Monitoring care processes in the gynecologic oncology department

The care processes of healthcare providers are typically considered as human-centric, flexible, evolving, complex and multi-disciplinary. Consequently, acquiring an insight in the dynamics of these care processes can be an arduous task. A novel event log based approach for extracting valuable medical and organizational information on past executions of the care processes is presented in this study. Care processes are analyzed with the help of a preferential set of process mining techniques in order to discover recurring patterns, analyze and characterize process variants and identify adverse medical events.

Complete and interpretable conformance checking of business processes

This article presents a method for checking the conformance between an event log capturing the actual execution of a business process, and a model capturing its expected or normative execution. Given a business process model and an event log, the method returns a set of statements in natural language describing the behavior allowed by the process model but not observed in the log and vice versa. The method relies on a unified representation of process models and event logs based on a well-known model of concurrency, namely event structures. Specifically, the problem of conformance checking is approached by folding the input event log into an event structure, unfolding the process model into another event structure, and comparing the two event structures via an error-correcting synchronized product. Each behavioral difference detected in the synchronized product is then verbalized as a natural language statement. An empirical evaluation shows that the proposed method scales up to real-life datasets while producing more concise and higher-level difference descriptions than state-of-the-art conformance checking methods.

Phenotypical responses of Fucus vesiculosus germlings to warming, acidification, nutrient enrichment and local upwelling under natural fluctuations (July - September 2014)

During summer 2014 (mid-July - mid-September 2014), early life-stage Fucus vesiculosus were exposed to combined ocean acidification and warming (OAW) in the presence and absence of enhanced nutrient levels (OAW x N experiment). Subsequently, F. vesiculosus germlings were exposed to a final upwelling disturbance during 3 days (mid-September 2014). Experiments were performed in the near-natural scenario "Kiel Outdoor Benthocosms" including natural fluctuations in the southwestern Baltic Sea, Kiel Fjord, Germany (54°27 'N, 10°11 'W). Genetically different sibling groups and different levels of genetic diversity were employed to test to which extent genetic variation would result in response variation. The data presented here show the phenotypical response (growth and survival) of the different experimental populations of F. vesiculosus under OAW, nutrient enrichment and the upwelling event. Log effect ratios demonstrate the responses to enhanced OAW and nutrient concentrations relative to the ambient conditons. Carbon, nitrogen content (% DW) and C:N ratios were measured after the exposure of ambient and high nutrient levels. Abiotic conditions the OAW x nutrient experiment and the upwelling event, are shown.

Effect of patient accrual patterns on power and size of log-rank test for time-to-event outcome in clinical trials

The determination of size as well as power of a test is a vital part of a Clinical Trial Design. This research focuses on the simulation of clinical trial data with time-to-event as the primary outcome. It investigates the impact of different recruitment patterns, and time dependent hazard structures on size and power of the log-rank test. A non-homogeneous Poisson process is used to simulate entry times according to the different accrual patterns. A Weibull distribution is employed to simulate survival times according to the different hazard structures. The current study utilizes simulation methods to evaluate the effect of different recruitment patterns on size and power estimates of the log-rank test. The size of the log-rank test is estimated by simulating survival times with identical hazard rates between the treatment and the control arm of the study resulting in a hazard ratio of one. Powers of the log-rank test at specific values of hazard ratio (≠1) are estimated by simulating survival times with different, but proportional hazard rates for the two arms of the study. Different shapes (constant, decreasing, or increasing) of the hazard function of the Weibull distribution are also considered to assess the effect of hazard structure on the size and power of the log-rank test. ^

SCOOT game event : families as partners in learning

How games can be designed to engage families in learning spaces outside of the classroom. SCOOT Game has been played by families in various science museums and art galleries in Australian capital cities since 2004. Families form groups to collaborate in the game that takes them on an SMS quest through these places engaging them with artworks, historic facts, landmarks, puzzles, street performances etc.

