Biomedical subjects

Michael M Wagner

Publications and source records attributed to Michael M Wagner.

At least 19 recordsLinked to original sources

PubMed2005

Generating a reliable reference standard set for syndromic case classification.

OBJECTIVE: To generate and measure the reliability for a reference standard set with representative cases from seven broad syndromic case definitions and several narrower syndromic definitions used for biosurveillance. DESIGN: From 527,228 eligible patients between 1990 and 2003, we generated a set of patients potentially positive for seven syndromes by classifying all eligible patients according to their ICD-9 primary discharge diagnoses. We selected a representative subset of the cases for chart review by physicians, who read emergency department reports and assigned values to 14 variables related to the seven syndromes. MEASUREMENTS: (1) Positive predictive value of the ICD-9 diagnoses; (2) prevalence of the syndromic definitions and related variables; (3) agreement between physician raters demonstrated by kappa, kappa corrected for bias and prevalence, and Finn's r; and (4) reliability of the reference standard classifications demonstrated by generalizability coefficients. RESULTS: Positive predictive value for ICD-9 classification ranged from 0.33 for botulinic to 0.86 for gastrointestinal. We generated between 80 and 566 positive cases for six of the seven syndromic definitions. Rash syndrome exhibited low prevalence (34 cases). Agreement between physician raters was high, with kappa > 0.70 for most variables. Ratings showed no bias. Finn's r was >0.70 for all variables. Generalizability coefficients were >0.70 for all variables but three. CONCLUSION: Of the 27 syndromes generated by the 14 variables, 21 showed high enough prevalence, agreement, and reliability to be used as reference standard definitions against which an automated syndromic classifier could be compared. Syndromic definitions that showed poor agreement or low prevalence include febrile botulinic syndrome, febrile and nonfebrile rash syndrome, respiratory syndrome explained by a nonrespiratory or noninfectious diagnosis, and febrile and nonfebrile gastrointestinal syndrome explained by a nongastrointestinal or noninfectious diagnosis.

Bioterrorism↗

PubMed2005

Classification of emergency department chief complaints into 7 syndromes: a retrospective analysis of 527,228 patients.

STUDY OBJECTIVE: Electronic surveillance systems often monitor triage chief complaints in hopes of detecting an outbreak earlier than can be accomplished with traditional reporting methods. We measured the accuracy of a Bayesian chief complaint classifier called CoCo that assigns patients 1 of 7 syndromic categories (respiratory, botulinic, gastrointestinal, neurologic, rash, constitutional, or hemorrhagic) based on free-text triage chief complaints. METHODS: We compared CoCo's classifications with criterion syndromic classification based on International Classification of Diseases, Ninth Revision (ICD-9) discharge diagnoses. We assigned the criterion classification to a patient based on whether the patient's primary diagnosis was a member of a set of ICD-9 codes associated with CoCo's 7 syndromes. We tested CoCo's performance on a set of 527,228 chief complaints from patients registered at the University of Pittsburgh Medical Center emergency department (ED) between 1990 and 2003. We performed a sensitivity analysis by varying the ICD-9 codes in the criterion standard. We also tested CoCo on chief complaints from EDs in a second location (Utah). RESULTS: Approximately 16% (85,569/527,228) of the patients were classified according to the criterion standard into 1 of the 7 syndromes. CoCo's classification performance (number of cases by criterion standard, sensitivity [95% confidence interval (CI)], and specificity [95% CI]) was respiratory (34,916, 63.1 [62.6 to 63.6], 94.3 [94.3 to 94.4]); botulinic (1,961, 30.1 [28.2 to 32.2], 99.3 [99.3 to 99.3]); gastrointestinal (20,431, 69.0 [68.4 to 69.6], 95.6 [95.6 to 95.7]); neurologic (7,393, 67.6 [66.6 to 68.7], 92.7 [92.6 to 92.8]); rash (2,232, 46.8 [44.8 to 48.9], 99.3 [99.3 to 99.3]); constitutional (10,603, 45.8 [44.9 to 46.8], 96.6 [96.6 to 96.7]); and hemorrhagic (8,033, 75.2 [74.3 to 76.2], 98.5 [98.4 to 98.5]). The sensitivity analysis showed that the results were not affected by the choice of ICD-9 codes in the criterion standard. Classification accuracy did not differ on chief complaints from the second location. CONCLUSION: Our results suggest that, for most syndromes, our chief complaint classification system can identify about half of the patients with relevant syndromic presentations, with specificities higher than 90% and positive predictive values ranging from 12% to 44%.

