Developing Predictive Molecular Maps of Human Disease through Community-based Modeling

The failure of biology to identify the molecular causes of disease has led to disappointment in the rate of development of new medicines. By combining the power of community-based modeling with broad access to large datasets on a platform that promotes reproducible analyses we can work towards more predictive molecular maps that can deliver better therapeutics

Imaging Informatics and the Human Brain Project: the Role of Structure, Review

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National Center for Biomedical Ontology: Advancing biomedicine through structured organization of scientific knowledge

The National Center for Biomedical Ontology is a consortium that comprises leading informaticians, biologists, clinicians, and ontologists, funded by the National Institutes of Health (NIH) Roadmap, to develop innovative technology and methods that allow scientists to record, manage, and disseminate biomedical information and knowledge in machine-processable form. The goals of the Center are (1) to help unify the divergent and isolated efforts in ontology development by promoting high quality open-source, standards-based tools to create, manage, and use ontologies, (2) to create new software tools so that scientists can use ontologies to annotate and analyze biomedical data, (3) to provide a national resource for the ongoing evaluation, integration, and evolution of biomedical ontologies and associated tools and theories in the context of driving biomedical projects (DBPs), and (4) to disseminate the tools and resources of the Center and to identify, evaluate, and communicate best practices of ontology development to the biomedical community. Through the research activities within the Center, collaborations with the DBPs, and interactions with the biomedical community, our goal is to help scientists to work more effectively in the e-science paradigm, enhancing experiment design, experiment execution, data analysis, information synthesis, hypothesis generation and testing, and understand human disease

Biomedical Informatics Applications for Precision Management of Neurodegenerative Diseases

Modern medicine is in the midst of a revolution driven by “big data,” rapidly advancing computing power, and broader integration of technology into healthcare. Highly detailed and individualized profiles of both health and disease states are now possible, including biomarkers, genomic profiles, cognitive and behavioral phenotypes, high-frequency assessments, and medical imaging. Although these data are incredibly complex, they can potentially be used to understand multi-determinant causal relationships, elucidate modifiable factors, and ultimately customize treatments based on individual parameters. Especially for neurodegenerative diseases, where an effective therapeutic agent has yet to be discovered, there remains a critical need for an interdisciplinary perspective on data and information management due to the number of unanswered questions. Biomedical informatics is a multidisciplinary field that falls at the intersection of information technology, computer and data science, engineering, and healthcare that will be instrumental for uncovering novel insights into neurodegenerative disease research, including both causal relationships and therapeutic targets and maximizing the utility of both clinical and research data. The present study aims to provide a brief overview of biomedical informatics and how clinical data applications such as clinical decision support tools can be developed to derive new knowledge from the wealth of available data to advance clinical care and scientific research of neurodegenerative diseases in the era of precision medicine

Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

Nanoinformatics: developing new computing applications for nanomedicine

Nanoinformatics has recently emerged to address the need of computing applications at the nano level. In this regard, the authors have participated in various initiatives to identify its concepts, foundations and challenges. While nanomaterials open up the possibility for developing new devices in many industrial and scientific areas, they also offer breakthrough perspectives for the prevention, diagnosis and treatment of diseases. In this paper, we analyze the different aspects of nanoinformatics and suggest five research topics to help catalyze new research and development in the area, particularly focused on nanomedicine. We also encompass the use of informatics to further the biological and clinical applications of basic research in nanoscience and nanotechnology, and the related concept of an extended ?nanotype? to coalesce information related to nanoparticles. We suggest how nanoinformatics could accelerate developments in nanomedicine, similarly to what happened with the Human Genome and other -omics projects, on issues like exchanging modeling and simulation methods and tools, linking toxicity information to clinical and personal databases or developing new approaches for scientific ontologies, among many others

Alcuni abstract di articoli che trattano argomenti relativi all'eHealth

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Occupational therapy for children with attention deficit hyperactivity disorder (ADHD), Part 1: A delineation model of practice

In the United Kingdom (UK), occupational therapy for children with attention deficit hyperactivity disorder (ADHD) is a small field of practice (Chu 2003a), even though 5% of school-aged children in the population are affected by the condition (American Psychiatric Association [APA] 1994). Occupational therapists have much to offer children with ADHD in facilitating engagement in meaningful tasks and successful participation in different occupations, but lack holistic models of evaluation and intervention. In part 1 of a two-part article, an occupational therapy delineation model of practice is presented, in order to provide guidelines for understanding the specific psychopathology and management of this disorder from a multidimensional perspective. The model is based on an extensive literature review, the first author’s clinical experience and the data gathered from occupational therapists about their priorities for assessment and treatment (Chu 2005). Many strategies are suggested for assessing, understanding and addressing the needs of children with ADHD. The application of this model is discussed by describing specific occupational therapy evaluation and intervention procedures that are suitable for a family-centred assessment and treatment package. Some validation for this model is achieved through a multicentre evaluation, which will be reported in part 2 of this article

Integrating Research and Quality Improvement Using TeamSTEPPS: A Health Team Communication Project to Improve Hospital Discharge

Purpose/Objectives: The purpose of this article is to describe an innovative approach to the integration of quality improvement and research processes. A project with the objective of improving health team communication about hospital discharge provides an exemplar case. Description of the Project/Program: The TeamSTEPPS 10-step action planning guide provided the structure for planning, developing, and evaluating a redesign of interprofessional health team communication to improve hospital discharge led by 2 clinical nurse specialists. The redesign involved development of processes for team bedside rounding, registered nurse bedside shift reports, and briefing tools to support the rounding processes. Outcome: Using the TeamSTEPPS process, a 4-phase combined quality improvement and research project was designed and implemented. Implementation is ongoing, supported by process evaluation for continuing process improvement. Longitudinal analysis of research outcomes will follow in the future. Conclusions: Led by unit-based clinical nurse specialists, use of an integrated process of quality improvement and research creates evidence-based innovation to solve interprofessional practice problems. Incorporating research within the project design allows for data-based decisions to inform the clinical process improvement, as well as documentation of both the processes and outcomes of the local improvements that can inform replications in other sites