Effective disease-management activities need accurate prediction of disease occurrences. The timely prediction of disease outbreaks facilitates the effective coordination and mobilization of medical, human, and pharmaceutical resources. Prior knowledge of potential disease occurrences enables proactive development of medical interventions, medical prophylaxis to disease hazards, and containment of disease vectors. This is true for both civilian public health coordination and for the military. Infectious diseases are a significant threat to the armed forces and prior knowledge of disease threats will enable the military to not only plan effective mitigation and containment strategies, but also plan the military engagement timing based on these factors. Traditional epidemiology has focused on compartmental models (susceptibility, exposed, infected, recovered (SEIR) based approaches for forecasting disease progression) [Hethcote 2000; Tebbens et. al., 2005; Mayer, 2013]. There is a need to develop occurrence prediction capabilities for novel, emerging, and re-emerging diseases.Continue reading
The MAES initiative designed and developed technology that collects data from multiple sources, analyzes the data for trends and patterns, and, using handheld devices, deliver context sensitive, location based alerts and advice to users on healthcare, disease, safety, and environmental related issues.
Humanitarian relief, disaster recovery, nation building: these missions are becoming more significant for both military and non-governmental organizations every day. Health awareness and a proactive approach to health outcomes are critical to these missions, but, in many developing countries, even basic reporting of the health status of affected populations can be a daunting task. Communications infrastructures are minimal, transportation may be difficult, time is short, and the focus is often on addressing basic human survival needs. Establishing baselines for epidemiological awareness may be too time consuming and difficult to be a priority.
MMKD is a configurable, dashboard driven knowledge discovery system that allows users without data mining expertise to perform cutting edge knowledge discovery. The technology helps the DoD Medical Logistics community meet the challenges of troop deployments in ever widening combat scenarios.
The Medical Materiel Knowledge Discoverer (MMKD) is a knowledge discovery system for the Department of Defense (DoD) Medical Logistics community that goes beyond simple data mining. The changing nature of military conflicts in the world favor an emphasis on the rapid deployment of troops in an ever-widening variety of scenarios and locales. These developments raise significant logistical challenges and risks for, among other military branches, the DoD medical community.
The BIOWARS technology is an adaptive system for discovering disease outbreaks and impending bio terrorism attacks. BIOWARS uses syndromic surveillance to find symptomatic data patterns and applies Bayesian networks in collecting and archiving these patterns.
An important challenge faced by intelligence analysts and the intelligence community in our post 9/11 world is to gather, piece together, and correctly interpret vast amounts of intelligence data–data that may signal an impending attack or that may help limit the severity of an attack. As a Defense Science Board study of transnational threats noted, the “making of connections between otherwise meaningless bits of information is at the core of (transnational) threat analysis.”
The E3SAT tool suite allows researchers to collect, integrate, and data mine medical records, environmental exposures, and deployment locations. This provides an encompassing data view of soldier health in the military healthcare system and enables studies of environmental, epidemiological, and etiological factors driving the system.
In the aftermath of Desert Shield and Desert Storm, researchers have struggled with explaining the array of serious health impairing symptoms that have become collectively known as Gulf War Syndrome. Approximately 30 percent of the 700,000 U.S. servicemen and servicewomen in the first Persian Gulf War have registered in the Gulf War Illness database complaining of these symptoms. A key stumbling block in researching Gulf War Syndrome is the absence of a means for integrating relevant data—soldier medical records, environmental health and surveillance data, deployment data—and discovering and analyzing to discover patterns and correlations between deployment exposures and soldier signs, symptoms, and potential causes.
The Disaster Case Management Platform (DCP) supports the life cycle of disaster case management, providing advanced, customizable reporting and analytics for resource optimization, templates and statistical parameter estimation for different disaster scenarios.
The sought after level of disaster case management includes an IT platform that serves multiple functions, including case management and resource database capabilities. The IT system should automate and support all case management functions, including intake, screening, assessment, plan development, referrals, recovery plan goal tracking, and closeout. It should also support program administration, monitoring, and reporting by providing compliance and performance monitoring reports to ACF, contractor management, and case management supervisors. Recent disaster relief operations have highlighted the need for increased federal involvement in relief operations planning and execution. Specifically, the Administration for Children and Families (ACF), Office of Human Services Emergency Preparedness and Response (OHSEPR) has been charged with providing leadership in human services emergency preparedness and response. A particular mandate for the ACF, despite recent technical and financial commitments from the federal government, is for further improvements in disaster relief case management.
Effective emergency response requires more than getting equipment and personnel to the scene. As important is determining what supplies are available and where they are available, as well as ensuring that required supplies are staged and deployed in a manner that meets the needs of the situation and any contingencies that might arise. Providing this kind of responsiveness requires two important capabilities: an awareness of what supplies are available, and the ability to communicate, easily and comprehensively, the status of those supplies. As any supply chain manager will tell you, inventory planning and awareness is critical to successful emergency response.
The BRAMS™ technology allows users to enter data for blood product and transfusion events via a Web-based interface, and to apply data mining and analytics in detecting blood supply problems, analyzing system behavior, evaluating alternate solutions, and optimizing the blood supply chain.
The BRAMS™ technology provides data integrity and access control mechanisms to ensure that the data entered into the system is valid, clean, and not duplicated in other data sources. In addition to extending JMAR data coverage and, consequently, the accuracy and reliability of JMAR data, the BRAMS™ technology enables users to perform data mining and knowledge based analyses of blood reserve data that will help the Armed Services Blood Program (ASBP) and JMAR transition from reactive agencies to forward-looking, proactive agencies.
The Military Health Data Mining Algorithms Library (M-HDML) applies data mining technologies and techniques to the storage and retrieval of patient data, helping doctors in the DoD’s vast medical health system (MHS) to more accurately diagnose their patients.
In our increasingly data-centric world, data mining technologies are being enlisted for a wide variety of uses: from retail sales, to video gaming, to, most recently, combating terrorism. The staggering amount of data has improved the stock of intelligent data mining systems and knowledge discovery techniques that help users extract meaningful information from enormous data sets. In the industrial arena, more and more organizations are investing in data mining techniques (software and hardware) as a means for gaining profitable business insights from their huge central transactional databases. The Gartner group estimates that the use of data mining applications will increase from less than 5% currently to 80% over the next decade.*