Addressing Bed Costs for the Elderly: A New Methodology for Modelling Patient Outcomes and Length of Stay

The proportion of elderly in the population has dramatically increased and will continue to do so for at least the next 50 years. Medical resources throughout the world are feeling the added strain of the increasing proportion of elderly in the population. The effective care of elderly patients in hospitals may be enhanced by accurately modelling the length of stay of the patients in hospital and the associated costs involved. This paper examines previously developed models for patient length of stay in hospital and describes the recently developed conditional phase-type distribution (C-Ph) to model patient duration of stay in relation to explanatory patient variables. The Clinics data set was used to demonstrate the C-Ph methodology. The resulting model highlighted a strong relationship between Barthel grade, patient outcome and length of stay showing various groups of patient behaviour. The patients who stay in hospital for a very long time are usually those that consume the largest amount of hospital resources. These have been identified as the patients whose resulting outcome is transfer. Overall, the majority of transfer patients spend a considerably longer period of time in hospital compared to patients who die or are discharged home. The C-Ph model has the potential for considering costs where different costs are attached to the various phases or subgroups of patients and the anticipated cost of care estimated in advance. It is hoped that such a method will lead to the successful identification of the most cost effective case-mix management of the hospital ward.

Learning Bayesian networks from data: An information-theory based approach

This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop efficient algorithms that can effectively learn Bayesian networks, requiring only polynomial numbers of conditional independence (CI) tests in typical cases. We provide precise conditions that specify when these algorithms are guaranteed to be correct as well as empirical evidence (from real world applications and simulation tests) that demonstrates that these systems work efficiently and reliably in practice.

Computerised Diagnostic Decision Support System for the Classification of Pre-Invasive Cervical Squamous Lesions.

Previous studies have revealed considerable interobserver and intraobserver variation in the histological classification of preinvasive cervical squamous lesions. The aim of the present study was to develop a decision support system (DSS) for the histological interpretation of these lesions. Knowledge and uncertainty were represented in the form of a Bayesian belief network that permitted the storage of diagnostic knowledge and, for a given case, the collection of evidence in a cumulative manner that provided a final probability for the possible diagnostic outcomes. The network comprised 8 diagnostic histological features (evidence nodes) that were each independently linked to the diagnosis (decision node) by a conditional probability matrix. Diagnostic outcomes comprised normal; koilocytosis; and cervical intraepithelial neoplasia (CIN) 1, CIN II, and CIN M. For each evidence feature, a set of images was recorded that represented the full spectrum of change for that feature. The system was designed to be interactive in that the histopathologist was prompted to enter evidence into the network via a specifically designed graphical user interface (i-Path Diagnostics, Belfast, Northern Ireland). Membership functions were used to derive the relative likelihoods for the alternative feature outcomes, the likelihood vector was entered into the network, and the updated diagnostic belief was computed for the diagnostic outcomes and displayed. A cumulative probability graph was generated throughout the diagnostic process and presented on screen. The network was tested on 50 cervical colposcopic biopsy specimens, comprising 10 cases each of normal, koilocytosis, CIN 1, CIN H, and CIN III. These had been preselected by a consultant gynecological pathologist. Using conventional morphological assessment, the cases were classified on 2 separate occasions by 2 consultant and 2 junior pathologists. The cases were also then classified using the DSS on 2 occasions by the 4 pathologists and by 2 medical students with no experience in cervical histology. Interobserver and intraobserver agreement using morphology and using the DSS was calculated with K statistics. Intraobserver reproducibility using conventional unaided diagnosis was reasonably good (kappa range, 0.688 to 0.861), but interobserver agreement was poor (kappa range, 0.347 to 0.747). Using the DSS improved overall reproducibility between individuals. Using the DSS, however, did not enhance the diagnostic performance of junior pathologists when comparing their DSS-based diagnosis against an experienced consultant. However, the generation of a cumulative probability graph also allowed a comparison of individual performance, how individual features were assessed in the same case, and how this contributed to diagnostic disagreement between individuals. Diagnostic features such as nuclear pleomorphism were shown to be particularly problematic and poorly reproducible. DSSs such as this therefore not only have a role to play in enhancing decision making but also in the study of diagnostic protocol, education, self-assessment, and quality control. (C) 2003 Elsevier Inc. All rights reserved.

