Semantic medical care in smart cities

Medical care is a vitally important part of successful smart cities further development. High quality medical treatment has always been a challenging task for administrative departments of cities government. The key reason is that the treatment of patients significantly depends on the skills of medical stuff that can hardly be controlled and estimated. Semantic technologies by now have showed capabilities to solve highly complicated badly formalized problems in conditions of uncertainty. It makes reasonable to apply them in medical domain. In the paper a real example of information system for semantic medical care is presented. The system is being developed for Federal Almazov North-West Medical Research Centre in St-Petersburg, Russia (http://www.almazovcentre.ru/?lang=en). The main attention is paid to the proposed solution for the problem of medical treatment estimation in administrative and managerial departments. We focus on medical treatment examinations matching, trend analysis and administrative analytical and prediction task solving making use of semantic technologies, statistical analysis and deep learning applied to huge amounts of diverse data. Semantic medical data analysis project is an attempt to proceed to semantic medicine - an interoperable approach to medical domain area

A FRAMEWORK FOR BIOPROFILE ANALYSIS OVER GRID

An important trend in modern medicine is towards individualisation of healthcare to tailor care to the needs of the individual. This makes it possible, for example, to personalise diagnosis and treatment to improve outcome. However, the benefits of this can only be fully realised if healthcare and ICT resources are exploited (e.g. to provide access to relevant data, analysis algorithms, knowledge and expertise). Potentially, grid can play an important role in this by allowing sharing of resources and expertise to improve the quality of care. The integration of grid and the new concept of bioprofile represents a new topic in the healthgrid for individualisation of healthcare. A bioprofile represents a personal dynamic "fingerprint" that fuses together a person's current and past bio-history, biopatterns and prognosis. It combines not just data, but also analysis and predictions of future or likely susceptibility to disease, such as brain diseases and cancer. The creation and use of bioprofile require the support of a number of healthcare and ICT technologies and techniques, such as medical imaging and electrophysiology and related facilities, analysis tools, data storage and computation clusters. The need to share clinical data, storage and computation resources between different bioprofile centres creates not only local problems, but also global problems. Existing ICT technologies are inappropriate for bioprofiling because of the difficulties in the use and management of heterogeneous IT resources at different bioprofile centres. Grid as an emerging resource sharing concept fulfils the needs of bioprofile in several aspects, including discovery, access, monitoring and allocation of distributed bioprofile databases, computation resoiuces, bioprofile knowledge bases, etc. However, the challenge of how to integrate the grid and bioprofile technologies together in order to offer an advanced distributed bioprofile environment to support individualized healthcare remains. The aim of this project is to develop a framework for one of the key meta-level bioprofile applications: bioprofile analysis over grid to support individualised healthcare. Bioprofile analysis is a critical part of bioprofiling (i.e. the creation, use and update of bioprofiles). Analysis makes it possible, for example, to extract markers from data for diagnosis and to assess individual's health status. The framework provides a basis for a "grid-based" solution to the challenge of "distributed bioprofile analysis" in bioprofiling. The main contributions of the thesis are fourfold: A. An architecture for bioprofile analysis over grid. The design of a suitable aichitecture is fundamental to the development of any ICT systems. The architecture creates a meaiis for categorisation, determination and organisation of core grid components to support the development and use of grid for bioprofile analysis; B. A service model for bioprofile analysis over grid. The service model proposes a service design principle, a service architecture for bioprofile analysis over grid, and a distributed EEG analysis service model. The service design principle addresses the main service design considerations behind the service model, in the aspects of usability, flexibility, extensibility, reusability, etc. The service architecture identifies the main categories of services and outlines an approach in organising services to realise certain functionalities required by distributed bioprofile analysis applications. The EEG analysis service model demonstrates the utilisation and development of services to enable bioprofile analysis over grid; C. Two grid test-beds and a practical implementation of EEG analysis over grid. The two grid test-beds: the BIOPATTERN grid and PlymGRID are built based on existing grid middleware tools. They provide essential experimental platforms for research in bioprofiling over grid. The work here demonstrates how resources, grid middleware and services can be utilised, organised and implemented to support distributed EEG analysis for early detection of dementia. The distributed Electroencephalography (EEG) analysis environment can be used to support a variety of research activities in EEG analysis; D. A scheme for organising multiple (heterogeneous) descriptions of individual grid entities for knowledge representation of grid. The scheme solves the compatibility and adaptability problems in managing heterogeneous descriptions (i.e. descriptions using different languages and schemas/ontologies) for collaborated representation of a grid environment in different scales. It underpins the concept of bioprofile analysis over grid in the aspect of knowledge-based global coordination between components of bioprofile analysis over grid

The Office of Combination Products: Its Roots, Its Creation, and Its Role

The Office of Combination Products (OCP), within the Food and Drug Administration (FDA), was created formally by statute in December, 2002. Upon its creation, the Office became broadly responsible for overseeing the regulation of combination products, or products that involve components that would normally be regulated under different FDA Centers. But the statutory creation of this Office in one day represented the culmination of nearly sixty-five years of both technological advancement in the products submitted to the FDA and amassed experience within the FDA and its administrative predecessors for managing those products in the name of the public safety. To understand the need for this Office and its primary goals of timely, effective and clear regulation of these groundbreaking technologies, the history of the FDA with respect to the advent of these combination products must be examined chronologically. Drafts of statutes, administrative proposals offered for public comment, and lengthy debates from Capitol Hill all illustrate the journey that led inevitably to the OCP as a final destination. Nearly twenty-four months have passed since the OCP was created, so an analysis of its initial progress through an explanation of the changes it has effected is necessary. Both combination product manufacturers and FDA jurisdiction officers have benefited from industry guidances, enhanced inter-Center communication, and a wealth of expertise provided by FDA employees eager to streamline the regulatory pathways to be navigated. Thus, the next three years within the OCP would seem to promise similar success for product manufacturers, the FDA and the American public

Long-term neural and physiological phenotyping of a single human

Psychiatric disorders are characterized by major fluctuations in psychological function over the course of weeks and months, but the dynamic characteristics of brain function over this timescale in healthy individuals are unknown. Here, as a proof of concept to address this question, we present the MyConnectome project. An intensive phenome-wide assessment of a single human was performed over a period of 18 months, including functional and structural brain connectivity using magnetic resonance imaging, psychological function and physical health, gene expression and metabolomics. A reproducible analysis workflow is provided, along with open access to the data and an online browser for results. We demonstrate dynamic changes in brain connectivity over the timescales of days to months, and relations between brain connectivity, gene expression and metabolites. This resource can serve as a testbed to study the joint dynamics of human brain and metabolic function over time, an approach that is critical for the development of precision medicine strategies for brain disorders

33rd Aerospace Mechanisms Symposium

The proceedings of the 33rd Aerospace Mechanisms Symposium are reported. JPL hosted the conference, which was held at the Pasadena Conference and Exhibition Center, Pasadena, California, on May 19-21, 1999. Lockheed Martin Missiles and Space cosponsored the symposium. Technology areas covered include bearings and tribology; pointing, solar array and deployment mechanisms; orbiter/space station; and other mechanisms for spacecraft