Stabilizing sparse cox model using statistic and semantic structures in electronic medical records

Stability in clinical prediction models is crucial for transferability between studies, yet has received little attention. The problem is paramount in high dimensional data, which invites sparse models with feature selection capability. We introduce an effective method to stabilize sparse Cox model of time-to-events using statistical and semantic structures inherent in Electronic Medical Records (EMR). Model estimation is stabilized using three feature graphs built from (i) Jaccard similarity among features (ii) aggregation of Jaccard similarity graph and a recently introduced semantic EMR graph (iii) Jaccard similarity among features transferred from a related cohort. Our experiments are conducted on two real world hospital datasets: a heart failure cohort and a diabetes cohort. On two stability measures – the Consistency index and signal-to-noise ratio (SNR) – the use of our proposed methods significantly increased feature stability when compared with the baselines.

Real-Time Control Of Floods Along The Demer River, Belgium, By Means Of MPC In Combination With GA And A Fast Conceptual River Model

Climate model projections show that climate change will further increase the risk of flooding in many regions of the world. There is a need for climate adaptation, but building new infrastructure or additional retention basins has its limits, especially in densely populated areas where open spaces are limited. Another solution is the more efficient use of the existing infrastructure. This research investigates a method for real-time flood control by means of existing gated weirs and retention basins. The method was tested for the specific study area of the Demer basin in Belgium but is generally applicable. Today, retention basins along the Demer River are controlled by means of adjustable gated weirs based on fixed logic rules. However, because of the high complexity of the system, only suboptimal results are achieved by these rules. By making use of precipitation forecasts and combined hydrological-hydraulic river models, the state of the river network can be predicted. To fasten the calculation speed, a conceptual river model was used. The conceptual model was combined with a Model Predictive Control (MPC) algorithm and a Genetic Algorithm (GA). The MPC algorithm predicts the state of the river network depending on the positions of the adjustable weirs in the basin. The GA generates these positions in a semi-random way. Cost functions, based on water levels, were introduced to evaluate the efficiency of each generation, based on flood damage minimization. In the final phase of this research the influence of the most important MPC and GA parameters was investigated by means of a sensitivity study. The results show that the MPC-GA algorithm manages to reduce the total flood volume during the historical event of September 1998 by 46% in comparison with the current regulation. Based on the MPC-GA results, some recommendations could be formulated to improve the logic rules.

Income inequality in a job-search model with heterogeneous time preferences

This paper explores the use of an intertemporal job-search model in the investigation of within-cohort and between-cohort income inequality, the latter being generated by the heterogeneity of time preferences among cohorts of homogenous workers and the former by the cross-sectional turnover in the job market. It also offers an alternative explanation for the empirically-documented negative correlation between time preference and labor income. Under some speciÖc distributions regarding wage offers and time preferences, we show how the within-cohort and between-cohort Gini coe¢ cients of income distribution can be calculated, and how they vary as a function of the parameters of the model.

A time-varying markov-switching model for economic growth

This paper investigates economic growth’s pattern of variation across and within countries using a Time-Varying Transition Matrix Markov-Switching Approach. The model developed follows the approach of Pritchett (2003) and explains the dynamics of growth based on a collection of different states, each of which has a sub-model and a growth pattern, by which countries oscillate over time. The transition matrix among the different states varies over time, depending on the conditioning variables of each country, with a linear dynamic for each state. We develop a generalization of the Diebold’s EM Algorithm and estimate an example model in a panel with a transition matrix conditioned on the quality of the institutions and the level of investment. We found three states of growth: stable growth, miraculous growth, and stagnation. The results show that the quality of the institutions is an important determinant of long-term growth, whereas the level of investment has varying roles in that it contributes positively in countries with high-quality institutions but is of little relevance in countries with medium- or poor-quality institutions.

Forecasting multivariate time series under present-value-model short- and long-run co-movement restrictions

It is well known that cointegration between the level of two variables (e.g. prices and dividends) is a necessary condition to assess the empirical validity of a present-value model (PVM) linking them. The work on cointegration,namelyon long-run co-movements, has been so prevalent that it is often over-looked that another necessary condition for the PVM to hold is that the forecast error entailed by the model is orthogonal to the past. This amounts to investigate whether short-run co-movememts steming from common cyclical feature restrictions are also present in such a system. In this paper we test for the presence of such co-movement on long- and short-term interest rates and on price and dividend for the U.S. economy. We focuss on the potential improvement in forecasting accuracies when imposing those two types of restrictions coming from economic theory.

