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More than 40 years ago, Lions and Mercier introduced in a seminal paper the Douglas–Rachford algorithm. Today, this method is well-recognized as a classic and highly successful splitting method to find minimizers of the sum of two (not necessarily smooth) convex functions. Whereas the underlying theory has matured, one case remains a mystery: the behavior of the shadow sequence when the given functions have disjoint domains. Building on previous work, we establish for the first time weak and value convergence of the shadow sequence generated by the Douglas–Rachford algorithm in a setting of unprecedented generality. The weak limit point is shown to solve the associated normal problem, which is a minimal perturbation of the original optimization problem. We also present new results on the geometry of the minimal displacement vector. Funding: The research of H. H. Bauschke and W. M. Moursi was partially supported by Discovery Grants of the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2018-03703 and RGPIN-2019-04803], respectively. [ABSTRACT FROM AUTHOR]

The current prostate cancer (PCa) screen test, prostate-specific antigen (PSA), has a high sensitivity for PCa but low specificity for high-risk, clinically significant PCa (csPCa), resulting in overdiagnosis and overtreatment of non-csPCa. Early identification of csPCa while avoiding unnecessary biopsies in men with non-csPCa is challenging. We built an optimized machine learning platform (ClarityDX) and showed its utility in generating models predicting csPCa. Integrating the ClarityDX platform with blood-based biomarkers for clinically significant PCa and clinical biomarker data from a 3448-patient cohort, we developed a test to stratify patients' risk of csPCa; called ClarityDX Prostate. When predicting high risk cancer in the validation cohort, ClarityDX Prostate showed 95% sensitivity, 35% specificity, 54% positive predictive value, and 91% negative predictive value, at a ≥ 25% threshold. Using ClarityDX Prostate at this threshold could avoid up to 35% of unnecessary prostate biopsies. ClarityDX Prostate showed higher accuracy for predicting the risk of csPCa than PSA alone and the tested model-based risk calculators. Using this test as a reflex test in men with elevated PSA levels may help patients and their healthcare providers decide if a prostate biopsy is necessary. [ABSTRACT FROM AUTHOR]

Since individual neural networks have limited deep expressiveness and effectiveness, many learning frameworks face difficulties in the availability and balance of sample selection. As a result, in change detection, it is difficult to upgrade the hit rate of a high-performance model on both positive and negative pixels. Therefore, supposing that the sacrificed components coincide perfectly with the important evaluation objectives, such as positives, it would lose more than gain. To address this issue, in this paper, we propose a multi-visual collaborative deep network (MV-CDN) served by three collaborative network members that consists of three subdivision approaches, the CDN with one collaborator (CDN-C), CDN with two collaborators (CDN-2C), and CDN with three collaborators (CDN-3C). The purpose of the collaborator is to re-evaluate the feature elements in the network transmission, and thus to translate the group-thinking into a more robust field of vision. We use three sets of public double-temporal hyperspectral images taken by the AVIRIS and HYPERION sensors to show the feasibility of the proposed schema. The comparison results have confirmed that our proposed schema outperforms the existing state-of-the-art algorithms on the three tested datasets, which demonstrates the broad adaptability and progressiveness of the proposal. [ABSTRACT FROM AUTHOR]

Tide refers to a phenomenon that causes the change of water level in oceans. Tidal level forecasting plays an important role in many real-world applications especially those related to oceanic and coastal areas. For instance, accurate forecasting of tidal level can significantly increase the vessels’ safety as an excessive level of tidal makes serious problems in the movement of vessels. In this work, we propose a deep learning-based prediction interval framework in order to model the forecasting uncertainties of tidal current datasets. The proposed model develops optimum prediction intervals (PIs) focused on the deep learning-based CNN-LSTM model (CLSTM), and nonparametric approach termed as the lower upper bound estimation (LUBE) model. Moreover, we develop a novel deep neuroevolution algorithm based on a two-stage modification of the gaining-sharing knowledge optimization algorithm to optimize the architecture of the CLSTM automatically without the procedure of trial and error. This leads to a decline in the complexity raises in designing manually the deep learning architectures, as well as an enhancement in the performance of the PIs. We also utilize coverage width criterion to establish an excellent correlation appropriately between both the PI coverage probability and PI normalized average width. We indicate the searching efficiency and high accuracy of our proposed framework named as MGSK-CLSTM-LUBE by examining over the practical collected tidal current datasets from the Bay of Fundy, NS, Canada. [ABSTRACT FROM AUTHOR]

This paper presents a model of the relationship between relative attributes, relative preferences, and market share. It shows how parameters in the model may be estimated, using survey data to illustrate the estimation procedure. Use of the model and data for a new product are also discussed. The eventual market share of solar energy for residential heating in Canada is predicted using the procedures developed. [ABSTRACT FROM AUTHOR]

We present a fully finite-element based inversion methodology for imaging 3-D magnetotelluric impedance data on unstructured meshes. The inverse problem is formulated using a minimum-structure Gauss–Newton type optimization scheme that minimizes an objective function with respect to the model perturbation. By introducing a rigorous regularization scheme, we derived a Ritz-type variational formulation of the model objective function and designed a face-based finite-element basis function to discretize the model gradient across tetrahedron's inter-element boundaries. The forward modelling engine of our optimization scheme is based on a finite-element solution of the E-field Helmholtz equation that is enforced for the magnetotelluric simulation problem using the appropriate edge-based basis functions and 3D boundary conditions. The optimization algorithm developed here utilizes a message passing interface scheme and uses a direct solver to factorize and store both the regularization matrix and the forward modelling coefficient matrix on the processes working in parallel. Having to do this only once within each Gauss–Newton optimization cycle facilitates both the calculation of the dot product of the model regularization terms with the evolving model perturbation, and computing implicitly the sensitivity-vector products. We validated the methodology and the correctness of the developed algorithm for two test examples (COMMEMI 3Ds) from the literature. Also, by comparing the performance between classes of iterative solvers we demonstrated the superior performance of generalized minimum residual solver in reducing the residual norm of the iterative solver during model updates. Using the algorithm in a geologically realistic scenario, we imaged the anticipated geometry of the Lalor volcanogenic massive sulphide deposit in Canada. The feasibility of the imaging methodology is further evaluated with the survey data, for which, again the algorithm converged to the anticipated model solution reproducing the lithostratigraphic sequence of the ore deposit. [ABSTRACT FROM AUTHOR]

