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

Recursive least squares background prediction of univariate syndromic surveillance data

<p>Abstract</p> <p>Background</p> <p>Surveillance of univariate syndromic data as a means of potential indicator of developing public health conditions has been used extensively. This paper aims to improve the performance of detecting outbreaks by using a background forecasting algorithm based on the adaptive recursive least squares method combined with a novel treatment of the Day of the Week effect.</p> <p>Methods</p> <p>Previous work by the first author has suggested that univariate recursive least squares analysis of syndromic data can be used to characterize the background upon which a prediction and detection component of a biosurvellance system may be built. An adaptive implementation is used to deal with data non-stationarity. In this paper we develop and implement the RLS method for background estimation of univariate data. The distinctly dissimilar distribution of data for different days of the week, however, can affect filter implementations adversely, and so a novel procedure based on linear transformations of the sorted values of the daily counts is introduced. Seven-days ahead daily predicted counts are used as background estimates. A signal injection procedure is used to examine the integrated algorithm's ability to detect synthetic anomalies in real syndromic time series. We compare the method to a baseline CDC forecasting algorithm known as the W2 method.</p> <p>Results</p> <p>We present detection results in the form of Receiver Operating Characteristic curve values for four different injected signal to noise ratios using 16 sets of syndromic data. We find improvements in the false alarm probabilities when compared to the baseline W2 background forecasts.</p> <p>Conclusion</p> <p>The current paper introduces a prediction approach for city-level biosurveillance data streams such as time series of outpatient clinic visits and sales of over-the-counter remedies. This approach uses RLS filters modified by a correction for the weekly patterns often seen in these data series, and a threshold detection algorithm from the residuals of the RLS forecasts. We compare the detection performance of this algorithm to the W2 method recently implemented at CDC. The modified RLS method gives consistently better sensitivity at multiple background alert rates, and we recommend that it should be considered for routine application in bio-surveillance systems.</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

Anomaly Detection in Time Series: Theoretical and Practical Improvements for Disease Outbreak Detection

The automatic collection and increasing availability of health data provides a new opportunity for techniques to monitor this information. By monitoring pre-diagnostic data sources, such as over-the-counter cough medicine sales or emergency room chief complaints of cough, there exists the potential to detect disease outbreaks earlier than traditional laboratory disease confirmation results. This research is particularly important for a modern, highly-connected society, where the onset of disease outbreak can be swift and deadly, whether caused by a naturally occurring global pandemic such as swine flu or a targeted act of bioterrorism. In this dissertation, we first describe the problem and current state of research in disease outbreak detection, then provide four main additions to the field. First, we formalize a framework for analyzing health series data and detecting anomalies: using forecasting methods to predict the next day's value, subtracting the forecast to create residuals, and finally using detection algorithms on the residuals. The formalized framework indicates the link between the forecast accuracy of the forecast method and the performance of the detector, and can be used to quantify and analyze the performance of a variety of heuristic methods. Second, we describe improvements for the forecasting of health data series. The application of weather as a predictor, cross-series covariates, and ensemble forecasting each provide improvements to forecasting health data. Third, we describe improvements for detection. This includes the use of multivariate statistics for anomaly detection and additional day-of-week preprocessing to aid detection. Most significantly, we also provide a new method, based on the CuScore, for optimizing detection when the impact of the disease outbreak is known. This method can provide an optimal detector for rapid detection, or for probability of detection within a certain timeframe. Finally, we describe a method for improved comparison of detection methods. We provide tools to evaluate how well a simulated data set captures the characteristics of the authentic series and time-lag heatmaps, a new way of visualizing daily detection rates or displaying the comparison between two methods in a more informative way