Forecasting Multivariate Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictions

This paper has two original contributions. First, we show that the present value model (PVM hereafter), which has a wide application in macroeconomics and fi nance, entails common cyclical feature restrictions in the dynamics of the vector error-correction representation (Vahid and Engle, 1993); something that has been already investigated in that VECM context by Johansen and Swensen (1999, 2011) but has not been discussed before with this new emphasis. We also provide the present value reduced rank constraints to be tested within the log-linear model. Our second contribution relates to forecasting time series that are subject to those long and short-run reduced rank restrictions. The reason why appropriate common cyclical feature restrictions might improve forecasting is because it finds natural exclusion restrictions preventing the estimation of useless parameters, which would otherwise contribute to the increase of forecast variance with no expected reduction in bias. We applied the techniques discussed in this paper to data known to be subject to present value restrictions, i.e. the online series maintained and up-dated by Shiller. We focus on three different data sets. The fi rst includes the levels of interest rates with long and short maturities, the second includes the level of real price and dividend for the S&P composite index, and the third includes the logarithmic transformation of prices and dividends. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to them. Moreover, imposing short-run restrictions produce forecast winners 70% of the time for target variables of PVMs and 63.33% of the time when all variables in the system are considered.

Forecasting multivariate time series under present-value-model short- and long-run co-movement restrictions

Using a sequence of nested multivariate models that are VAR-based, we discuss different layers of restrictions imposed by present-value models (PVM hereafter) on the VAR in levels for series that are subject to present-value restrictions. Our focus is novel - we are interested in the short-run restrictions entailed by PVMs (Vahid and Engle, 1993, 1997) and their implications for forecasting. Using a well-known database, kept by Robert Shiller, we implement a forecasting competition that imposes different layers of PVM restrictions. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to the unrestricted VAR. Moreover, imposing short-run restrictions produces forecast winners 70% of the time for the target variables of PVMs and 63.33% of the time when all variables in the system are considered.

The Poisson-exponential regression model under different latent activation schemes

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Acute renal ischemia model in dogs: Effects of metoprolol

Introduction: To study the functional and hystological alterations in dog kidneys submitted to total ischemia for thirty minutes and the possible metoprolol protective action. Material and methods: Sixteen dogs anesthetized with sodium pentobarbital (SP) were studied and divided into two groups: G1-8 dogs submitted to left nephrectomy and right renal artery clamping for thirty minutes, and G2-8 dogs submitted to the same procedures of G1 and to the administration of 0.5 mg.kg(-1) metoprolol before ischemia. Attributes of renal function were studied. Results: There was acute tubular necrosis and a decrease of renal blood flow and glomerular filtration, and a increase of renal vascular resistance in both groups. Conclusion: the thirty minute renal ischemia appears to have determined the alterations found in the renal function and hystology in both groups. Metoprolol, used in G2, as to the time and dose applied didn't protect the kidney from the ischemic episode.

Rat model of depending prostaglandin renal state: Effect of ketoprofen

Introduction .The renal prostaglandins (PGs), vasodilators, preserve kidney function during increased activity of the renin-angiotensin system or renal sympathetic nerves (renal PG-dependent state [RPGD]). Ketoprofen (Ket) inhibits cyclooxygenase and, therefore, the synthesis of PGs. The aim of this study was to determine, in the rat, the action of Ket in the renal histology and function in a RPGD state (stress of anesthesia and hemorrhage). Material and Methods . Twenty male Wistar rats, anesthetized with sodium pentobarbital, were randomly divided into two groups: G1-control ( n = 10) and G2-Ket ( n = 10) submitted to arterial hemorrhage of 30% of volemia (estimated as 6% of body weight) three times (10% each 10 min), 65 min after anesthesia. G2 animals received Ket, 1.5 mg. kg -1 , venously, 5 min after anesthesia and 60 min before the first hemorrhage moment (first moment of the study [M1]). Medium arterial pressure (MAP), rectal temperature (T), and heart rate were monitored. G1 and G2 received para-aminohippurate sodium (PAH) and iothalamate sodium (IOT) solutions during the entire experimental time in order to determine clearance of PAH (effective renal plasma flow [ERPF]) and clearance of IOT (glomerular filtration rate [GFR]) without urine collection (determination of blood concentrations of PAH and IOT through the high-performance liquid chromatography), filtration fraction (FF), and renal vascular resistance (RVR). The animals were sacrificed in M3, 30 min after the third hemorrhage (M2) moment, and the kidneys and blood collected during the hemorrhage periods were utilized for histological study and determinations of hematocrit (Ht), serum creatinine (S Cr ), ERPF, GFR, FF, and RVR, respectively. Results . There were significant reductions of MAP, T, and Ht and a significant increase of S Cr . During the experiment, ERPF and GFR did not change, but ERPF was always higher in G1 than in G2. Ket did not alter FF, which increased in G1 over the duration of experiment. The Ket group had significantly higher RVR than the control group. The histology verified that both G1 and G2 were similar for tubular dilation and necrosis, but they were significantly different for tubular degeneration: G1 > G2. Conclusion . The changes observed in kidney histology probably were determined by hemorrhage and hypotension. Ket inhibited the synthesis of PGs and diminished tubular degeneration.