Power-law behaviour, heterogeneity, and trend chasing

Long-range dependence in volatility is one of the most prominent examples in financial market research involving universal power laws. Its characterization has recently spurred attempts to provide some explanations of the underlying mechanism. This paper contributes to this recent line of research by analyzing a simple market fraction asset pricing model with two types of traders---fundamentalists who trade on the price deviation from estimated fundamental value and trend followers whose conditional mean and variance of the trend are updated through a geometric learning process. Our analysis shows that agent heterogeneity, risk-adjusted trend chasing through the geometric learning process, and the interplay of noisy fundamental and demand processes and the underlying deterministic dynamics can be the source of power-law distributed fluctuations. In particular, the noisy demand plays an important role in the generation of insignificant autocorrelations (ACs) on returns, while the significant decaying AC patterns of the absolute returns and squared returns are more influenced by the noisy fundamental process. A statistical analysis based on Monte Carlo simulations is conducted to characterize the decay rate. Realistic estimates of the power-law decay indices and the (FI)GARCH parameters are presented.

Blood-derived dermal langerin+ dendritic cells survey the skin in the steady state.

Langerin is a C-type lectin receptor that recognizes glycosylated patterns on pathogens. Langerin is used to identify human and mouse epidermal Langerhans cells (LCs), as well as migratory LCs in the dermis and the skin draining lymph nodes (DLNs). Using a mouse model that allows conditional ablation of langerin(+) cells in vivo, together with congenic bone marrow chimeras and parabiotic mice as tools to differentiate LC- and blood-derived dendritic cells (DCs), we have revisited the origin of langerin(+) DCs in the skin DLNs. Our results show that in contrast to the current view, langerin(+)CD8(-) DCs in the skin DLNs do not derive exclusively from migratory LCs, but also include blood-borne langerin(+) DCs that transit through the dermis before reaching the DLN. The recruitment of circulating langerin(+) DCs to the skin is dependent on endothelial selectins and CCR2, whereas their recruitment to the skin DLNs requires CCR7 and is independent of CD62L. We also show that circulating langerin(+) DCs patrol the dermis in the steady state and migrate to the skin DLNs charged with skin antigens. We propose that this is an important and previously unappreciated element of immunosurveillance that needs to be taken into account in the design of novel vaccine strategies.

The Macroeconomic Factors Conditioning the Impact of Identity on Attitudes towards the EU

Factors relating to identity and to economics have been shown to be important predictors of attitudes towards the European Union (EU). In this article, we show that the impact of identity is conditional on economic context. First, living in a member state that receives relatively high levels of EU funding acts as a 'buffer', diluting the impact of an exclusive national identity on Euroscepticism. Second, living in a relatively wealthy member state, with its associated attractiveness for economic migrants, increases the salience of economic xenophobia as a driver of sceptical attitudes. These results highlight the importance of seeing theories of attitude formation (such as economic and identity theories) not as competitors but rather as complementary, with the predictive strength of one theoretical approach (identity) being a function of system-level variation in factors relating to the other theoretical approach (macro-level economic conditions).

Semifactual: Byrne's account of even-if

Byrne's approach to the semifactual conditional captures the reasoning data. However, we argue that it does not account for the processes or Principles by which people arrive at representations of even-if conditionals, upon which their reasoning is said to be based. Drawing upon recent work on the suppositional conditional we present such an account.

Prediction of microRNAs affecting mRNA expression during retinal development.

Background: MicroRNAs (miRNAs) are small RNA molecules (similar to 22 nucleotides) which have been shown to play an important role both in development and in maintenance of adult tissue. Conditional inactivation of miRNAs in the eye causes loss of visual function and progressive retinal degeneration. In addition to inhibiting translation, miRNAs can mediate degradation of targeted mRNAs. We have previously shown that candidate miRNAs affecting transcript levels in a tissue can be deduced from mRNA microarray expression profiles. The purpose of this study was to predict miRNAs which affect mRNA levels in developing and adult retinal tissue and to confirm their expression.

Results: Microarray expression data from ciliary epithelial retinal stem cells (CE-RSCs), developing and adult mouse retina were generated or downloaded from public repositories. Analysis of gene expression profiles detected the effects of multiple miRNAs in CE-RSCs and retina. The expression of 20 selected miRNAs was confirmed by RT-PCR and the cellular distribution of representative candidates analyzed by in situ hybridization. The expression levels of miRNAs correlated with the significance of their predicted effects upon mRNA expression. Highly expressed miRNAs included miR-124, miR-125a, miR-125b, miR-204 and miR-9. Over-expression of three miRNAs with significant predicted effects upon global mRNA levels resulted in a decrease in mRNA expression of five out of six individual predicted target genes assayed.