Background: Measurement of care quality and safety mainly relies on abstracted administrative data. However, it is well studied that administrative data-based adverse event (AE) detection methods are suboptimal due to lack of clinical information. Electronic medical records (EMR) have been widely implemented and contain detailed and comprehensive information regarding all aspects of patient care, offering a valuable complement to administrative data. Harnessing the rich clinical data in EMRs offers a unique opportunity to improve detection, identify possible risk factors of AE and enhance surveillance. However, the methodological tools for detection of AEs within EMR need to be developed and validated. The objectives of this study are to develop EMR-based AE algorithms from hospital EMR data and assess AE algorithm's validity in Canadian EMR data. Methods: Patient EMR structured and text data from acute care hospitals in Calgary, Alberta, Canada will be linked with discharge abstract data (DAD) between 2010 and 2020 (n~1.5 million). AE algorithms development. First, a comprehensive list of AEs will be generated through a systematic literature review and expert recommendations. Second, these AEs will be mapped to EMR free texts using Natural Language Processing (NLP) technologies. Finally, an expert panel will assess the clinical relevance of the developed NLP algorithms. AE algorithms validation: We will test the newly developed AE algorithms on 10,000 randomly selected EMRs between 2010 to 2020 from Calgary, Alberta. Trained reviewers will review the selected 10,000 EMR charts to identify AEs that had occurred during hospitalization. Performance indicators (e.g., sensitivity, specificity, positive predictive value, negative predictive value, F1 score, etc.) of the developed AE algorithms will be assessed using chart review data as the reference standard. Discussion: The results of this project can be widely implemented in EMR based healthcare system to accurately and timely detect in-hospital AEs. [ABSTRACT FROM AUTHOR]

Floating Catchment Area (FCA) methods are a popular tool to investigate accessibility to public facilities, in particular health care services. FCA approaches are attractive because, unlike other accessibility measures, they take into account the potential for congestion of facilities. This is done by 1) considering the population within the catchment area of a facility to calculate a variable that measures level of service, and then 2) aggregating the level of service by population centers subject to catchment area constraints. In this paper we discuss an effect of FCA approaches, an artifact that we term demand and level of service inflation. These artifacts are present in previous implementations of FCA methods. We argue that inflation makes interpretation of estimates of accessibility difficult, which has possible deleterious consequences for decision making. Next, we propose a simple and intuitive approach to proportionally allocate demandand and level of service in FCA calculations. The approach is based on a standardization of the impedance matrix, similar to approaches popular in the spatial statistics and econometrics literature. The result is a more intiuitive measure of accessibility that 1) provides a local version of the provider-to-population ratio; and 2) preserves the level of demand and the level of supply in a system. We illustrate the relevant issues with some examples, and then empirically by means of a case study of accessibility to family physicians in the Hamilton Census Metropolitan Area (CMA), in Ontario, Canada. Results indicate that demand and supply inflation/deflation affect the interpretation of accessibility analysis using existing FCA methods, and that the proposed adjustment can lead to more intuitive results. [ABSTRACT FROM AUTHOR]

Freely-available satellite data streams and the ability to process these data on cloud-computing platforms such as Google Earth Engine have made frequent, large-scale landcover mapping at high resolution a real possibility. In this paper we apply these technologies, along with machine learning, to the mapping of peatlands–a landcover class that is critical for preserving biodiversity, helping to address climate change impacts, and providing ecosystem services, e.g., carbon storage–in the Boreal Forest Natural Region of Alberta, Canada. We outline a data-driven, scientific framework that: compiles large amounts of Earth observation data sets (radar, optical, and LiDAR); examines the extracted variables for suitability in peatland modelling; optimizes model parameterization; and finally, predicts peatland occurrence across a large boreal area (397, 958 km2) of Alberta at 10 m spatial resolution (equalling 3.9 billion pixels across Alberta). The resulting peatland occurrence model shows an accuracy of 87% and a kappa statistic of 0.57 when compared to our validation data set. Differentiating peatlands from mineral wetlands achieved an accuracy of 69% and kappa statistic of 0.37. This data-driven approach is applicable at large geopolitical scales (e.g., provincial, national) for wetland and landcover inventories that support long-term, responsible resource management. [ABSTRACT FROM AUTHOR]

Atmospheric correction of satellite optical imagery over inland waters is a key remaining challenge in aquatic remote sensing. This is due to numerous confounding factors such as the complexity of water optical properties, the surface glint, the heterogeneous nature of atmospheric aerosols, and the proximity of bright land surfaces. This combination of factors makes it difficult to retrieve accurate information about the system observed. Moreover, the impact of radiance coming from adjacent land (adjacency effects) in complex geometries further adds to this challenge, especially for small lakes. In this study, ten atmospheric correction algorithms were evaluated for high-resolution multispectral imagery of Landsat-8 Operational Land Imager and Sentinel-2 MultiSpectral Instrument using in situ optical measurements from ~300 lakes across Canada. The results of the validation show that the performance of the algorithms varies by spectral band and evaluation metrics. The dark spectrum fitting algorithm had the best performance in terms of similarity angle (spectral shape), while the neural network-based models showed the lowest errors and bias per band. However, none of the tested atmospheric correction algorithms meet a 30% retrieval accuracy target across all the visible bands, likely due to uncorrected adjacency effects. To quantify this process, three-dimensional radiative transfer simulations were performed and compared to satellite observations. These simulations show that up to 60% of the top of atmosphere reflectance in the near-infrared bands over the lake was from the adjacent lands covered with green vegetation. The significance of these adjacency effects on atmospheric correction has been analyzed qualitatively, and potential efforts to improve the atmospheric correction algorithms are discussed. [ABSTRACT FROM AUTHOR]

In procedural-based medicine, the technical ability can be a critical determinant of patient outcomes. Psychomotor performance occurs in real-time, hence a continuous assessment is necessary to provide action-oriented feedback and error avoidance guidance. We outline a deep learning application, the Intelligent Continuous Expertise Monitoring System (ICEMS), to assess surgical bimanual performance at 0.2-s intervals. A long-short term memory network was built using neurosurgeon and student performance in 156 virtually simulated tumor resection tasks. Algorithm predictive ability was tested separately on 144 procedures by scoring the performance of neurosurgical trainees who are at different training stages. The ICEMS successfully differentiated between neurosurgeons, senior trainees, junior trainees, and students. Trainee average performance score correlated with the year of training in neurosurgery. Furthermore, coaching and risk assessment for critical metrics were demonstrated. This work presents a comprehensive technical skill monitoring system with predictive validation throughout surgical residency training, with the ability to detect errors. [ABSTRACT FROM AUTHOR]

