Estimation of hospital emergency room data using otc pharmaceutical sales and least mean square filters

BACKGROUND: Surveillance of Over-the-Counter pharmaceutical (OTC) sales as a potential early indicator of developing public health conditions, in particular in cases of interest to Bioterrorism, has been suggested in the literature. The data streams of interest are quite non-stationary and we address this problem from the viewpoint of linear adaptive filter theory: the clinical data is the primary channel which is to be estimated from the OTC data that form the reference channels. METHOD: The OTC data are grouped into a few categories and we estimate the clinical data using each individual category, as well as using a multichannel filter that encompasses all the OTC categories. The estimation (in the least mean square sense) is performed using an FIR (Finite Impulse Response) filter and the normalized LMS algorithm. RESULTS: We show all estimation results and present a table of effectiveness of each OTC category, as well as the effectiveness of the combined filtering operation. Individual group results clearly show the effectiveness of each particular group in estimating the clinical hospital data and serve as a guide as to which groups have sustained correlations with the clinical data. CONCLUSION: Our results indicate that Multichannle adaptive FIR least squares filtering is a viable means of estimating public health conditions from OTC sales, and provide quantitative measures of time dependent correlations between the clinical data and the OTC data channels

Vacuum Stability of the wrong sign (−ϕ6)(-\phi^{6}) Scalar Field Theory

We apply the effective potential method to study the vacuum stability of the bounded from above $(-\phi^{6})$ (unstable) quantum field potential. The stability ($\partial E/\partial b=0)$ and the mass renormalization ($\partial^{2} E/\partial b^{2}=M^{2})$ conditions force the effective potential of this theory to be bounded from below (stable). Since bounded from below potentials are always associated with localized wave functions, the algorithm we use replaces the boundary condition applied to the wave functions in the complex contour method by two stability conditions on the effective potential obtained. To test the validity of our calculations, we show that our variational predictions can reproduce exactly the results in the literature for the $\mathcal{PT}$-symmetric $\phi^{4}$ theory. We then extend the applications of the algorithm to the unstudied stability problem of the bounded from above $(-\phi^{6})$ scalar field theory where classical analysis prohibits the existence of a stable spectrum. Concerning this, we calculated the effective potential up to first order in the couplings in $d$ space-time dimensions. We find that a Hermitian effective theory is instable while a non-Hermitian but $\mathcal{PT}$-symmetric effective theory characterized by a pure imaginary vacuum condensate is stable (bounded from below) which is against the classical predictions of the instability of the theory. We assert that the work presented here represents the first calculations that advocates the stability of the $(-\phi^{6})$ scalar potential.Comment: 21pages, 12 figures. In this version, we updated the text and added some figure

Combining Free Text and Structured Electronic Medical Record Entries to Detect Acute Respiratory Infections

The electronic medical record (EMR) contains a rich source of information that could be harnessed for epidemic surveillance. We asked if structured EMR data could be coupled with computerized processing of free-text clinical entries to enhance detection of acute respiratory infections (ARI).A manual review of EMR records related to 15,377 outpatient visits uncovered 280 reference cases of ARI. We used logistic regression with backward elimination to determine which among candidate structured EMR parameters (diagnostic codes, vital signs and orders for tests, imaging and medications) contributed to the detection of those reference cases. We also developed a computerized free-text search to identify clinical notes documenting at least two non-negated ARI symptoms. We then used heuristics to build case-detection algorithms that best combined the retained structured EMR parameters with the results of the text analysis.An adjusted grouping of diagnostic codes identified reference ARI patients with a sensitivity of 79%, a specificity of 96% and a positive predictive value (PPV) of 32%. Of the 21 additional structured clinical parameters considered, two contributed significantly to ARI detection: new prescriptions for cough remedies and elevations in body temperature to at least 38°C. Together with the diagnostic codes, these parameters increased detection sensitivity to 87%, but specificity and PPV declined to 95% and 25%, respectively. Adding text analysis increased sensitivity to 99%, but PPV dropped further to 14%. Algorithms that required satisfying both a query of structured EMR parameters as well as text analysis disclosed PPVs of 52-68% and retained sensitivities of 69-73%.Structured EMR parameters and free-text analyses can be combined into algorithms that can detect ARI cases with new levels of sensitivity or precision. These results highlight potential paths by which repurposed EMR information could facilitate the discovery of epidemics before they cause mass casualties