Tragedy on grade crossing: driver failure or systemic fragility?

In 2010, an accident occurred in Americana-SP, Brazil, involving two trains and one bus on a Grade Crossing, when 10 people died and 17 were injured including workers. This paper aims to analyze the accident using the Model of Analysis and Prevention of Work Accidents (MAPA). The method provides observation of work, interviews and analysis of documents to understand precedents of the event in the following stages: to understand the usual work from the involved people, the changes occurred in the system, the operation of barriers, managerial and organizational aspects. By the end, measures are suggested to avoid new occurrences. The accident took place at night in a site with insufficient lighting. The working conditions of bus drivers, train operators and watchmen are inadequate. There were only symbolic barriers (visual and acoustic signals) triggered manually by watchman upon train operator radio communication. The fragility of the barrier system associated to poor lighting and short time to trigger the signaling seem to play a critical role in the event. Contrary to the official report which resulted in guilt of the bus driver, the conclusion of the paper emphasizes the fragility of the safety system and the need of level crossing reproject.

Time-dependent expression of annexin 1 in a model of chronic granulomatous inflammation

Objective and design: To determine the expression pattern and distribution of the glucocorticoid-inducible protein annexin 1 (ANXA1) in a murine model of chronic granulomatous inflammation.Materials or subjects: TO Mouse.Treatment: Chronic granulomatous inflammation was induced by injecting into dorsal sub-cutaneous air-pouches in mice, a mixture of croton oil and Freund's complete adjuvant (CO/FCA).Methods: Western and northern analysis, corticosterone assay, and immunohistochemistry. Statistical analysis was performed using ANOVA followed by Tukey's pair-wise comparisons or Dunnett's multiple comparisons.Results: ANXA1 protein levels changed significantly throughout the 4-week time course, with an initial peak at day 7 and a later elevation at 28 days. ANXA1 mRNA levels peaked at days 1 and 3, with a significant decline at day 7 followed by an upward trend to day 28. Plasma corticosterone measurements taken throughout the time course revealed an increase from 14 days onward, suggesting that corticosterone does not influence ANXA1 expression during the initial stages of the model. Immunogold staining revealed that ANXA1 expression in the inflamed tissue was mainly in extravasated neutrophils, with intact protein (37 kDa) being predominantly observed on the cell membrane.Conclusions: the pattern of ANXA1 expression indicates that infiltrated neutrophils are responsible for the majority of ANXA1 present both at early and later stages of this model of granulomatous inflammation.

A model for Hopfions on the space-time S(3)xR

We construct static and time dependent exact soliton solutions for a theory of scalar fields taking values on a wide class of two dimensional target spaces, and defined on the four dimensional space-time S-3 X R. The construction is based on an ansatz built out of special coordinates on S3. The requirement for finite energy introduce boundary conditions that determine an infinite discrete spectrum of frequencies for the oscillating solutions. For the case where the target space is the sphere S-2, we obtain static soliton solutions with nontrivial Hopf topological charges. In addition, such Hopfions can oscillate in time, preserving their topological Hopf charge, with any of the frequencies belonging to that infinite discrete spectrum. (C) 2005 American Institute of Physics.

Time evolution of the Wigner function in discrete quantum phase space for a soluble quasi-spin model

The discrete phase space approach to quantum mechanics of degrees of freedom without classical counterparts is applied to the many-fermions/quasi-spin Lipkin model. The Wi:ner function is written for some chosen states associated to discrete angle and angular momentum variables, and the rime evolution is numerically calculated using the discrete von Neumnnn-Liouville equation. Direct evidences in the lime evolution of the Wigner function are extracted that identify a tunnelling effect. A connection with a SU(2)-based semiclassical continuous approach to the Lipkin model is also presented.

Aircraft interior failure pattern recognition utilizing text mining and neural networks

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)