Bayes Theorem↗

PubMed2005

Classifying free-text triage chief complaints into syndromic categories with natural language processing.

OBJECTIVE: Develop and evaluate a natural language processing application for classifying chief complaints into syndromic categories for syndromic surveillance. INTRODUCTION: Much of the input data for artificial intelligence applications in the medical field are free-text patient medical records, including dictated medical reports and triage chief complaints. To be useful for automated systems, the free-text must be translated into encoded form. METHODS: We implemented a biosurveillance detection system from Pennsylvania to monitor the 2002 Winter Olympic Games. Because input data was in free-text format, we used a natural language processing text classifier to automatically classify free-text triage chief complaints into syndromic categories used by the biosurveillance system. The classifier was trained on 4700 chief complaints from Pennsylvania. We evaluated the ability of the classifier to classify free-text chief complaints into syndromic categories with a test set of 800 chief complaints from Utah. RESULTS: The classifier produced the following areas under the ROC curve: Constitutional = 0.95; Gastrointestinal = 0.97; Hemorrhagic = 0.99; Neurological = 0.96; Rash = 1.0; Respiratory = 0.99; Other = 0.96. Using information stored in the system's semantic model, we extracted from the Respiratory classifications lower respiratory complaints and lower respiratory complaints with fever with a precision of 0.97 and 0.96, respectively. CONCLUSION: Results suggest that a trainable natural language processing text classifier can accurately extract data from free-text chief complaints for biosurveillance.

Bayes Theorem↗

PubMed2005

An evaluation of three policies for updating product categories in the National Retail Data Monitor.

A problem in biosurveillance is how frequently to update controlled vocabularies that identify various data elements such as laboratory tests and over-the-counter healthcare products. More frequent updates improve completeness of data captured over time, but introduction of new codes into a surveillance system may cause false alarms when codes are aggregated into analytic categories. We studied the effect of three policies for updating UPCs, the controlled vocabulary for over-the-counter healthcare products used by the National Retail Data Monitor. To compare different policies for updating, we analyzed historical data from two cities for the 18 product categories of the National Retail Data Monitor under annual, quarterly, or monthly UPC update policies. We measured the effect on data completeness and false alarm rate. We found that the monthly update policy had the highest data completeness and led to the fewest number of additional false alarms. Overall, monthly updating of UPCs was the superior policy.

Commerce↗

PubMed2005

Key design elements of a data utility for national biosurveillance: event-driven architecture, caching, and Web service model.

The National Retail Data Monitor (NRDM) has monitored over-the-counter (OTC) medication sales in the United States since December 2002. The NRDM collects data from over 18,600 retail stores and processes over 0.6 million sales records per day. This paper describes key architectural features that we have found necessary for a data utility component in a national biosurveillance system. These elements include event-driven architecture to provide analyses of data in near real time, multiple levels of caching to improve query response time, high availability through the use of clustered servers, scalable data storage through the use of storage area networks and a web-service function for interoperation with affiliated systems. The methods and architectural principles are relevant to the design of any production data utility for public health surveillance-systems that collect data from multiple sources in near real time for use by analytic programs and user interfaces that have substantial requirements for time-series data aggregated in multiple dimensions.

Commerce↗

PubMed2005

Issue a boil-water advisory or wait for definitive information? A decision analysis.