The Western Arctic Linkage Experiment (WALE) is aimed at understanding the role of high-latitude terrestrial ecosystems in the response of the Arctic system to global change through collection and comparison of climate datasets and model results. In this paper, a spatiotemporal approach is taken to compare and validate model results from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) with commonly used analysis and reanalysis datasets for monthly averages of temperature and precipitation in 1992–2000 and for a study area at 55°–65°N, 160°–110°W in northwestern Canada and Alaska. Objectives include a quantitative assessment of similarity between datasets and climate model fields, and identification of geographic areas and seasons that are problematic in modeling, with potential causes that may aid in model improvement. These are achieved by application of algebraic similarity mapping, a simple yet effective method for synoptic analysis of many (here, 45) different spatial datasets, maps, and models. Results indicate a dependence of model–data similarity on seasonality, on climate variable, and on geographic location. In summary, 1) similarity of data and models is better for temperature than for precipitation; and 2) modeling of summer precipitation fields, and to a lesser extent, temperature fields, appears more problematic than that of winter fields. The geographic distribution of areas with best and worst agreement shifts throughout the year, with generally better agreement between maps and models in the northeastern and northern inland areas than in topographically complex and near-coastal areas. The study contributes to an understanding of the geographic complexity of the Arctic system and modeling its diverse climate. [ABSTRACT FROM AUTHOR]

A dynamic simulation algorithm based on 1-D transient convection/diffusion transport per particle size class is proposed to simulate a hydraulic classifier operated to selectively remove quartz from an iron oxide concentrate produced by processing the ore from an iron ore mine in northeastern Canada. The calibrated model is used to simulate the operation of dense bed hydraulic classifiers of different sizes and/or under different operating conditions. The simulator predicts the behavior and characteristics of the pulp at different depths within the classifier as a function of time. The simulator is validated by confronting the simulation results to experimental data obtained from sampling industrial and laboratory classifiers. The simulator is then used to assess the role of the fluidization or teeter water and of bed density on the quality of the produced separation of quartz from the valuable iron oxide of the processed ore. The knowledge acquired in the noise-free environment of simulation provides clues on the way to manipulate the hydraulic classifier operating variables in a process control strategy for an industrial scale unit. [ABSTRACT FROM AUTHOR]

Climate change affects natural streamflow regimes globally. To assess alterations in streamflow regimes, typically temporal variations in one or a few streamflow characteristics are taken into account. This approach, however, cannot see simultaneous changes in multiple streamflow characteristics, does not utilize all the available information contained in a streamflow hydrograph, and cannot describe how and to what extent streamflow regimes evolve from one to another. To address these gaps, we conceptualize streamflow regimes as intersecting spectrums that are formed by multiple streamflow characteristics. Accordingly, the changes in a streamflow regime should be diagnosed through gradual, yet continuous changes in an ensemble of streamflow characteristics. To incorporate these key considerations, we propose a generic algorithm to first classify streams into a finite set of intersecting fuzzy clusters. Accordingly, by analyzing how the degrees of membership to each cluster change in a given stream, we quantify shifts from one regime to another. We apply this approach to the data, obtained from 105 natural Canadian streams, during the period of 1966 to 2010. We show that natural streamflow in Canada can be categorized into six regime types, with clear hydrological and geographical distinctions. Analyses of trends in membership values show that alterations in natural streamflow regimes vary among different regions. Having said that, we show that in more than 80 % of considered streams, there is a dominant regime shift that can be attributed to simultaneous changes in streamflow characteristics, some of which have remained previously unknown. Our study not only introduces a new globally relevant algorithm for identifying changing streamflow regimes but also provides a fresh look at streamflow alterations in Canada, highlighting complex and multifaceted impacts of climate change on streamflow regimes in cold regions. [ABSTRACT FROM AUTHOR]

This paper describes a collection of 272 three-dimensional laser scans gathered at two unique planetary analogue rover test facilities in Canada, which offer emulated planetary terrain at manageable scales for algorithmic development. This dataset is subdivided into four individual subsets, each gathered using panning laser rangefinders on different mobile rover platforms. This data should be of interest to field robotics researchers developing rover navigation algorithms suitable for use in three-dimensional, unstructured, natural terrain. All of the data are presented in human-readable text files, and are accompanied by Matlab parsing scripts to facilitate use thereof. This paper provides an overview of the available data. [ABSTRACT FROM PUBLISHER]

A heavy rainfall event over a 2-h period on 8 July 2013 caused significant flash flooding in the city of Toronto and produced 126 mm of rain accumulation at a gauge located near the Toronto Pearson International Airport. This paper evaluates the quantitative precipitation estimates from the nearby King City C-band dual-polarized radar (WKR). Horizontal reflectivity Z and differential reflectivity ZDR were corrected for attenuation using a modified ZPHI rain profiling algorithm, and rain rates R were calculated from R( Z) and R( Z, ZDR) algorithms. Specific differential phase KDP was used to compute rain rates from three R( KDP) algorithms, one modified to use positive and negative KDP, and an R( KDP, ZDR) algorithm. Additionally, specific attenuation at horizontal polarization A was used to calculate rates from the R( A) algorithm. High-temporal-resolution rain gauge data at 44 locations measured the surface rainfall every 5 min and produced total rainfall accumulations over the affected area. The nearby NEXRAD S-band dual-polarized radar at Buffalo, New York, provided rain-rate and storm accumulation estimates from R( Z) and S-band dual-polarimetric algorithm. These two datasets were used as references to evaluate the C-band estimates. Significant radome attenuation at WKR overshadowed the attenuation correction techniques and resulted in poor rainfall estimates from the R( Z) and R( Z, ZDR) algorithms. Rainfall estimation from the Brandes et al. R( KDP) and R( A) algorithms were superior to the other methods, and the derived storm total accumulation gave biases of 2.1 and −6.1 mm, respectively, with correlations of 0.94. The C-band estimates from the Brandes et al. R( KDP) and R( A) algorithms were comparable to the NEXRAD S-band estimates. [ABSTRACT FROM AUTHOR]