Conclusions: This study has detected the effect of miRNAs upon mRNA expression in immature and adult retinal tissue and cells. The validity of these observations is supported by the experimental confirmation of candidate miRNA expression and the regulation of predicted target genes following miRNA over-expression. Identified miRNAs are likely to be important in retinal development and function. Misregulation of these miRNAs might contribute to retinal degeneration and disease. Conversely, manipulation of their expression could potentially be used as a therapeutic tool in the future.

Priming of CD8+ and CD4+ T Cells in Experimental Leishmaniasis Is Initiated by Different Dendritic Cell Subtypes

The biological role of Langerin(+) dendritic cells (DCs) such as Langerhans cells and a subset of dermal DCs (dDCs) in adaptive immunity against cutaneous pathogens remains enigmatic. Thus, we analyzed the impact of Langerin(+) DCs in adaptive T cell-mediated immunity toward Leishmania major parasites in a Lang-DTR mouse model that allows conditional diphtheria toxin (DT)-induced ablation of The biological role of Langerin+ dendritic cells (DCs) such as Langerhans cells and a subset of dermal DCs (dDCs) in adaptive immunity against cutaneous pathogens remains enigmatic. Thus, we analyzed the impact of Langerin+ DCs in adaptive T cell-mediated immunity toward Leishmania major parasites in a Lang-DTR mouse model that allows conditional diphtheria toxin (DT)-induced ablation of Langerin+ DCs in vivo. For the first time, infection experiments with DT-treated Lang-DTR mice revealed that proliferation of L. major-specific CD8+ T cells is significantly reduced during the early phase of the immune response following depletion of Langerin+ DCs. Consequently, the total number of activated CD8+ T cells within the draining lymph node and at the site of infection is diminished. Furthermore, we show that the impaired CD8+ T cell response is due to the absence of Langerin+ dDCs and not Langerhans cells. Nevertheless, the CD4+ T cell response is not altered and the infection is cleared as effectively in DT-treated Lang-DTR mice as in control mice. This clearly demonstrates that Langerin+ DCs are, in general, dispensable for an efficient adaptive immune response against L. major parasites. Thus, we propose a novel concept that, in the experimental model of leishmaniasis, priming of CD4+ T cells is mediated by Langerin− dDCs, whereas Langerin+ dDCs are involved in early priming of CD8+ T cells.

A framework for managing uncertain inputs: An axiomization of rewarding

The success postulate in belief revision ensures that new evidence (input) is always trusted. However, admitting uncertain input has been questioned by many researchers. Darwiche and Pearl argued that strengths of evidence should be introduced to determine the outcome of belief change, and provided a preliminary definition towards this thought. In this paper, we start with Darwiche and Pearl’s idea aiming to develop a framework that can capture the influence of the strengths of inputs with some rational assumptions. To achieve this, we first define epistemic states to represent beliefs attached with strength, and then present a set of postulates to describe the change process on epistemic states that is determined by the strengths of input and establish representation theorems to characterize these postulates. As a result, we obtain a unique rewarding operator which is proved to be a merging operator that is in line with many other works. We also investigate existing postulates on belief merging and compare them with our postulates. In addition, we show that from an epistemic state, a corresponding ordinal conditional function by Spohn can be derived and the result of combining two epistemic states is thus reduced to the result of combining two corresponding ordinal conditional functions proposed by Laverny and Lang. Furthermore, when reduced to the belief revision situation, we prove that our results induce all the Darwiche and Pearl’s postulates as well as the Recalcitrance postulate and the Independence postulate.

Modelling the mechanical response of the Reed-mouthpiece-lip system of a clarinet. Part I. A one-dimensional distributed model

The motion of a clarinet reed that is clamped to a mouthpiece and supported by a lip is simulated in the time-domain using finite difference methods. The reed is modelled as a bar with non-uniform cross section, and is described using a one-dimensional, fourth-order partial differential equation. The interactions with the mouthpiece Jay and the player's lip are taken into account by incorporating conditional contact forces in the bar equation. The model is completed by clamped-free boundary conditions for the reed. An implicit finite difference method is used for discretising the system, and values for the physical parameters are chosen both from laboratory measurements and by accurate tuning of the numerical simulations. The accuracy of the numerical system is assessed through analysis of frequency warping effects and of resonance estimation. Finally, the mechanical properties of the system are studied by analysing its response to external driving forces. In particular, the effects of reed curling are investigated.