OBJECTIVE: Study the decision to issue a boil-water advisory in response to a spike in sales of diarrhea remedies or wait 72 hours for the results of definitive testing of water and people. METHODS: Decision analysis. RESULTS: In the base-case analysis, the optimal decision is test-and-wait. If the cost of issuing a boil-water advisory is less than 13.92 cents per person per day, the optimal decision is to issue the boil-water advisory immediately. CONCLUSIONS: Decisions based on surveillance data that are suggestive but not conclusive about the existence of a disease outbreak can be modeled.

Antidiarrheals↗

PubMed2004

Syndrome and outbreak detection using chief-complaint data--experience of the Real-Time Outbreak and Disease Surveillance project.

This paper summarizes the experience of the Real-Time Outbreak and Disease Surveillance (RODS) project in collecting and analyzing free-text emergency department (ED) chief complaints. The technical approach involves real-time transmission of chief-complaint data as Health Level 7 messages from hospitals to a regional data center, where a Bayesian text classifier assigns each chief complaint to one of eight syndrome categories. Time-series algorithms analyze the syndrome data and generate alerts. Authorized public health users review the syndrome data by using Internet interfaces with timelines and maps. Deployments in Pennsylvania, Utah, Atlantic City, and Ohio have demonstrated feasibility of real-time collection of chief complaints. Retrospective experiments that measured case-classification accuracy demonstrated that the Bayesian classifier can discriminate between different syndrome presentations. Retrospective experiments that measured outbreak-detection accuracy determined that the classifier's performance was adequate to support accurate and timely detection of seasonal disease outbreaks. Prospective evaluation revealed that a cluster of carbon monoxide exposures was detected by RODS within 4 hours of the presentation of the first case to an emergency department.

Algorithms↗

PubMed2004

National Retail Data Monitor for public health surveillance.

The National Retail Data Monitor (NRDM) is a public health surveillance tool that collects and analyzes daily sales data for over-the-counter (OTC) health-care products. NRDM collects sales data for selected OTC health-care products in near real time from >15,000 retail stores and makes them available to public health officials. NRDM is one of the first examples of a national data utility for public health surveillance that collects, redistributes, and analyzes daily sales-volume data of selected health-care products, thereby reducing the effort for both data providers and health departments.

Disease Outbreaks↗

PubMed2004

Fever detection from free-text clinical records for biosurveillance.

Automatic detection of cases of febrile illness may have potential for early detection of outbreaks of infectious disease either by identification of anomalous numbers of febrile illness or in concert with other information in diagnosing specific syndromes, such as febrile respiratory syndrome. At most institutions, febrile information is contained only in free-text clinical records. We compared the sensitivity and specificity of three fever detection algorithms for detecting fever from free-text. Keyword CC and CoCo classified patients based on triage chief complaints; Keyword HP classified patients based on dictated emergency department reports. Keyword HP was the most sensitive (sensitivity 0.98, specificity 0.89), and Keyword CC was the most specific (sensitivity 0.61, specificity 1.0). Because chief complaints are available sooner than emergency department reports, we suggest a combined application that classifies patients based on their chief complaint followed by classification based on their emergency department report, once the report becomes available.

Algorithms↗

PubMed2004

The RODS Open Source Project: removing a barrier to syndromic surveillance.

The goal of the Real-time Outbreak and Disease Surveillance (RODS) Open Source Project is to accelerate deployment of computer-based syndromic surveillance. To this end, the project has released the RODS software under the GNU General Public License and created an organizational structure to catalyze its development. This paper describes the design of the software, requested extensions, and the structure of the development effort.

Algorithms↗

PubMed2004

Analysis of Web access logs for surveillance of influenza.

