An Advanced Home ElderCare Service

With the increase of welfare cost all over the developed world, there is a need to resort to new technologies that could help reduce this enormous cost and provide some quality eldercare services. This paper presents a middleware-level solution that integrates monitoring and emergency detection solutions with networking solutions. The proposed system enables efficient integration between a variety of sensors and actuators deployed at home for emergency detection and provides a framework for creating and managing rescue teams willing to assist elders in case of emergency situations. A prototype of the proposed system was designed and implemented. Results were obtained from both computer simulations and a real-network testbed. These results show that the proposed system can help overcome some of the current problems and help reduce the enormous cost of eldercare service

Ready or Not? Protecting the Public's Health From Diseases, Disasters, and Bioterrorism, 2009

Based on ten indicators, assesses progress in the readiness of states, federal government, and hospitals to respond to public health emergencies, with a focus on the H1N1 flu. Outlines improvements and concerns in funding, accountability, and other areas

Cancer Surveillance using Data Warehousing, Data Mining, and Decision Support Systems

This article discusses how data warehousing, data mining, and decision support systems can reduce the national cancer burden or the oral complications of cancer therapies, especially as related to oral and pharyngeal cancers. An information system is presented that will deliver the necessary information technology to clinical, administrative, and policy researchers and analysts in an effective and efficient manner. The system will deliver the technology and knowledge that users need to readily: (1) organize relevant claims data, (2) detect cancer patterns in general and special populations, (3) formulate models that explain the patterns, and (4) evaluate the efficacy of specified treatments and interventions with the formulations. Such a system can be developed through a proven adaptive design strategy, and the implemented system can be tested on State of Maryland Medicaid data (which includes women, minorities, and children)

Human behavioural analysis with self-organizing map for ambient assisted living

This paper presents a system for automatically classifying the resting location of a moving object in an indoor environment. The system uses an unsupervised neural network (Self Organising Feature Map) fully implemented on a low-cost, low-power automated home-based surveillance system, capable of monitoring activity level of elders living alone independently. The proposed system runs on an embedded platform with a specialised ceiling-mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels and to detect specific events such as potential falls. First order motion information, including first order moving average smoothing, is generated from the 2D image coordinates (trajectories). A novel edge-based object detection algorithm capable of running at a reasonable speed on the embedded platform has been developed. The classification is dynamic and achieved in real-time. The dynamic classifier is achieved using a SOFM and a probabilistic model. Experimental results show less than 20% classification error, showing the robustness of our approach over others in literature with minimal power consumption. The head location of the subject is also estimated by a novel approach capable of running on any resource limited platform with power constraints

Screening strategies in surveillance and control of methicillin-resistant Staphylococcus aureus (MRSA)

With reports of hospital-acquired methicillin-resistant Staphylococcus aureus (MRSA) continuing to increase and therapeutic options decrease, infection control methods are of increasing importance. Here we investigate the relationship between surveillance and infection control. Surveillance plays two roles with respect to control: it allows detection of infected/colonized individuals necessary for their removal from the general population, and it allows quantification of control success. We develop a stochastic model of MRSA transmission dynamics exploring the effects of two screening strategies in an epidemic setting: random and on admission. We consider both hospital and community populations and include control and surveillance in a single framework. Random screening was more efficient at hospital surveillance and allowed nosocomial control, which also prevented epidemic behaviour in the community. Therefore, random screening was the more effective control strategy for both the hospital and community populations in this setting. Surveillance strategies have significant impact on both ascertainment of infection prevalence and its control

Syndromic surveillance of influenza-like illness in Scotland during the influenza A H1N1v pandemic and beyond