To deal with the large volume of data produced by hyperspectral sensors, the Canadian Space Agency (CSA) has developed and patented two near lossless data compression algorithms for use onboard a hyperspectral satellite: successive approximation multi-stage vector quantization (SAMVQ) and hierarchical self-organizing cluster vector quantization (HSOCVQ). This paper describes the two compression algorithms and demonstrates their near lossless feature. The compression error introduced by the two compression algorithms was compared with the intrinsic noise of the original data that is caused by the instrument noise and other noise sources such as calibration and atmospheric correction errors. The experimental results showed that the compression error was not larger than the intrinsic noise of the original data when a test data set was compressed at a compression ratio of 20:1. The overall noise in the reconstructed data that contains both the intrinsic noise and the compression error is even smaller than the intrinsic noise when the data is compressed using SAMVQ. A multi-disciplinary user acceptability study has been carried out in order to evaluate the impact of the two compression algorithms on hyperspectral data applications. This paper briefly summarizes the evaluation results of the user acceptability study. A prototype hardware compressor that implements the two compression algorithms has been built using field programmable gate arrays (FPGAs) and benchmarked. The compression ratio and fidelity achieved by the hardware compressor are similar to those obtained by software simulation. [ABSTRACT FROM AUTHOR]

Background: As part of a broader study to improve the capacity for advance care planning (ACP) in primary healthcare settings, the research team set out to develop and validate a computerized algorithm to help primary care physicians identify individuals at risk of death, and also carried out focus groups and interviews with relevant stakeholder groups. Interviews with patients and family caregivers were carried out in parallel to algorithm development and validation to examine (1) views on early identification of individuals at risk of deteriorating health or dying; (2) views on the use of a computerized algorithm for early identification; and (3) preferences and challenges for ACP. Methods: Fourteen participants were recruited from two Canadian provinces. Participants included individuals aged 65 and older with declining health and self-identified caregivers of individuals aged 65 and older with declining health. Semi-structured interviews were conducted via telephone. A qualitative descriptive analytic approach was employed, which focused on summarizing and describing the informational contents of the data. Results: Participants supported the early identification of patients at risk of deteriorating health or dying. Early identification was viewed as conducive to planning not only for death, but for the remainder of life. Participants were also supportive of the use of a computerized algorithm to assist with early identification, although limitations were recognized. While participants felt that having family physicians assume responsibility for early identification and ACP was appropriate, questions arose around feasibility, including whether family physicians have sufficient time for ACP. Preferences related to the content of and approach to ACP discussions were highly individualized. Required supports during ACP include informational and emotional supports. Conclusions: This work supports the role of primary care providers in the early identification of individuals at risk of deteriorating health or death and the process of ACP. To improve ACP capacity in primary healthcare settings, compensation systems for primary care providers should be adjusted to ensure appropriate compensation and to accommodate longer ACP appointments. Additional resources and more established links to community organizations and services will also be required to facilitate referrals to relevant community services as part of the ACP process. [ABSTRACT FROM AUTHOR]

Since mid-February 2013, the ensemble Kalman filter (EnKF) in operation at the Canadian Meteorological Centre (CMC) has been using a 600 × 300 global horizontal grid and 74 vertical levels. This yields 5.4 × 107 model coordinates. The EnKF has 192 members and uses seven time levels, spaced 1 h apart, for the time interpolation in the 6-h assimilation window. It follows that over 7 × 1010 values are required to specify an ensemble of trial field trajectories. This paper focuses on numerical and computational aspects of the EnKF. In response to the increasing computational challenge posed by the ever more ambitious configurations, an ever larger fraction of the EnKF software system has gradually been parallelized over the past decade. In a strong scaling experiment, the way in which the execution time decreases as larger numbers of processes are used is investigated. In fact, using a substantial fraction of one of the CMC's computers, very short execution times are achieved. As it would thus appear that the CMC's computers can handle more demanding configurations, weak scaling experiments are also performed. Here, both the size of the problem and the number of processes are simultaneously increased. The parallel algorithm responds well to an increase in either the number of ensemble members or the number of model coordinates. A substantial increase (by an order of magnitude) in the number of assimilated observations would, however, be more problematic. Thus, to the extent that this depends on computational aspects, it appears that the meteorological quality of the Canadian operational EnKF can be further improved. [ABSTRACT FROM AUTHOR]

This paper compares three existing Palmer Drought Severity Index (PDSI) formulations for simulating summer moisture variability in western Canada and a preliminary analysis of climate change impacts on summer moisture anomalies. The three models considered are Palmer's original algorithm (orPDSI), the self-calibrating PDSI (scPDSI), and a version modified for Canadian Prairie conditions (cpPDSI). In all formulations, potential evapotranspiration was parameterized by the Penman-Monteith method instead of the traditional Thornthwaite method. The scPDSI was used as a benchmark for evaluation as it is more appropriate for comparing drought severity of diverse climates. The results confirm that orPDSI produces inflated drought statistics as compared to scPDSI, whereas cpPDSI produced more conservative drought statistics than scPDSI. On the basis of results from scPDSI, historical moisture availability in the Canadian Prairies has shown a significant downward trend since 1950 at the 5% level, whereas southern British Columbia has shown a significant increasing trend. No discernible trend was found in the northern parts of the study area. These results were corroborated by trends in annual precipitation and summer temperature over the respective regions. When scPDSI parameters were calibrated using historical climate data, simulations for the 2050s using climate change scenarios from the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) showed increases in summer moisture deficit relative to the 1961-90 baseline. However, projecting the extent to which the frequency of extreme drought and/or wet spell categories will change is not trivial since the computation of scPDSI is tied to the definition of the frequency of extreme events. [ABSTRACT FROM AUTHOR]

Objective: To develop a triage-based screening algorithm and treatment order-sets aimed at improving the quality of care of all patients with sepsis presenting to our emergency department (ED).Design: Retrospective cohort study conducted during a pre-intervention period from 1 April 2010 to 31 March 2011 and a post-intervention period from 1 September 2014 to 30 April 2015.Setting: A large teaching hospital located in Toronto, Ontario, Canada with a 35-bed ED.Participants: All patients meeting pre-specified sepsis criteria during the ED encounter.Main Outcome Measures: Process of care measures included time to assessment by emergency physician, lactate measurement, blood culture collection, fluid and antibiotic administration. Intensive care unit (ICU) outcomes including admissions, length of stay (LOS) and deaths were reviewed.Results: There were 346 patients pre-intervention, and 270 patients post-intervention. We significantly improved all process measures including mean time to antibiotics by 60 min (P = 0.003) and proportion of patients receiving fluid resuscitation (64.7% vs. 94.4%, P < 0.001). There was no significant difference in the number of patients admitted to ICU (P = 0.14). The median ICU LOS was shorter in the post-intervention group [2.0 days (interquartile range (IQR) 1.0-4.5 days) vs. 5.0 days (IQR 1.5-10.8 days), P = 0.04], and there was no difference in in-hospital mortality between groups (P = 0.27).Conclusions: We have demonstrated that a triage-based sepsis screening tool results in expedited and consistent delivery of care, with a significant improvement in initial resuscitation measures. [ABSTRACT FROM AUTHOR]