The purpose of this study was to determine whether the level of influenza in a population correlates with the number of times that internet users access information about influenza on health-related Web sites. We obtained Web access logs from the Healthlink Web site. Web access logs contain information about the user and the information the user accessed, and are maintained electronically by most Web sites, including Healthlink. We developed weekly counts of the number of accesses of selected influenza-related articles on the Healthlink Web site and measured their correlation with traditional influenza surveillance data from the Centers for Disease Control and Prevention (CDC) using the cross-correlation function (CCF). We defined timeliness as the time lag at which the correlation was a maximum. There was a moderately strong correlation between the frequency of influenza-related article accesses and the CDC's traditional surveillance data, but the results on timeliness were inconclusive. With improvements in methods for performing spatial analysis of the data and the continuing increase in Web searching behavior among Americans, Web article access has the potential to become a useful data source for public health early warning systems.

Centers for Disease Control and Prevention, U.S.↗

PubMed2003

Implementing syndromic surveillance: a practical guide informed by the early experience.

Syndromic surveillance refers to methods relying on detection of individual and population health indicators that are discernible before confirmed diagnoses are made. In particular, prior to the laboratory confirmation of an infectious disease, ill persons may exhibit behavioral patterns, symptoms, signs, or laboratory findings that can be tracked through a variety of data sources. Syndromic surveillance systems are being developed locally, regionally, and nationally. The efforts have been largely directed at facilitating the early detection of a covert bioterrorist attack, but the technology may also be useful for general public health, clinical medicine, quality improvement, patient safety, and research. This report, authored by developers and methodologists involved in the design and deployment of the first wave of syndromic surveillance systems, is intended to serve as a guide for informaticians, public health managers, and practitioners who are currently planning deployment of such systems in their regions.

Bioterrorism↗

PubMed2003

Automated syndromic surveillance for the 2002 Winter Olympics.

The 2002 Olympic Winter Games were held in Utah from February 8 to March 16, 2002. Following the terrorist attacks on September 11, 2001, and the anthrax release in October 2001, the need for bioterrorism surveillance during the Games was paramount. A team of informaticists and public health specialists from Utah and Pittsburgh implemented the Real-time Outbreak and Disease Surveillance (RODS) system in Utah for the Games in just seven weeks. The strategies and challenges of implementing such a system in such a short time are discussed. The motivation and cooperation inspired by the 2002 Olympic Winter Games were a powerful driver in overcoming the organizational issues. Over 114,000 acute care encounters were monitored between February 8 and March 31, 2002. No outbreaks of public health significance were detected. The system was implemented successfully and operational for the 2002 Olympic Winter Games and remains operational today.

Algorithms↗

PubMed2003

Detection of pediatric respiratory and diarrheal outbreaks from sales of over-the-counter electrolyte products.

OBJECTIVE: To determine whether sales of electrolyte products contain a signal of outbreaks of respiratory and diarrheal disease in children and, if so, how much earlier a signal relative to hospital diagnoses. DESIGN: Retrospective analysis was conducted of sales of electrolyte products and hospital diagnoses for six urban regions in three states for the period 1998 through 2001. MEASUREMENTS: Presence of signal was ascertained by measuring correlation between electrolyte sales and hospital diagnoses and the temporal relationship that maximized correlation. Earliness was the difference between the date that the exponentially weighted moving average (EWMA) method first detected an outbreak from sales and the date it first detected the outbreak from diagnoses. The coefficient of determination (r2) measured how much variance in earliness resulted from differences in sales' and diagnoses' signal strengths. RESULTS: The correlation between electrolyte sales and hospital diagnoses was 0.90 (95% CI, 0.87-0.93) at a time offset of 1.7 weeks (95% CI, 0.50-2.9), meaning that sales preceded diagnoses by 1.7 weeks. EWMA with a nine-sigma threshold detected the 18 outbreaks on average 2.4 weeks (95% CI, 0.1-4.8 weeks) earlier from sales than from diagnoses. Twelve outbreaks were first detected from sales, four were first detected from diagnoses, and two were detected simultaneously. Only 26% of variance in earliness was explained by the relative strength of the sales and diagnoses signals (r2 = 0.26). CONCLUSION: Sales of electrolyte products contain a signal of outbreaks of respiratory and diarrheal diseases in children and usually are an earlier signal than hospital diagnoses.

