Rule-based approach for identifying assertions in clinical free-text data

A rule-based approach for classifying previously identified medical concepts in the clinical free text into an assertion category is presented. There are six different categories of assertions for the task: Present, Absent, Possible, Conditional, Hypothetical and Not associated with the patient. The assertion classification algorithms were largely based on extending the popular NegEx and Context algorithms. In addition, a health based clinical terminology called SNOMED CT and other publicly available dictionaries were used to classify assertions, which did not fit the NegEx/Context model. The data for this task includes discharge summaries from Partners HealthCare and from Beth Israel Deaconess Medical Centre, as well as discharge summaries and progress notes from University of Pittsburgh Medical Centre. The set consists of 349 discharge reports, each with pairs of ground truth concept and assertion files for system development, and 477 reports for evaluation. The system’s performance on the evaluation data set was 0.83, 0.83 and 0.83 for recall, precision and F1-measure, respectively. Although the rule-based system shows promise, further improvements can be made by incorporating machine learning approaches.

Approximate clone detection in repositories of business process models

Evidence exists that repositories of business process models used in industrial practice contain significant amounts of duplication. This duplication may stem from the fact that the repository describes variants of the same pro- cesses and/or because of copy/pasting activity throughout the lifetime of the repository. Previous work has put forward techniques for identifying duplicate fragments (clones) that can be refactored into shared subprocesses. However, these techniques are limited to finding exact clones. This paper analyzes the prob- lem of approximate clone detection and puts forward two techniques for detecting clusters of approximate clones. Experiments show that the proposed techniques are able to accurately retrieve clusters of approximate clones that originate from copy/pasting followed by independent modifications to the copied fragments.

Using approximate Bayesian computation to estimate transmission rates of nosocomial pathogens

In this paper, we apply a simulation based approach for estimating transmission rates of nosocomial pathogens. In particular, the objective is to infer the transmission rate between colonised health-care practitioners and uncolonised patients (and vice versa) solely from routinely collected incidence data. The method, using approximate Bayesian computation, is substantially less computer intensive and easier to implement than likelihood-based approaches we refer to here. We find through replacing the likelihood with a comparison of an efficient summary statistic between observed and simulated data that little is lost in the precision of estimated transmission rates. Furthermore, we investigate the impact of incorporating uncertainty in previously fixed parameters on the precision of the estimated transmission rates.

UCPR rule 687 - exercise of discretion to fix costs - applicable principles

Although the Uniform Civil Procedure Rules 1999 (Qld) (UCPR) have always included a power for the court to order a party to pay an amount for costs to be fixed by the court, until recently the power was rarely used in the higher courts. In light of recent practice directions, and the changes to the procedures for assessment of costs contained in the new Chapter 17A of the UCPR, this is no longer the case. The judgment of Mullins J in ASIC v Atlantic 3 Financial (Aust) Pty Ltd [2008] QSC 9 provides some helpful guidance for practitioners about the principles which might be applied.

The role of the rule of law in virtual communities

Please see the updated published version of this work at http://eprints.qut.edu.au/37850/ There is a severe tendency in cyberlaw theory to delegitimize state intervention in the governance of virtual communities. Much of the existing theory makes one of two fundamental flawed assumptions: that communities will always be best governed without the intervention of the state; or that the territorial state can best encourage the development of communities by creating enforceable property rights and allowing the market to resolve any disputes. These assumptions do not ascribe sufficient weight to the value-laden support that the territorial state always provides to private governance regimes, the inefficiencies that will tend to limit the development utopian communities, and the continued role of the territorial state in limiting autonomy in accordance with communal values...

Sufficiently informative measurements for stability of approximate conditional mean estimates

This paper establishes sufficient conditions to bound the error in perturbed conditional mean estimates derived from a perturbed model (only the scalar case is shown in this paper but a similar result is expected to hold for the vector case). The results established here extend recent stability results on approximating information state filter recursions to stability results on the approximate conditional mean estimates. The presented filter stability results provide bounds for a wide variety of model error situations.

Detecting approximate clones in process model repositories with Apromore

Approximate clone detection is the process of identifying similar process fragments in business process model collections. The tool presented in this paper can efficiently cluster approximate clones in large process model repositories. Once a repository is clustered, users can filter and browse the clusters using different filtering parameters. Our tool can also visualize clusters in the 2D space, allowing a better understanding of clusters and their member fragments. This demonstration will be useful for researchers and practitioners working on large process model repositories, where process standardization is a critical task for increasing the consistency and reducing the complexity of the repository.