Visualising security incidents through event aggregation

Extracting and aggregating the relevant event records relating to an identified security incident from the multitude of heterogeneous logs in an enterprise network is a difficult challenge. Presenting the information in a meaningful way is an additional challenge. This paper looks at solutions to this problem by first identifying three main transforms; log collection, correlation, and visual transformation. Having identified that the CEE project will address the first transform, this paper focuses on the second, while the third is left for future work. To aggregate by correlating event records we demonstrate the use of two correlation methods, simple and composite. These make use of a defined mapping schema and confidence values to dynamically query the normalised dataset and to constrain result events to within a time window. Doing so improves the quality of results, required for the iterative re-querying process being undertaken. Final results of the process are output as nodes and edges suitable for presentation as a network graph.

The log-exponentiated Weibull regression model for interval-censored data

In interval-censored survival data, the event of interest is not observed exactly but is only known to occur within some time interval. Such data appear very frequently. In this paper, we are concerned only with parametric forms, and so a location-scale regression model based on the exponentiated Weibull distribution is proposed for modeling interval-censored data. We show that the proposed log-exponentiated Weibull regression model for interval-censored data represents a parametric family of models that include other regression models that are broadly used in lifetime data analysis. Assuming the use of interval-censored data, we employ a frequentist analysis, a jackknife estimator, a parametric bootstrap and a Bayesian analysis for the parameters of the proposed model. We derive the appropriate matrices for assessing local influences on the parameter estimates under different perturbation schemes and present some ways to assess global influences. Furthermore, for different parameter settings, sample sizes and censoring percentages, various simulations are performed; in addition, the empirical distribution of some modified residuals are displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended to a modified deviance residual in log-exponentiated Weibull regression models for interval-censored data. (C) 2009 Elsevier B.V. All rights reserved.

Generalized log-gamma regression models with cure fraction

In this paper, the generalized log-gamma regression model is modified to allow the possibility that long-term survivors may be present in the data. This modification leads to a generalized log-gamma regression model with a cure rate, encompassing, as special cases, the log-exponential, log-Weibull and log-normal regression models with a cure rate typically used to model such data. The models attempt to simultaneously estimate the effects of explanatory variables on the timing acceleration/deceleration of a given event and the surviving fraction, that is, the proportion of the population for which the event never occurs. The normal curvatures of local influence are derived under some usual perturbation schemes and two martingale-type residuals are proposed to assess departures from the generalized log-gamma error assumption as well as to detect outlying observations. Finally, a data set from the medical area is analyzed.

Software tools and efficient algorithms for the feature detection, feature tracking, event localization, and visualization of large sets of atmospheric data

Data sets describing the state of the earth's atmosphere are of great importance in the atmospheric sciences. Over the last decades, the quality and sheer amount of the available data increased significantly, resulting in a rising demand for new tools capable of handling and analysing these large, multidimensional sets of atmospheric data. The interdisciplinary work presented in this thesis covers the development and the application of practical software tools and efficient algorithms from the field of computer science, aiming at the goal of enabling atmospheric scientists to analyse and to gain new insights from these large data sets. For this purpose, our tools combine novel techniques with well-established methods from different areas such as scientific visualization and data segmentation. In this thesis, three practical tools are presented. Two of these tools are software systems (Insight and IWAL) for different types of processing and interactive visualization of data, the third tool is an efficient algorithm for data segmentation implemented as part of Insight.Insight is a toolkit for the interactive, three-dimensional visualization and processing of large sets of atmospheric data, originally developed as a testing environment for the novel segmentation algorithm. It provides a dynamic system for combining at runtime data from different sources, a variety of different data processing algorithms, and several visualization techniques. Its modular architecture and flexible scripting support led to additional applications of the software, from which two examples are presented: the usage of Insight as a WMS (web map service) server, and the automatic production of a sequence of images for the visualization of cyclone simulations. The core application of Insight is the provision of the novel segmentation algorithm for the efficient detection and tracking of 3D features in large sets of atmospheric data, as well as for the precise localization of the occurring genesis, lysis, merging and splitting events. Data segmentation usually leads to a significant reduction of the size of the considered data. This enables a practical visualization of the data, statistical analyses of the features and their events, and the manual or automatic detection of interesting situations for subsequent detailed investigation. The concepts of the novel algorithm, its technical realization, and several extensions for avoiding under- and over-segmentation are discussed. As example applications, this thesis covers the setup and the results of the segmentation of upper-tropospheric jet streams and cyclones as full 3D objects. Finally, IWAL is presented, which is a web application for providing an easy interactive access to meteorological data visualizations, primarily aimed at students. As a web application, the needs to retrieve all input data sets and to install and handle complex visualization tools on a local machine are avoided. The main challenge in the provision of customizable visualizations to large numbers of simultaneous users was to find an acceptable trade-off between the available visualization options and the performance of the application. Besides the implementational details, benchmarks and the results of a user survey are presented.