Algorithms↗

PubMed2003

Creating a text classifier to detect radiology reports describing mediastinal findings associated with inhalational anthrax and other disorders.

OBJECTIVE: The aim of this study was to create a classifier for automatic detection of chest radiograph reports consistent with the mediastinal findings of inhalational anthrax. DESIGN: The authors used the Identify Patient Sets (IPS) system to create a key word classifier for detecting reports describing mediastinal findings consistent with anthrax and compared their performances on a test set of 79,032 chest radiograph reports. MEASUREMENTS: Area under the ROC curve was the main outcome measure of the IPS classifier. Sensitivity and specificity of an initial IPS model were calculated based on an existing key word search and were compared against a Boolean version of the IPS classifier. RESULTS: The IPS classifier received an area under the ROC curve of 0.677 (90% CI = 0.628 to 0.772) with a specificity of 0.99 and maximum sensitivity of 0.35. The initial IPS model attained a specificity of 1.0 and a sensitivity of 0.04. CONCLUSION: The IPS system is a useful tool for helping domain experts create a statistical key word classifier for textual reports that is a potentially useful component in surveillance of radiographic findings suspicious for anthrax.

Anthrax↗

PubMed2003

Technical description of RODS: a real-time public health surveillance system.

This report describes the design and implementation of the Real-time Outbreak and Disease Surveillance (RODS) system, a computer-based public health surveillance system for early detection of disease outbreaks. Hospitals send RODS data from clinical encounters over virtual private networks and leased lines using the Health Level 7 (HL7) message protocol. The data are sent in real time. RODS automatically classifies the registration chief complaint from the visit into one of seven syndrome categories using Bayesian classifiers. It stores the data in a relational database, aggregates the data for analysis using data warehousing techniques, applies univariate and multivariate statistical detection algorithms to the data, and alerts users of when the algorithms identify anomalous patterns in the syndrome counts. RODS also has a Web-based user interface that supports temporal and spatial analyses. RODS processes sales of over-the-counter health care products in a similar manner but receives such data in batch mode on a daily basis. RODS was used during the 2002 Winter Olympics and currently operates in two states-Pennsylvania and Utah. It has been and continues to be a resource for implementing, evaluating, and applying new methods of public health surveillance.

Algorithms↗

PubMed2003

Design of a national retail data monitor for public health surveillance.

The National Retail Data Monitor receives data daily from 10,000 stores, including pharmacies, that sell health care products. These stores belong to national chains that process sales data centrally and utilize Universal Product Codes and scanners to collect sales information at the cash register. The high degree of retail sales data automation enables the monitor to collect information from thousands of store locations in near to real time for use in public health surveillance. The monitor provides user interfaces that display summary sales data on timelines and maps. Algorithms monitor the data automatically on a daily basis to detect unusual patterns of sales. The project provides the resulting data and analyses, free of charge, to health departments nationwide. Future plans include continued enrollment and support of health departments, developing methods to make the service financially self-supporting, and further refinement of the data collection system to reduce the time latency of data receipt and analysis.

Algorithms↗

PubMed2003

Representative threats for research in public health surveillance.

A large number of biological agents can cause natural or bioterroristic disease outbreaks and each can present in a bewildering number of ways (e.g., a few cases versus many cases, confined to a building versus widely disseminated). This 'problem space' is a challenge for designers of early warning systems for disease outbreaks and the sheer size of this space is a barrier to progress. This paper addresses this problem by deriving nine categories of threats that represent a parsimonious characterization of the problem space. A literature search also identified one or more example outbreaks for each of the nine categories. These outbreaks have occurred in recent times and could be used by researchers in need of actual outbreak data for investigations of the role of different types of surveillance data and algorithms in outbreak detection. The methodological contribution of this research is a Criterion Set of threats for analysis and evaluation of detection systems. This set characterizes the problem space in a tractable manner with less loss of generality than analyses based on one or two selected diseases, which is representative of current analyses.

Bioterrorism↗