Rule-based transit signal priority control method using a real-time transit travel time prediction model

An advanced rule-based Transit Signal Priority (TSP) control method is presented in this paper. An on-line transit travel time prediction model is the key component of the proposed method, which enables the selection of the most appropriate TSP plans for the prevailing traffic and transit condition. The new method also adopts a priority plan re-development feature that enables modifying or even switching the already implemented priority plan to accommodate changes in the traffic conditions. The proposed method utilizes conventional green extension and red truncation strategies and also two new strategies including green truncation and queue clearance. The new method is evaluated against a typical active TSP strategy and also the base case scenario assuming no TSP control in microsimulation. The evaluation results indicate that the proposed method can produce significant benefits in reducing the bus delay time and improving the service regularity with negligible adverse impacts on the non-transit street traffic.

Prediction of multilocus identity-by-descent

Previous studies have enabled exact prediction of probabilities of identity-by-descent (IBD) in randommating populations for a few loci (up to four or so), with extension to more using approximate regression methods. Here we present a precise predictor of multiple-locus IBD using simple formulas based on exact results for two loci. In particular, the probability of non-IBD X ABC at each of ordered loci A, B, and C can be well approximated by XABC = XABXBC/XB and generalizes to X123. . .k = X12X23. . .Xk-1,k/ Xk-2, where X is the probability of non-IBD at each locus. Predictions from this chain rule are very precise with population bottlenecks and migration, but are rather poorer in the presence of mutation. From these coefficients, the probabilities of multilocus IBD and non-IBD can also be computed for genomic regions as functions of population size, time, and map distances. An approximate but simple recurrence formula is also developed, which generally is less accurate than the chain rule but is more robust with mutation. Used together with the chain rule it leads to explicit equations for non-IBD in a region. The results can be applied to detection of quantitative trait loci (QTL) by computing the probability of IBD at candidate loci in terms of identity-by-state at neighboring markers.

The phosphate mineral sigloite Fe3+Al2(PO4)2(OH)3·7(H2O), an exception to the paragenesis rule – a vibrational spectroscopic study

The secondary phosphate mineral sigloite Fe3+Al2(PO4)2(OH)3·7H2O is the exception to the rule that phosphate mineral paragenesis is related to the final phase of hydrothermal mineralization at low temperatures. Sigloite was formed as an oxidation pseudomorph after paravauxite, during the last supergene paragenetic stage. We have studied the secondary phosphate mineral sigloite Fe3+Al2(PO4)2(OH)3·7H2O using vibrational spectroscopic techniques. Because the mineral is a phosphate mineral, it is readily studied by spectroscopic techniques as the phosphate and hydrogen phosphate units are readily measured. Indeed, sigloite shows the presence of both phosphate and hydrogen phosphate units in its structure. Raman bands at 1009 cm−1 with shoulders at 993 and 1039 cm−1 are assigned to stretching vibrations of and units. The Raman band at 993 cm−1 is assigned to the ν1 symmetric stretching mode of the POH units, whereas the Raman band at 1009 cm−1 is assigned to the ν1 symmetric stretching mode. Raman bands observed at 506, 528, 571, 596, 619 and 659 cm−1 are attributed to the ν4 out of plane bending modes of the PO4 and H2PO4 units. The Raman bands at 2988, 3118 and 3357 cm−1 are assigned to water stretching vibration. The series of bands at 3422, 3449, 3493, 3552 and 3615 cm−1 are assigned to the OH stretching vibrations of the hydroxyl units. The observation of multiple bands gives credence to the non-equivalence of the OH units in the sigloite structure.

Exact and approximate Bayesian inference for low count time series models with intractable likelihoods

In this paper we present a new simulation methodology in order to obtain exact or approximate Bayesian inference for models for low-valued count time series data that have computationally demanding likelihood functions. The algorithm fits within the framework of particle Markov chain Monte Carlo (PMCMC) methods. The particle filter requires only model simulations and, in this regard, our approach has connections with approximate Bayesian computation (ABC). However, an advantage of using the PMCMC approach in this setting is that simulated data can be matched with data observed one-at-a-time, rather than attempting to match on the full dataset simultaneously or on a low-dimensional non-sufficient summary statistic, which is common practice in ABC. For low-valued count time series data we find that it is often computationally feasible to match simulated data with observed data exactly. Our particle filter maintains $N$ particles by repeating the simulation until $N+1$ exact matches are obtained. Our algorithm creates an unbiased estimate of the likelihood, resulting in exact posterior inferences when included in an MCMC algorithm. In cases where exact matching is computationally prohibitive, a tolerance is introduced as per ABC. A novel aspect of our approach is that we introduce auxiliary variables into our particle filter so that partially observed and/or non-Markovian models can be accommodated. We demonstrate that Bayesian model choice problems can be easily handled in this framework.