New digital technologies are changing the nature and contexts of work in Canada. It is essential that education policy and practice acknowledge and respond to these changes. The impacts and implications of new and emerging technologies for work can be summarized within two paradigms: technology is replacing work through automation and digital Taylorism; and technology is changing communication, collaboration and knowledge creation. Derived from a SSHRC Knowledge Synthesis report, this article explores how nurturing uniquely human abilities by employing a threshold concept approach will help create education policy and practice that can better prepare students for the realities of the evolving knowledge-based creative economy. Highlighting the complexity and transdisciplinary nature of knowledge, The New Literacies Threshold Concepts in English Language Arts are presented as a curriculum heuristic that is wellsuited to developing uniquely human abilities. [ABSTRACT FROM AUTHOR]

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Background: Disease incidence and prevalence are both core indicators of population health. Incidence is generally not as readily accessible as prevalence. Cohort studies and electronic health record systems are two major way to estimate disease incidence. The former is time-consuming and expensive; the latter is not available in most developing countries. Alternatively, mathematical models could be used to estimate disease incidence from prevalence.Methods: We proposed and validated a method to estimate the age-standardized incidence of cardiovascular disease (CVD), with prevalence data from successive surveys and mortality data from empirical studies. Hallett's method designed for estimating HIV infections in Africa was modified to estimate the incidence of myocardial infarction (MI) in the U.S. population and incidence of heart disease in the Canadian population.Results: Model-derived estimates were in close agreement with observed incidence from cohort studies and population surveillance systems. This method correctly captured the trend in incidence given sufficient waves of cross-sectional surveys. The estimated MI declining rate in the U.S. population was in accordance with the literature. This method was superior to closed cohort, in terms of the estimating trend of population cardiovascular disease incidence.Conclusion: It is possible to estimate CVD incidence accurately at the population level from cross-sectional prevalence data. This method has the potential to be used for age- and sex- specific incidence estimates, or to be expanded to other chronic conditions. [ABSTRACT FROM AUTHOR]

This paper describes a successful application of classical inventory theory at a drug manufacturing plant located in Montreal that has resulted in annual savings in the order of $110,800 and assisted management in its decision to invest in larger production equipment. The basic assumptions underlying EOQ models are reviewed and dealt with. For instance, an algorithm was devised to handle the peaks in the demand of many A-class items. Following the implementation of this algorithm, buffer stocks were reduced significantly while maintaining the same high levels of service. [ABSTRACT FROM AUTHOR]

This paper examines the problems of routing freight traffic, scheduling train services and allocating classification work between yards on a rail network. It describes a general optimization model which takes into account the interactions between these activities and develops global strategies for their management over a medium term planning horizon. A heuristic algorithm developed to solve this nonlinear, mixed-integer multicommodity flow problem is also presented with some computational results based on an example at the Canadian National Railroads. [ABSTRACT FROM AUTHOR]

The recently observed reduction in perennial ice in the Arctic has given rise to a corresponding increase in seasonal ice, which includes young ice (YI). This type of ice has a major impact on the weather and climate systems. However, only a limited number of studies have been dedicated to explore its spatial coverage and duration. This is mainly due to the lack of remote sensing tools that can identify it. This study uses an ice type and concentration retrieval algorithm, namely, Environment Canada's Ice Concentration Extractor, to study YI distribution and duration in the Arctic during seven ice formation seasons: 2002–03 to 2008–09. Results on the YI area, peak period, duration, and its interannual variability are presented in six regions covering the Arctic Basin. Duration is presented in terms of two parameters that describe the peak period and the number of days when YI concentration exceeds 50%. Probability distribution of the latter parameter shows that YI survives very few days before it grows into first-year ice. The summer of the minimum ice record in 2007 did not leave a remarkable impact on the subsequent YI area or its duration, although a delay in ice formation is observed. YI in the North Water polynya is also studied and shows no particular trend, although it varies between years. Anomalies are explained in terms of modeled surface temperature and wind. [ABSTRACT FROM AUTHOR]

Purpose The purpose of the present systematic review was to develop a practice guideline to inform health care providers about screening, assessment, and effective management of cancer-related fatigue (CRF) in adults. Methods The internationally endorsed ADAPTE methodology was used to develop a practice guideline for pan- Canadian use. A systematic search of the literature identified a broad range of evidence: clinical practice guidelines, systematic reviews, and other guidance documents on the screening, assessment, and management of CRF. The search included MEDLINE, EMBASE, CINAHL, the Cochrane Library, and other guideline and data sources to December 2009. Results Two clinical practice guidelines were identified for adaptation. Seven guidance documents and four systematic reviews also provided supplementary evidence to inform guideline recommendations. Health professionals across Canada provided expert feedback on the adapted recommendations in the practice guideline and algorithm through a participatory external review process. Conclusions Practice guidelines can facilitate the adoption of evidence-based assessment and interventions for adult cancer patients experiencing fatigue. Development of an algorithm to guide decision-making in practice may also foster the uptake of a guideline into routine care. [ABSTRACT FROM AUTHOR]

The objective of the study was to evaluate the efficacy of an interdisciplinary intervention known as Educoeur in reducing cardiovascular risk and improving health behaviors in people without evidence of cardiovascular disease and to compare the Educoeur program to interventions in a specialized clinic and in usual care family practice. In a parallel, randomized, controlled trial of 185 adults with at least two modifiable cardiovascular risk factors, patients were randomly assigned to either Educoeur, specialized clinic or usual care. Cardiovascular risk, biological and lifestyle measures were assessed at baseline and at 2 years. In Educoeur, measurements were also taken before and after the lifestyle group treatment program. In 12 weeks, patients in Educoeur significantly lowered their cardiovascular risk, weight, body mass index, waist circumference, systolic blood pressure, kilocalories intake and improved their VO Max and mental health. Changes remained significant at 2 years. Between group comparisons at 2 years demonstrated that Educoeur was significantly better in reducing cardiovascular risk than interventions in usual care. Together, these results highlight the importance of providing interdisciplinary programs that optimize cardiovascular risk reduction and promote active lifestyles in patients at risk of cardiovascular disease. [ABSTRACT FROM AUTHOR]

