A bayesian approach to wireless location problems

Several approaches for indoor location estimation in wireless networks are proposed. We explore non-hierarchical and hierarchical Bayesian graphical models that use prior knowledge about physics of signal propagation, as well as different modifications of Bayesian bivariate spline models. The hierarchical Bayesian model that incorporates information about locations of access points achieves accuracy that is similar to other published models and algorithms, but by using prior knowledge, this model drastically reduces the requirement for training data when compared to existing approaches. Proposed Bayesian bivariate spline models for location surpass predictive accuracy of existing methods. It has been shown that different versions of this model, in combination with sampling/importance resampling and particle filter algorithms, are suitable for the real-time estimation and tracking of moving objects. It has been demonstrated that plug-in versions of the bivariate Bayesian spline model perform as good as the full Bayesian version. A combination of two Bayesian models to reduce the maximum predictive error is proposed. Models presented in this work utilize MCMC simulations in directed acyclic graphs (DAGs) to solve ill-posed problem of location estimation in wireless networks using only received signal strengths. Similar approaches may be applied to other ill-posed problems

Location Estimation in Wireless Networks: A Bayesian Approach

We present a Bayesian hierarchical model for indoor location estimation in wireless networks. We demonstrate that out model achieves accuracy that is similar to other published models and algorithms. By harnessing prior knowledge, our model drastically reduces the requirement for training data as compared with existing approaches

Bestrophinopathy: An RPE-Photoreceptor Interface Disease

Bestrophinopathies, one of the most common forms of inherited macular degenerations, are caused by mutations in the BEST1 gene expressed in the retinal pigment epithelium (RPE). Both human and canine BEST1-linked maculopathies are characterized by abnormal accumulation of autofluorescent material within RPE cells and bilateral macular or multifocal lesions; however, the specific mechanism leading to the formation of these lesions remains unclear. We now provide an overview of the current state of knowledge on the molecular pathology of bestrophinopathies, and explore factors promoting formation of RPE-neuroretinal separations, using the first spontaneous animal model of BEST1-associated retinopathies, canine Best (cBest). Here, we characterize the nature of the autofluorescent RPE cell inclusions and report matching spectral signatures of RPE-associated fluorophores between human and canine retinae, indicating an analogous composition of endogenous RPE deposits in Best Vitelliform Macular Dystrophy (BVMD) patients and its canine disease model. This study also exposes a range of biochemical and structural abnormalities at the RPE-photoreceptor interface related to the impaired cone-associated microvillar ensheathment and compromised insoluble interphotoreceptor matrix (IPM), the major pathological culprits responsible for weakening of the RPE-neuroretina interactions, and consequently, formation of vitelliform lesions. These salient alterations detected at the RPE apical domain in cBest as well as in BVMD- and ARB-hiPSC-RPE model systems provide novel insights into the pathological mechanism of BEST1-linked disorders that will allow for development of critical outcome measures guiding therapeutic strategies for bestrophinopathies. © 2017 Elsevier Lt

rEHR: An R package for manipulating and analysing Electronic Health Record data

Research with structured Electronic Health Records (EHRs) is expanding as data becomes more accessible; analytic methods advance; and the scientific validity of such studies is increasingly accepted. However, data science methodology to enable the rapid searching/extraction, cleaning and analysis of these large, often complex, datasets is less well developed. In addition, commonly used software is inadequate, resulting in bottlenecks in research workflows and in obstacles to increased transparency and reproducibility of the research. Preparing a research-ready dataset from EHRs is a complex and time consuming task requiring substantial data science skills, even for simple designs. In addition, certain aspects of the workflow are computationally intensive, for example extraction of longitudinal data and matching controls to a large cohort, which may take days or even weeks to run using standard software. The rEHR package simplifies and accelerates the process of extracting ready-for-analysis datasets from EHR databases. It has a simple import function to a database backend that greatly accelerates data access times. A set of generic query functions allow users to extract data efficiently without needing detailed knowledge of SQL queries. Longitudinal data extractions can also be made in a single command, making use of parallel processing. The package also contains functions for cutting data by time-varying covariates, matching controls to cases, unit conversion and construction of clinical code lists. There are also functions to synthesise dummy EHR. The package has been tested with one for the largest primary care EHRs, the Clinical Practice Research Datalink (CPRD), but allows for a common interface to other EHRs. This simplified and accelerated work flow for EHR data extraction results in simpler, cleaner scripts that are more easily debugged, shared and reproduced

A supervised adverse drug reaction signalling framework imitating Bradford Hill’s causality considerations