An adaptive prediction and detection algorithm for multistream syndromic surveillance

<p>Abstract</p> <p>Background</p> <p>Surveillance of Over-the-Counter pharmaceutical (OTC) sales as a potential early indicator of developing public health conditions, in particular in cases of interest to biosurvellance, has been suggested in the literature. This paper is a continuation of a previous study in which we formulated the problem of estimating clinical data from OTC sales in terms of optimal LMS linear and Finite Impulse Response (FIR) filters. In this paper we extend our results to predict clinical data multiple steps ahead using OTC sales as well as the clinical data itself.</p> <p>Methods</p> <p>The OTC data are grouped into a few categories and we predict the clinical data using a multichannel filter that encompasses all the past OTC categories as well as the past clinical data itself. The prediction is performed using FIR (Finite Impulse Response) filters and the recursive least squares method in order to adapt rapidly to nonstationary behaviour. In addition, we inject simulated events in both clinical and OTC data streams to evaluate the predictions by computing the Receiver Operating Characteristic curves of a threshold detector based on predicted outputs.</p> <p>Results</p> <p>We present all prediction results showing the effectiveness of the combined filtering operation. In addition, we compute and present the performance of a detector using the prediction output.</p> <p>Conclusion</p> <p>Multichannel adaptive FIR least squares filtering provides a viable method of predicting public health conditions, as represented by clinical data, from OTC sales, and/or the clinical data. The potential value to a biosurveillance system cannot, however, be determined without studying this approach in the presence of transient events (nonstationary events of relatively short duration and fast rise times). Our simulated events superimposed on actual OTC and clinical data allow us to provide an upper bound on that potential value under some restricted conditions. Based on our ROC curves we argue that a biosurveillance system can provide early warning of an impending clinical event using ancillary data streams (such as OTC) with established correlations with the clinical data, and a prediction method that can react to nonstationary events sufficiently fast. Whether OTC (or other data streams yet to be identified) provide the best source of predicting clinical data is still an open question. We present a framework and an example to show how to measure the effectiveness of predictions, and compute an upper bound on this performance for the Recursive Least Squares method when the following two conditions are met: (1) an event of sufficient strength exists in both data streams, without distortion, and (2) it occurs in the OTC (or other ancillary streams) earlier than in the clinical data.</p

Estimation of hospital emergency room data using otc pharmaceutical sales and least mean square filters-3

<p><b>Copyright information:</b></p><p>Taken from "Estimation of hospital emergency room data using otc pharmaceutical sales and least mean square filters"</p><p>BMC Medical Informatics and Decision Making 2004;4():5-5.</p><p>Published online 15 Mar 2004</p><p>PMCID:PMC419503.</p><p>Copyright © 2004 Najmi and Magruder; licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL.</p>f individual LMS filters as well as the Multi-channel on

Drug sales data analysis for outbreak detection of infectious diseases: a systematic literature review

Article number: 604 (2014)International audienceBackground: This systematic literature review aimed to summarize evidence for the added value of drug sales data analysis for the surveillance of infectious diseases.Methods: A search for relevant publications was conducted in Pubmed, Embase, Scopus, Cochrane Library, African Index Medicus and Lilacs databases. Retrieved studies were evaluated in terms of objectives, diseases studied, data sources, methodologies and performance for real-time surveillance. Most studies compared drug sales data to reference surveillance data using correlation measurements or indicators of outbreak detection performance (sensitivity, specificity, timeliness of the detection).Results: We screened 3266 articles and included 27 in the review. Most studies focused on acute respiratory and gastroenteritis infections. Nineteen studies retrospectively compared drug sales data to reference clinical data, and significant correlations were observed in 17 of them. Four studies found that over-the-counter drug sales preceded clinical data in terms of incidence increase. Five studies developed and evaluated statistical algorithms for selecting drug groups to monitor specific diseases. Another three studies developed models to predict incidence increase from drug sales.Conclusions: Drug sales data analyses appear to be a useful tool for surveillance of gastrointestinal and respiratory disease, and OTC drugs have the potential for early outbreak detection. Their utility remains to be investigated for other diseases, in particular those poorly surveyed