Background The widespread use of risk algorithms in clinical medicine is testimony to how they have helped transform clinical decision-making. Risk algorithms have a similar but underdeveloped potential to support decision-making for population health. Objective To describe the role of predictive risk algorithms in a population setting. Methods First, predictive risk algorithms and how clinicians use them are described. Second, the population uses of risk algorithms are described, highlighting the strengths of risk algorithms for health planning. Lastly, the way in which predictive risk algorithms are developed is discussed briefly and a guide for algorithm assessment in population health presented. Conclusion For the past 20 years, absolute and baseline risk has been a cornerstone of population health planning. The most accurate and discriminating method to generate such estimates is the use of multivariable risk algorithms. Routinely collected data can be used to develop algorithms with characteristics that are well suited to health planning and such data are increasingly available. The widespread use of risk algorithms in clinical medicine is testimony to how they have helped transform clinical decision-making. Risk algorithms have a similar but underdeveloped potential to support decision-making for population health. INSETS: Case example of risk prediction in clinical medicined algorithms.;Case example of population use of a clinical risk algorithm. [ABSTRACT FROM AUTHOR]

An algorithm is developed to determine the flight icing threat to aircraft utilizing quantitative information on clouds derived from meteorological satellite data as input. Algorithm inputs include the satellite-derived cloud-top temperature, thermodynamic phase, water path, and effective droplet size. The icing-top and -base altitude boundaries are estimated from the satellite-derived cloud-top and -base altitudes using the freezing level obtained from numerical weather analyses or a lapse-rate approach. The product is available at the nominal resolution of the satellite pixel. Aircraft pilot reports (PIREPs) over the United States and southern Canada provide direct observations of icing and are used extensively in the algorithm development and validation on the basis of correlations with Geostationary Operational Environmental Satellite imager data. Verification studies using PIREPs, Tropospheric Airborne Meteorological Data Reporting, and NASA Icing Remote Sensing System data indicate that the satellite algorithm performs reasonably well, particularly during the daytime. The algorithm is currently being run routinely using data taken from a variety of satellites across the globe and is providing useful information on icing conditions at high spatial and temporal resolutions that are unavailable from any other source. [ABSTRACT FROM AUTHOR]

In order to better constrain the utility of multispectral datasets in the characterization of surface materials, Landsat Thematic Mapper (TM) data were evaluated in the discrimination of geological classes in the Cape Smith Belt of Quebec, a greenstone belt that hosts Early Proterozoic units including those of the Purtuniq ophiolite. Ground-based measurements collected for the study area highlight the importance of chemical alteration in controlling the reflectance properties of key geological classes. The spatial distribution of exposed lithologies in the study area was determined through (1) image classification using a feedforward backpropagation neural network classifier; and (2) generation of fraction images for spectral end members using a linear unmixing algorithm and ground reflectance data. Despite some shortcomings, the database of surface cover generated by the neural network classifier is a useful representation of the spatial distribution of exposed geological materials in the study area, with an overall agreement with ground truth of 87.7%. In contrast, the fraction images generated through unmixing are poor representations of ground truth for several key lithological classes. These results underscore both the considerable utility and marked limitations of Landsat TM data in the mapping of igneous and metamorphic lithologies. [ABSTRACT FROM AUTHOR]

The main task of this study is to introduce a statistical postprocessing algorithm to reduce the bias in the National Centers for Environmental Prediction (NCEP) and Meteorological Service of Canada (MSC) ensemble forecasts before they are merged to form a joint ensemble within the North American Ensemble Forecast System (NAEFS). This statistical postprocessing method applies a Kalman filter type algorithm to accumulate the decaying averaging bias and produces bias-corrected ensembles for 35 variables. NCEP implemented this bias-correction technique in 2006. NAEFS is a joint operational multimodel ensemble forecast system that combines NCEP and MSC ensemble forecasts after bias correction. According to operational statistical verification, both the NCEP and MSC bias-corrected ensemble forecast products are enhanced significantly. In addition to the operational calibration technique, three other experiments were designed to assess and mitigate ensemble biases on the model grid: a decaying averaging bias calibration method with short samples, a climate mean bias calibration method, and a bias calibration method using dependent data. Preliminary results show that the decaying averaging method works well for the first few days. After removing the decaying averaging bias, the calibrated NCEP operational ensemble has improved probabilistic performance for all measures until day 5. The reforecast ensembles from the Earth System Research Laboratory's Physical Sciences Division with and without the climate mean bias correction were also examined. A comparison between the operational and the bias-corrected reforecast ensembles shows that the climate mean bias correction can add value, especially for week-2 probability forecasts. [ABSTRACT FROM AUTHOR]

Background: The EQ-5D is a preference based instrument which provides a description of a respondent's health status, and an empirically derived value for that health state often from a representative sample of the general population. It is commonly used to derive Quality Adjusted Life Year calculations (QALY) in economic evaluations. However, values for health states have been found to differ between countries. The objective of this study was to develop a set of values for the EQ-5D health states for use in Canada. Methods: Values for 48 different EQ-5D health states were elicited using the Time Trade Off (TTO) via a web survey in English. A random effect model was fitted to the data to estimate values for all 243 health states of the EQ-5D. Various model specifications were explored. Comparisons with EQ-5D values from the UK and US were made. Sensitivity analysis explored different transformations of values worse than dead, and exclusion criteria of subjects. Results: The final model was estimated from the values of 1145 subjects with socio-demographics broadly representative of Canadian general population with the exception of Quebec. This yielded a good fit with observed TTO values, with an overall R2 of 0.403 and a mean absolute error of 0.044. Conclusion: A preference-weight algorithm for Canadian studies that include the EQ-5D is developed. The primary limitations regarded the representativeness of the final sample, given the language used (English only), the method of recruitment, and the difficulty in the task. Insights into potential issues for conducting valuation studies in countries as large and diverse as Canada are gained. [ABSTRACT FROM AUTHOR]

