Time-Series Aware Metrics for the Evaluation of Intraoperative Electroencephalography-Based Ischemia Detection.

Continuous intraoperative monitoring with electroencephalo2 graphy (EEG) is commonly used to detect cerebral ischemia in high-risk surgical procedures such as carotid endarterectomy. Machine learning (ML) models that detect ischemia in real time can form the basis of automated intraoperative EEG monitoring. In this study, we describe and compare two time-series aware precision and recall metrics to the classical precision and recall metrics for evaluating the performance of ML models that detect ischemia.

Design and implementation of a clinical laboratory information system in a low-resource setting.

Background: Reducing laboratory errors presents a significant opportunity for both cost reduction and healthcare quality improvement. This is particularly true in low-resource settings where laboratory errors are further exacerbated by poor infrastructure and shortages in a trained workforce. Informatics interventions can be used to address some of the sources of laboratory errors.

User-centered design and usability testing of RxMAGIC: a prescription management and general inventory control system for free clinic dispensaries.

Background: To address challenges related to medication management in underserved settings, we developed a system for Prescription Management And General Inventory Control, or RxMAGIC, in collaboration with the Birmingham Free Clinic in Pittsburgh, Pennsylvania. RxMAGIC is an interoperable, web-based medication management system designed to standardize and streamline the dispensing practice and improve inventory control in a free clinic setting. This manuscript describes the processes used to design, develop, and deploy RxMAGIC.

The center for causal discovery of biomedical knowledge from big data.

The Big Data to Knowledge (BD2K) Center for Causal Discovery is developing and disseminating an integrated set of open source tools that support causal modeling and discovery of biomedical knowledge from large and complex biomedical datasets. The Center integrates teams of biomedical and data scientists focused on the refinement of existing and the development of new constraint-based and Bayesian algorithms based on causal Bayesian networks, the optimization of software for efficient operation in a supercomputing environment, and the testing of algorithms and software developed using real data from 3 representative driving biomedical projects: cancer driver mutations, lung disease, and the functional connectome of the human brain. Associated training activities provide both biomedical and data scientists with the knowledge and skills needed to apply and extend these tools.

A method for detecting and characterizing outbreaks of infectious disease from clinical reports.

Outbreaks of infectious disease can pose a significant threat to human health. Thus, detecting and characterizing outbreaks quickly and accurately remains an important problem. This paper describes a Bayesian framework that links clinical diagnosis of individuals in a population to epidemiological modeling of disease outbreaks in the population.

PaTH: towards a learning health system in the Mid-Atlantic region.

The PaTH (University of Pittsburgh/UPMC, Penn State College of Medicine, Temple University Hospital, and Johns Hopkins University) clinical data research network initiative is a collaborative effort among four academic health centers in the Mid-Atlantic region. PaTH will provide robust infrastructure to conduct research, explore clinical outcomes, link with biospecimens, and improve methods for sharing and analyzing data across our diverse populations. Our disease foci are idiopathic pulmonary fibrosis, atrial fibrillation, and obesity.

Influenza detection from emergency department reports using natural language processing and Bayesian network classifiers.

Objectives: To evaluate factors affecting performance of influenza detection, including accuracy of natural language processing (NLP), discriminative ability of Bayesian network (BN) classifiers, and feature selection.

Methods: We derived a testing dataset of 124 influenza patients and 87 non-influenza (shigellosis) patients. To assess NLP finding-extraction performance, we measured the overall accuracy, recall, and precision of Topaz and MedLEE parsers for 31 influenza-related findings against a reference standard established by three physician reviewers.

A method for estimating from thermometer sales the incidence of diseases that are symptomatically similar to influenza.

Early detection and accurate characterization of disease outbreaks are important tasks of public health. Infectious diseases that present symptomatically like influenza (SLI), including influenza itself, constitute an important class of diseases that are monitored by public-health epidemiologists. Monitoring emergency department (ED) visits for presentations of SLI could provide an early indication of the presence, extent, and dynamics of such disease in the population.

Probabilistic, Decision-theoretic Disease Surveillance and Control.

The Pittsburgh Center of Excellence in Public Health Informatics has developed a probabilistic, decision-theoretic system for disease surveillance and control for use in Allegheny County, PA and later in Tarrant County, TX. This paper describes the software components of the system and its knowledge bases. The paper uses influenza surveillance to illustrate how the software components transform data collected by the healthcare system into population level analyses and decision analyses of potential outbreak-control measures.

Probabilistic case detection for disease surveillance using data in electronic medical records.

This paper describes a probabilistic case detection system (CDS) that uses a Bayesian network model of medical diagnosis and natural language processing to compute the posterior probability of influenza and influenza-like illness from emergency department dictated notes and laboratory results. The diagnostic accuracy of CDS for these conditions, as measured by the area under the ROC curve, was 0.97, and the overall accuracy for NLP employed in CDS was 0.

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.

Removing a barrier to computer-based outbreak and disease surveillance--the RODS Open Source Project.

Introduction: Computer-based outbreak and disease surveillance requires high-quality software that is well-supported and affordable. Developing software in an open-source framework, which entails free distribution and use of software and continuous, community-based software development, can produce software with such characteristics, and can do so rapidly.

Objectives: The objective of the Real-Time Outbreak and Disease Surveillance (RODS) Open Source Project is to accelerate the deployment of computer-based outbreak and disease surveillance systems by writing software and catalyzing the formation of a community of users, developers, consultants, and scientists who support its use.

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.

Telephone triage: a timely data source for surveillance of influenza-like diseases.

We evaluated telephone triage (TT) data for public health early warning systems. TT data is electronically available and contains coded elements that include the demographics and description of a caller's medical complaints. In the study, we obtained emergency room TT data and after hours TT data from a commercial TT software and service company.

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.

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.

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.

Data, network, and application: technical description of the Utah RODS Winter Olympic Biosurveillance System.

Given the post September 11th climate of possible bioterrorist attacks and the high profile 2002 Winter Olympics in the Salt Lake City, Utah, we challenged ourselves to deploy a computer-based real-time automated biosurveillance system for Utah, the Utah Real-time Outbreak and Disease Surveillance system (Utah RODS), in six weeks using our existing Real-time Outbreak and Disease Surveillance (RODS) architecture. During the Olympics, Utah RODS received real-time HL-7 admission messages from 10 emergency departments and 20 walk-in clinics. It collected free-text chief complaints, categorized them into one of seven prodromes classes using natural language processing, and provided a web interface for real-time display of time series graphs, geographic information system output, outbreak algorithm alerts, and details of the cases.

Rapid deployment of an electronic disease surveillance system in the state of Utah for the 2002 Olympic Winter Games.

The key to minimizing the effects of an intentionally caused disease outbreak is early detection of the attack and rapid identification of the affected individuals. The Bush administration's leadership in advocating for biosurveillance systems capable of monitoring for bioterrorism attacks suggests that we should move quickly to establish a nationwide early warning biosurveillance system as a defense against this threat. The spirit of collaboration and unity inspired by the events of 9-11 and the 2002 Olympic Winter Games in Salt Lake City provided the opportunity to demonstrate how a prototypic biosurveillance system could be rapidly deployed.

Roundtable on bioterrorism detection: information system-based surveillance.

During the 2001 AMIA Annual Symposium, the Anesthesia, Critical Care, and Emergency Medicine Working Group hosted the Roundtable on Bioterrorism Detection. Sixty-four people attended the roundtable discussion, during which several researchers discussed public health surveillance systems designed to enhance early detection of bioterrorism events. These systems make secondary use of existing clinical, laboratory, paramedical, and pharmacy data or facilitate electronic case reporting by clinicians.