have the highest-likelihood of the patient achieving remission, the chances of persisting depressive symptoms are reduced, which is often the leading medical conditions in those who commit suicide or develop chronic illnesses.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-05-01The student, Arjun Athreya, accepted the attached license on 2019-04-12 at 15:37.The student, Arjun Athreya, submitted this Dissertation for approval on 2019-04-12 at 15:45.This Dissertation was approved for publication on 2019-04-12 at 16:30.DSpace SAF Submission Ingestion Package generated from Vireo submission #13592 on 2019-08-22 at 15:05:58Made available in DSpace on 2019-08-23T20:35:51Z (GMT). No. of bitstreams: 2
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Previous issue date: 2019-04-12Embargo set by: Seth Robbins for item 112132
Lift date: 2021-08-23T20:36:18Z
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An asymptotic expansion of the distribution of Rao's U-statistic under a general condition
No full textAbstractIn this paper we consider the problem of testing the hypothesis about the sub-mean vector. For this propose, the asymptotic expansion of the null distribution of Rao's U-statistic under a general condition is obtained up to order of n-1. The same problem in the k-sample case is also investigated. We find that the asymptotic distribution of generalized U-statistic in the k-sample case is identical to that of the generalized Hotelling's T2 distribution up to n-1. A simulation experiment is carried out and its results are presented. It shows that the asymptotic distributions have significant improvement when comparing with the limiting distributions both in the small sample case and the large sample case. It also demonstrates the equivalence of two testing statistics mentioned above
Rate of telomere shortening and cardiovascular damage: a longitudinal study in the 1946 British Birth Cohort.
No full textCross-sectional studies reported associations between short leucocyte telomere length (LTL) and measures of vascular and cardiac damage. However, the contribution of LTL dynamics to the age-related process of cardiovascular (CV) remodelling remains unknown. In this study, we explored whether the rate of LTL shortening can predict CV phenotypes over 10-year follow-up and the influence of established CV risk factors on this relationship
Discrete uniform mixtures via posterior means
No full textBeta-Pascal distribution, discrete uniform distribution, identification of mixtures, mixture, negative binomial distribution, negative hypergeometric distribution, posterior mean, primary 62H05, secondary 62F15,
An asymptotic expansion of the distribution of Rao's U-statistic under a general condition
No full textIn this paper we consider the problem of testing the hypothesis about the sub-mean vector. For this propose, the asymptotic expansion of the null distribution of Rao's U-statistic under a general condition is obtained up to order of n-1. The same problem in the k-sample case is also investigated. We find that the asymptotic distribution of generalized U-statistic in the k-sample case is identical to that of the generalized Hotelling's T2 distribution up to n-1. A simulation experiment is carried out and its results are presented. It shows that the asymptotic distributions have significant improvement when comparing with the limiting distributions both in the small sample case and the large sample case. It also demonstrates the equivalence of two testing statistics mentioned above.Rao's U-statistic Characteristic function Multivariate Hermite polynomials Multivariate cumulants Multivariate skewness Multivariate kurtosis
The distribution of a linear combination of two correlated chi-square variables
No full textLa distribución de una combinación lineal de dos variables chi cuadradoes conocida si las variables son independientes. En este artículo, se deriva ladistribución de una combinación lineal positiva de dos variables chi cuadradocuando estas están correlacionadas a través de una distribución chi cuadradobivariada. Algunas propiedades de esta distribución como la función característica,la función de distribución acumulada, sus momentos, momentoscentrados alrededor de la media, los coeficientes de sesgo y curtosis sonderivados. Los resultados coinciden con el caso independiente cuando lasvariables son no correlacionadas. La gráfica de la función de densidad estambién presentada
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