Maximum likelihood estimation of higher-order integer-valued autoregressive processes

In this article, we extend the earlier work of Freeland and McCabe [Journal of time Series Analysis (2004) Vol. 25, pp. 701–722] and develop a general framework for maximum likelihood (ML) analysis of higher-order integer-valued autoregressive processes. Our exposition includes the case where the innovation sequence has a Poisson distribution and the thinning is binomial. A recursive representation of the transition probability of the model is proposed. Based on this transition probability, we derive expressions for the score function and the Fisher information matrix, which form the basis for ML estimation and inference. Similar to the results in Freeland and McCabe (2004), we show that the score function and the Fisher information matrix can be neatly represented as conditional expectations. Using the INAR(2) speci?cation with binomial thinning and Poisson innovations, we examine both the asymptotic e?ciency and ?nite sample properties of the ML estimator in relation to the widely used conditional least
squares (CLS) and Yule–Walker (YW) estimators. We conclude that, if the Poisson assumption can be justi?ed, there are substantial gains to be had from using ML especially when the thinning parameters are large.

Testosterone levels in dominant sociable males are lower than in solitary roamers: Physiological differences between three male reproductive tactics in a sociably flexible mammal

The relative plasticity hypothesis predicts that alternative tactics are associated with changes in steroid hormone levels. In species with alternative male reproductive tactics, the highest androgen levels have usually been reported in dominant males. However, in sociable species, dominant males show amicable behaviors to gain access to females, which might conflict with high testosterone levels. We compared testosterone, corticosterone, and resting metabolic rate in male striped mice (Rhabdomys pumilio) following a conditional strategy with three different reproductive tactics: (i) philopatric group-living males, (ii) solitary-living roamers, (iii) dominant but sociable group-living territorial breeders. Philopatrics had the lowest testosterone but highest corticosterone levels, suggesting that they make the best of a bad job. Dominant territorial breeders had lower testosterone levels than roamers, which have a lower competitive status. Roamers had the highest testosterone levels, which might promote risky behavior, such as invading territories defended by territorial males. Roamers also had lower resting metabolic rates than either type of group-living males. Our results suggest that dominant males' testosterone levels reflect a trade-off between low testosterone amicable behavior and high testosterone dominance behavior.

Modeling Multivariate Interest Rates Using Time-Varying Copulas and Reducible Nonlinear Stochastic Differential Equations

We propose a new approach for modeling nonlinear multivariate interest rate processes based on time-varying copulas and reducible stochastic differential equations (SDEs). In the modeling of the marginal processes, we consider a class of nonlinear SDEs that are reducible to Ornstein--Uhlenbeck (OU) process or Cox, Ingersoll, and Ross (1985) (CIR) process. The reducibility is achieved via a nonlinear transformation function. The main advantage of this approach is that these SDEs can account for nonlinear features, observed in short-term interest rate series, while at the same time leading to exact discretization and closed-form likelihood functions. Although a rich set of specifications may be entertained, our exposition focuses on a couple of nonlinear constant elasticity volatility (CEV) processes, denoted as OU-CEV and CIR-CEV, respectively. These two processes encompass a number of existing models that have closed-form likelihood functions. The transition density, the conditional distribution function, and the steady-state density function are derived in closed form as well as the conditional and unconditional moments for both processes. In order to obtain a more flexible functional form over time, we allow the transformation function to be time varying. Results from our study of U.S. and UK short-term interest rates suggest that the new models outperform existing parametric models with closed-form likelihood functions. We also find the time-varying effects in the transformation functions statistically significant. To examine the joint behavior of interest rate series, we propose flexible nonlinear multivariate models by joining univariate nonlinear processes via appropriate copulas. We study the conditional dependence structure of the two rates using Patton (2006a) time-varying symmetrized Joe--Clayton copula. We find evidence of asymmetric dependence between the two rates, and that the level of dependence is positively related to the level of the two rates. (JEL: C13, C32, G12) Copyright The Author 2010. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org, Oxford University Press.

Putting a Price on Prisoner Release: The History of Bail and the Future of Parole

In this article, we argue that the history of bail foretells the future of parole. Under a plancalled the Conditional Post-Conviction Release Bond Act (recently passed into law inthree states), US prisoners can secure early release only after posting ‘post-convictionbail’. As with pre-trial bail, the fledgling model would require prisoners to pay a percent-age of the bail amount to secure their release under the contractual responsibility of acommercial bail agency. If release conditions are breached, bounty hunters are legallyempowered to seize and return the parolee to prison. Our inquiry outlines the origins of this post-conviction bond plan and the research upon which it is based. Drawing on the‘new penology’ framework, we identify several underlying factors that make for a ripeadvocacy environment and set the stage for widespread state-level adoption of this planin the near future. Post-conviction bail fits squarely within the growing policy trendstoward privatization, managerialism, and actuarial justice. Most importantly, though,advocates have the benefit of precedent on their side, as most US states have longrelied on a system of commercial bail bonding and private bounty hunting to manageconditional pretrial release.