Big longitudinal observational medical data potentially hold a wealth of information and have been recognised as potential sources for gaining new drug safety knowledge. Unfortunately there are many complexities and underlying issues when analysing longitudinal observational data. Due to these complexities, existing methods for large-scale detection of negative side effects using observational data all tend to have issues distinguishing between association and causality. New methods that can better discriminate causal and non-causal relationships need to be developed to fully utilise the data. In this paper we propose using a set of causality considerations developed by the epidemiologist Bradford Hill as a basis for engineering features that enable the application of supervised learning for the problem of detecting negative side effects. The Bradford Hill considerations look at various perspectives of a drug and outcome relationship to determine whether it shows causal traits. We taught a classifier to find patterns within these perspectives and it learned to discriminate between association and causality. The novelty of this research is the combination of supervised learning and Bradford Hill’s causality considerations to automate the Bradford Hill’s causality assessment. We evaluated the framework on a drug safety gold standard known as the observational medical outcomes partnership’s non-specified association reference set. The methodology obtained excellent discrimination ability with area under the curves ranging between 0.792 and 0.940 (existing method optimal: 0.73) and a mean average precision of 0.640 (existing method optimal: 0.141). The proposed features can be calculated efficiently and be readily updated, making the framework suitable for big observational data

Empirical Performance of a New User Cohort Method: Lessons for Developing a Risk Identification and Analysis System

Background Observational healthcare data offer the potential to enable identification of risks of medical products, but appropriate methodology has not yet been defined. The new user cohort method, which compares the post-exposure rate among the target drug to a referent comparator group, is the prevailing approach for many pharmacoepidemiology evaluations and has been proposed as a promising approach for risk identification but its performance in this context has not been fully assessed. Objectives To evaluate the performance of the new user cohort method as a tool for risk identification in observational healthcare data. Research Design The method was applied to 399 drug-outcome scenarios (165 positive controls and 234 negative controls across 4 health outcomes of interest) in 5 real observational databases (4 administrative claims and 1 electronic health record) and in 6 simulated datasets with no effect and injected relative risks of 1.25, 1.5, 2, 4, and 10, respectively. Measures Method performance was evaluated through Area Under ROC Curve (AUC), bias, and coverage probability. Results The new user cohort method achieved modest predictive accuracy across the outcomes and databases under study, with the top-performing analysis near AUC >0.70 in most scenarios. The performance of the method was particularly sensitive to the choice of comparator population. For almost all drug-outcome pairs there was a large difference, either positive or negative, between the true effect size and the estimate produced by the method, although this error was near zero on average. Simulatio Conclusion The new user cohort method can contribute useful information toward a risk identification system, but should not be considered definitive evidence given the degree of error observed within the effect estimates. Careful consideration of the comparator selection and appropriate calibration of the effect estimates is required in order to properly interpret study findings

Multiple Self-Controlled Case Series for Large-Scale Longitudinal Observational Databases

Characterization of relationships between time-varying drug exposures and adverse events (AEs) related to health outcomes represents the primary objective in postmarketing drug safety surveillance. Such surveillance increasingly utilizes large-scale longitudinal observational databases (LODs), containing time-stamped patient-level medical information including periods of drug exposure and dates of diagnoses for millions of patients. Statistical methods for LODs must confront computational challenges related to the scale of the data, and must also address confounding and other biases that can undermine efforts to estimate effect sizes. Methods that compare on-drug with off-drug periods within patient offer specific advantages over between patient analysis on both counts. To accomplish these aims, we extend the self-controlled case series (SCCS) for LODs. SCCS implicitly controls for fixed multiplicative baseline covariates since each individual acts as their own control. In addition, only exposed cases are required for the analysis, which is computationally advantageous. The standard SCCS approach is usually used to assess single drugs and therefore estimates marginal associations between individual drugs and particular AEs. Such analyses ignore confounding drugs and interactions and have the potential to give misleading results. In order to avoid these difficulties, we propose a regularized multiple SCCS approach that incorporates potentially thousands or more of time-varying confounders such as other drugs. The approach successfully handles the high dimensionality and can provide a sparse solution via an L1 regularizer. We present details of the model and the associated optimization procedure, as well as results of empirical investigations

Empirical Performance of the Self-Controlled Case Series Design: Lessons for Developing a Risk Identification and Analysis System

Background The self-controlled case series (SCCS) offers potential as an statistical method for risk identification involving medical products from large-scale observational healthcare data. However, analytic design choices remain in encoding the longitudinal health records into the SCCS framework and its risk identification performance across real-world databases is unknown. Objectives To evaluate the performance of SCCS and its design choices as a tool for risk identification in observational healthcare data. Research Design We examined the risk identification performance of SCCS across five design choices using 399 drug-health outcome pairs in five real observational databases (four administrative claims and one electronic health records). In these databases, the pairs involve 165 positive controls and 234 negative controls. We also consider several synthetic databases with known relative risks between drug-outcome pairs. Measures We evaluate risk identification performance through estimating the area under the receiver-operator characteristics curve (AUC) and bias and coverage probability in the synthetic examples. Results The SCCS achieves strong predictive performance. Twelve of the twenty health outcome-database scenarios return AUCs >0.75 across all drugs. Including all adverse events instead of just the first per patient and applying a multivariate adjustment for concomitant drug use are the most important design choices. However, the SCCS as applied here returns relative risk point-estimates biased towards the null value of 1 with low coverage probability. Conclusions The SCCS recently extended to apply a multivariate adjustment for concomitant drug use offers promise as a statistical tool for risk identification in largescale observational healthcare databases. Poor estimator calibration dampens enthusiasm, but on-going work should correct this short-coming