Modeling multilevel sleep transitional data via Poisson log-linear multilevel models

This paper proposes Poisson log-linear multilevel models to investigate population variability in sleep state transition rates. We specifically propose a Bayesian Poisson regression model that is more flexible, scalable to larger studies, and easily fit than other attempts in the literature. We further use hierarchical random effects to account for pairings of individuals and repeated measures within those individuals, as comparing diseased to non-diseased subjects while minimizing bias is of epidemiologic importance. We estimate essentially non-parametric piecewise constant hazards and smooth them, and allow for time varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified through a re-derivation of a classical algebraic likelihood equivalence of Poisson regression with a log(time) offset and survival regression assuming piecewise constant hazards. This relationship allows us to synthesize two methods currently used to analyze sleep transition phenomena: stratified multi-state proportional hazards models and log-linear models with GEE for transition counts. An example data set from the Sleep Heart Health Study is analyzed.

Log-ratio of silica to aluminium counts (ln(Si/Al)) from ODP site 108-658

Growing evidence suggests that the low atmospheric CO2 concentration of the ice ages resulted from enhanced storage of CO2 in the ocean interior, largely as a result of changes in the Southern Ocean1. Early in the most recent deglaciation, a reduction in North Atlantic overturning circulation seems to have driven CO2 release from the Southern Ocean**2, 3, 4, 5, but the mechanism connecting the North Atlantic and the Southern Ocean remains unclear. Biogenic opal export in the low-latitude ocean relies on silicate from the underlying thermocline, the concentration of which is affected by the circulation of the ocean interior. Here we report a record of biogenic opal export from a coastal upwelling system off the coast of northwest Africa that shows pronounced opal maxima during each glacial termination over the past 550,000 years. These opal peaks are consistent with a strong deglacial reduction in the formation of silicate-poor glacial North Atlantic intermediate water**2 (GNAIW). The loss of GNAIW allowed mixing with underlying silicate-rich deep water to increase the silicate supply to the surface ocean. An increase in westerly-wind-driven upwelling in the Southern Ocean in response to the North Atlantic change has been proposed to drive the deglacial rise in atmospheric CO2 (refs 3, 4). However, such a circulation change would have accelerated the formation of Antarctic intermediate water and sub-Antarctic mode water, which today have as little silicate as North Atlantic Deep Water and would have thus maintained low silicate concentrations in the Atlantic thermocline. The deglacial opal maxima reported here suggest an alternative mechanism for the deglacial CO2 release**5, 6. Just as the reduction in GNAIW led to upward silicate transport, it should also have allowed the downward mixing of warm, low-density surface water to reach into the deep ocean. The resulting decrease in the density of the deep Atlantic relative to the Southern Ocean surface promoted Antarctic overturning, which released CO2 to the atmosphere.

Tie points and log-adjusted depth scales for ODP Leg 207 Sites

Multiple copies of Cretaceous black shales extending from the early Cenomanian to the end of the Santonian were recovered at five sites on Demerara Rise during Leg 207 of the Ocean Drilling Program. These sediments are primarily composed of laminated organic-rich claystones interbedded with coarser, lightly laminated foraminferal-bearing packstones and wackestones. The black shales represent the local expression of widespread organic-rich sedimentation in the Atlantic during the mid-Cretaceous. However, incomplete recovery prevented construction of continuous composite sections, resulting in uncertainties concerning the correct stratigraphic placement of individual cores. By combining high-resolution measurements of bulk density collected shipboard on the multisensor track with continuous downhole measurements of formation resistivity using the Formation MicroScanner, an equivalent logging depth scale was constructed for black shales recovered from Sites 1258, 1260, and 1261. The integrated depths approach centimeter-scale resolution and are supported by comparisons of coarser resolution natural gamma ray emissions collected on cores and through downhole logging operations. The new depths highlight the extent of both intra- and intercore gaps and provide an opportunity to further constrain temporal and spatial paleoceanographic changes captured in proxy records from these sediments.