Syndromic surveillance refers to the rapid monitoring of syndromic data to highlight and follow outbreaks of infectious diseases, increasing situational awareness. Such systems are based upon statistical models to described routinely collected health data. We describe a working exception reporting system (ERS) currently used in Scotland to monitor calls received to the NHS telephone helpline, NHS24. We demonstrate the utility of the system to describe the time series data from NHS24 both at an aggregated Scotland level and at the individual health board level for two case studies, firstly during the initial phase of the 2009 Influenza A H1N1v and secondly for the emergence of seasonal influenza in each winter season from 2006/07 and 2010/11. In particular, we focus on a localised cluster of infection in the Highland health board and the ability of the system to highlight this outbreak. Caveats of the system, including the effect of media reporting of the pandemic on the results and the associated statistical issues, will be discussed. We discuss the adaptability and timeliness of the system and how this continues to form part of a suite of surveillance used to give early warnings to public health decision makers

ANGELAH: A Framework for Assisting Elders At Home

The ever growing percentage of elderly people within modern societies poses welfare systems under relevant stress. In fact, partial and progressive loss of motor, sensorial, and/or cognitive skills renders elders unable to live autonomously, eventually leading to their hospitalization. This results in both relevant emotional and economic costs. Ubiquitous computing technologies can offer interesting opportunities for in-house safety and autonomy. However, existing systems partially address in-house safety requirements and typically focus on only elder monitoring and emergency detection. The paper presents ANGELAH, a middleware-level solution integrating both ”elder monitoring and emergency detection” solutions and networking solutions. ANGELAH has two main features: i) it enables efficient integration between a variety of sensors and actuators deployed at home for emergency detection and ii) provides a solid framework for creating and managing rescue teams composed of individuals willing to promptly assist elders in case of emergency situations. A prototype of ANGELAH, designed for a case study for helping elders with vision impairments, is developed and interesting results are obtained from both computer simulations and a real-network testbed

Cancer Informatics for Cancer Centers (CI4CC): Building a Community Focused on Sharing Ideas and Best Practices to Improve Cancer Care and Patient Outcomes.

Cancer Informatics for Cancer Centers (CI4CC) is a grassroots, nonprofit 501c3 organization intended to provide a focused national forum for engagement of senior cancer informatics leaders, primarily aimed at academic cancer centers anywhere in the world but with a special emphasis on the 70 National Cancer Institute-funded cancer centers. Although each of the participating cancer centers is structured differently, and leaders' titles vary, we know firsthand there are similarities in both the issues we face and the solutions we achieve. As a consortium, we have initiated a dedicated listserv, an open-initiatives program, and targeted biannual face-to-face meetings. These meetings are a place to review our priorities and initiatives, providing a forum for discussion of the strategic and pragmatic issues we, as informatics leaders, individually face at our respective institutions and cancer centers. Here we provide a brief history of the CI4CC organization and meeting highlights from the latest CI4CC meeting that took place in Napa, California from October 14-16, 2019. The focus of this meeting was "intersections between informatics, data science, and population science." We conclude with a discussion on "hot topics" on the horizon for cancer informatics

Sleeping sickness

Artyicle approval pendingHuman African trypanosomiasis (HAT), or sleeping sickness, is a vector-borne disease that flourishes in impoverished, rural parts of sub-Saharan Africa. It is caused by infection with the protozoan parasite Trypanosoma brucei and is transmitted by tsetse flies of the genus Glossina. The majority of cases are caused by T. b. gambiense, which gives rise to the chronic, anthroponotic endemic disease in Western and Central Africa. Infection with T. b. rhodesiense leads to the acute, zoonotic form of Eastern and Southern Africa. The parasites live and multiply extracellularly in the blood and tissue fluids of their human host. They have elaborated a variety of strategies for invading hosts, to escape the immune system and to take advantage of host growth factors. HAT is a challenging and deadly disease owing to its complex epidemiology and clinical presentation and, if left untreated, can result in high death rates. As one of the most neglected tropical diseases, HAT is characterized by the limited availability of safe and cost-effective control tools. No vaccine against HAT is available, and the toxicity of existing old and cumbersome drugs precludes the adoption of control strategies based on preventive chemotherapy. As a result, the keystones of interventions against sleeping sickness are active and passive case-finding for early detection of cases followed by treatment, vector control and animal reservoir management. New methods to diagnose and treat patients and to control transmission by the tsetse fly are needed to achieve the goal of global elimination of the disease