A new algorithm, called Environment Canada's Ice Concentration Extractor (ECICE), has been developed to calculate total ice concentration and partial concentration of each ice type from remote-sensing observations. It employs two new concepts. First, it obtains a best estimate of ice concentrations by minimizing the sum of squared difference between observed and estimated radiometric values based on a linear radiometric model for each ice type. Second, instead of employing a single radiometric value (tie point) for each ice type, it utilizes the probability density distribution of the radiometric values for each ice type. Then, in a Monte Carlo simulation, 1000 radiometric values are randomly selected, total and ice-type concentrations are calculated by solving the minimization problem, and finally, median values from the 1000 simulations are chosen. The algorithm was applied to the winter sea ice in the Gulf of St. Lawrence, Canada, us- ing observations from Special Sensor Microwave Imager (SSM/I) 85-GHz channel. Results were evaluated against ice concentration estimates from the operational analysis of Radarsat images at the Canadian Ice Service (CIS). Statistics of the differences between the output concentration and CIS estimates show that ECICE can successfully identify open water and consolidated pack ice pixels better than the Enhanced NASA Team algorithm. However, in areas of ice concentrations between 20% and 70%, the algorithm's performance could not be precisely evaluated because the typical size of the CIS's analysis polygon is much larger than the footprint of the 85-GHz SSM/I channel. Hence, the algorithm captures information at a finer spatial scale. Examples of using one, two, and three radiometric parameters to calculate the concentrations are presented. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) is an important surface variable for monitoring the status of vegetation and as input in a number of ecosystem process models. There are currently several coarse-resolution LAI maps over Canada, including a Canada Centre for Remote Sensing ten-day, 1-km resolution, Canada-wide product based on SPOT-4 VEGETATION (VGT), a MODIS eight-day, 1-km resolution, global product and a monthly, 7-km resolution, global map produced using POLDER-1. These products are difficult to validate because of their large spatial extent and coarse resolution. In this study we use in situ LAI measurements collected over a wide range of forest types and ecological zones in Canada to derive 30-m resolution reference LAI maps based on robust error-in-measurement regressions to Landsat Enhanced Thematic Mapper Plus vegetation indices. The reference maps and LAI products were aggregated to a coarser resolution (3 km for MODIS and VGT and 7 km for POLDER) before comparison to account for registration errors, and variability in sensor projected point spread functions. Spatially corresponding aggregated pixels with both high-quality reference and coarse scale LAI retrievals were compared. The comparison shows reasonable agreement (biases less than 25% or one LAI) between the VGT and reference LAI. The MODIS LAI product showed weak correlations (R² < 0.25) over all sites at the scale of comparison and typically overestimated reference LAI in mixed forests by approximately 200%. The POLDER LAI product, only available in June 1997, showed almost no correlation to the summer reference LAI datasets. It underestimated reference LAI for an early growing season with an extent, in some cases, greater than the seasonal differences in LAI. This independent validation of three large area LAI products suggests that there may be substantial biases due to the lack of regional tuning of retrieval algorithms. These biases are far larger than the uncertainties in the reference-based LAI scenes in the case of the MODIS product. This suggests that reliable LAI maps may require regional calibration to meet the Global Terrestrial Observing System mapping requirements of ± 15 % uncertainties. [ABSTRACT FROM AUTHOR]

Background: With the introduction of ICD-10 throughout Canada, it is important to ensure that Acute Myocardial Infarction (AMI) comorbidities employed in risk adjustment methods remain valid and robust. Therefore, we developed ICD-10 coding algorithms for nine AMI comorbidities, examined the validity of the ICD-10 and ICD-9 coding algorithms in detection of these comorbidities, and assessed their performance in predicting mortality. The nine comorbidities that we examined were shock, diabetes with complications, congestive heart failure, cancer, cerebrovascular disease, pulmonary edema, acute renal failure, chronic renal failure, and cardiac dysrhythmias. Methods: Coders generated a comprehensive list of ICD-10 codes corresponding to each AMI comorbidity. Physicians independently reviewed and determined the clinical relevance of each item on the list. To ensure that the newly developed ICD-10 coding algorithms were valid in recording comorbidities, medical charts were reviewed. After assessing ICD-10 algorithms' validity, both ICD-10 and ICD-9 algorithms were applied to a Canadian provincial hospital discharge database to predict in-hospital, 30-day, and 1-year mortality. Results: Compared to chart review data as a 'criterion standard', ICD-9 and ICD-10 data had similar sensitivities (ranging from 7.1 - 100%), and specificities (above 93.6%) for each of the nine AMI comorbidities studied. The frequencies for the comorbidities were similar between ICD-9 and ICD-10 coding algorithms for 49,861 AMI patients in a Canadian province during 1994 - 2004. The C-statistics for predicting 30-day and 1 year mortality were the same for ICD-9 (0.82) and for ICD-10 data (0.81). Conclusion: The ICD-10 coding algorithms developed in this study to define AMI comorbidities performed similarly as past ICD-9 coding algorithms in detecting conditions and risk-adjustment in our sample. However, the ICD-10 coding algorithms should be further validated in external databases. [ABSTRACT FROM AUTHOR]

In a day and age of rapid technological growth and advancement in digital technology, quantum computing, and decentralized cloud computing, it is difficult to get excited about USB sticks, those little dongles that store only a few gigabytes and commonly get lost in the bottom of your bag. Well, they are making a major comeback at our institution in Canada. That is right, when Stanford and MIT are making the next Facebook and autonomous vehicles, we are bringing USB back. [ABSTRACT FROM AUTHOR]

The accuracy of data-driven predictive mineral prospectivity models relies heavily on the training datasets used. These models are usually trained using data for "known" deposit locations as well as "non-deposit" locations that are based on randomly generated point patterns. In this study, data related to the Seabridge Gold Inc Iskut project, an epithermal Au deposit in northwestern British Columbia (BC), Canada, are used to test the utility of data-driven mineral prospectivity modeling. The input spatial dataset is comprised mostly of publicly available data. Data for 18 vein and epithermal Au known mineral occurrences (KMO) are obtained from the BC Geological Survey's MINFILE repository and selected as training deposit locations. A total of eleven sets of non-deposit locations (NDL) were also created, including one set of selected non-prospective KMO for Au deposits from the MINFILE and ten sets of random point patterns. Given the scale of this study, most of the KMO recorded on the property are of the epithermal deposit type. Hence, they could not be used as a selection criterion. Data-driven mineral potential models are generated using the random forest (RF) algorithm and trained on multiple data sets. The comparison of RF models demonstrated that using non-prospective KMO generates more accurate predictions than the random point pattern. The produced mineral prospectivity maps delineated multiple areas with higher discovery potential, which matched viable targets for the Au-Cu epithermal-porphyry system identified through previous Seabridge Gold Inc. (Toronto, ON, Canada) field reconnaissance and drilling programs. [ABSTRACT FROM AUTHOR]

Terrestrial laser scanners provide accurate and detailed point clouds of forest plots, which can be used as an alternative to destructive measurements during forest inventories. Various specialized algorithms have been developed to provide automatic and objective estimates of forest attributes from point clouds. The STEP (Snakes for Tuboid Extraction from Point cloud) algorithm was developed to estimate both stem diameter at breast height and stem diameters along the bole length. Here, we evaluate the accuracy of this algorithm and compare its performance with two other state-of-the-art algorithms that were designed for the same purpose (i.e., the CompuTree and SimpleTree algorithms). We tested each algorithm against point clouds that incorporated various degrees of noise and occlusion. We applied these algorithms to three contrasting test sites: (1) simulated scenes of coniferous stands in Newfoundland (Canada), (2) test sites of deciduous stands in Phalsbourg (France), and (3) coniferous plantations in Quebec, Canada. In most cases, the STEP algorithm predicted diameter at breast height with higher R2 and lower RMSE than the other two algorithms. The STEP algorithm also achieved greater accuracy when estimating stem diameter in occluded and noisy point clouds, with mean errors in the range of 1.1 cm to 2.28 cm. The CompuTree and SimpleTree algorithms respectively produced errors in the range of 2.62 cm to 6.1 cm and 1.03 cm to 3.34 cm, respectively. Unlike CompuTree or SimpleTree, the STEP algorithm was not able to estimate trunk diameter in the uppermost portions of the trees. Our results show that the STEP algorithm is more adapted to extract DBH and stem diameter automatically from occluded and noisy point clouds. Our study also highlights that SimpleTree and CompuTree require data filtering and results corrections. Conversely, none of these procedures were applied for the implementation of the STEP algorithm. [ABSTRACT FROM AUTHOR]

Background: Current methods for evaluating food marketing to children often study a single marketing channel or approach. As the World Health Organization urges the removal of unhealthy food marketing in children's settings, methods that comprehensively explore the exposure and power of food marketing within a setting from multiple marketing channels and approaches are needed. The purpose of this study was to test the inter-rater reliability and the validity of a novel settings-based food marketing audit tool. Methods: The Food and beverage Marketing Assessment Tool for Settings (FoodMATS) was developed and its psychometric properties evaluated in five public recreation and sport facilities (sites) and subsequently used in 51 sites across Canada for a cross-sectional analysis of food marketing. Raters recorded the count of food marketing occasions, presence of child-targeted and sports-related marketing techniques, and the physical size of marketing occasions. Marketing occasions were classified by healthfulness. Inter-rater reliability was tested using Cohen's kappa (κ) and intra-class correlations (ICC). FoodMATS scores for each site were calculated using an algorithm that represented the theoretical impact of the marketing environment on food preferences, purchases, and consumption. Higher FoodMATS scores represented sites with higher exposure to, and more powerful (unhealthy, child-targeted, sports-related, large) food marketing. Validity of the scoring algorithm was tested through (1) Pearson's correlations between FoodMATS scores and facility sponsorship dollars, and (2) sequential multiple regression for predicting "Least Healthy" food sales from FoodMATS scores. Results: Inter-rater reliability was very good to excellent (κ = 0.88–1.00, p < 0.001; ICC = 0.97, p < 0.001). There was a strong positive correlation between FoodMATS scores and food sponsorship dollars, after controlling for facility size (r = 0.86, p < 0.001). The FoodMATS score explained 14% of the variability in "Least Healthy" concession sales (p = 0.012) and 24% of the variability total concession and vending "Least Healthy" food sales (p = 0.003). Conclusions: FoodMATS has high inter-rater reliability and good validity. As the first validated tool to evaluate the exposure and power of food marketing in recreation facilities, the FoodMATS provides a novel means to comprehensively track changes in food marketing environments that can assist in developing and monitoring the impact of policies and interventions. [ABSTRACT FROM AUTHOR]

ACADEMIC EMERGENCY MEDICINE 2012; 19:640-649 © 2012 by the Society for Academic Emergency Medicine Abstract Objectives: Adverse drug events (ADEs) are unintended and harmful consequences of medication use. They are associated with high health resource use and cost. Yet, high levels of inaccuracy exist in their identification in clinical practice, with over one-third remaining unidentified in the emergency department (ED). The study objective was to derive clinical decision rules (CDRs) that are sensitive for the detection of ADEs, allowing their systematic identification early in a patient's hospital course. Methods: This was a prospective observational cohort study carried out in two Canadian tertiary care hospitals. Participants were adults presenting to the ED having ingested at least one prescription or over-the-counter medication within 2 weeks. Nurses and physicians evaluated patients for standardized clinical findings. A second evaluator performed interobserver assessments of predictor variables in a subset of patients. Pharmacists, who were blinded to the predictor variables, evaluated all patients for ADEs. An independent committee reviewed and adjudicated cases where the ADE assessment was uncertain or the pharmacist's diagnosis differed from the physician's working diagnosis. The primary outcome was an ADE that required a change in medical therapy, diagnostic testing, consultation, or hospital admission. CDRs were derived using kappa coefficients, chi-square statistics, and recursive partitioning. Results: Among 1,591 patients, 131 (8.2%, 95% confidence interval [CI] = 7.0% to 9.7%) were diagnosed with the primary outcome. The following variables were associated with ADEs and were used to derive two CDRs: 1) presence of comorbid conditions, 2) antibiotic use within 7 days, 3) medication changes within 28 days, 4) age ≥80 years, 5) arrival by ambulance, 6) triage acuity, 7) recent hospital admission, 8) renal failure, and 9) use of three or more prescription medications. The more sensitive rule had a sensitivity of 96.7% (95% CI = 91.8% to 98.6%) and required 40.8% (95% CI = 37.7% to 42.9%) of patients to have medication review. The more specific rule had a sensitivity 90.8% (95% CI = 81.4% to 95.7%) and required 28.3% of patients to proceed to medication review. Conclusions: The authors derived CDRs that identified patients with ADEs with high sensitivity. These rules may improve the identification of ADEs early in a patient's hospital course while limiting the number of patients requiring a detailed medication review. [ABSTRACT FROM AUTHOR]