Capturing personal health data from wearable sensors

Recently, there has been a significant growth in pervasive computing and ubiquitous sensing which strives to develop and deploy sensing technology all around us. We are also seeing the emergence of applications such as environmental and personal health monitoring to leverage data from a physical world. Most of the developments in this area have been concerned with either developing the sensing technologies, or the infrastructure (middleware) to gather this data and the issues which have been addressed include power consumption on the devices, security of data transmission, networking challenges in gathering and storing the data and fault tolerance in the event of network and/or device failure. Research is focusing on harvesting and managing data and providing query capabilities

Intuitive querying of e-Health data repositories

At the centre of the Clinical e-Science Framework (CLEF) project is a repository of well organised, detailed clinical histories, encoded as data that will be available for use in clinical care and in-silico medical experiments. An integral part of the CLEF workbench is a tool to allow biomedical researchers and clinicians to query – in an intuitive way – the repository of patient data. This paper describes the CLEF query editing interface, which makes use of natural language generation techniques in order to alleviate some of the problems generally faced by natural language and graphical query interfaces. The query interface also incorporates an answer renderer that dynamically generates responses in both natural language text and graphics

Summarisation and visualisation of e-Health data repositories

At the centre of the Clinical e-Science Framework (CLEF) project is a repository of well organised, detailed clinical histories, encoded as data that will be available for use in clinical care and in-silico medical experiments. We describe a system that we have developed as part of the CLEF project, to perform the task of generating a diverse range of textual and graphical summaries of a patient’s clinical history from a data-encoded model, a chronicle, representing the record of the patient’s medical history. Although the focus of our current work is on cancer patients, the approach we describe is generalisable to a wide range of medical areas

The European Institute for Innovation through Health Data

The European Institute for Innovation through Health Data (i~HD, www.i-hd.eu) has been formed as one of the key sustainable entities arising from the Electronic Health Records for Clinical Research (IMI-JU-115189) and SemanticHealthNet (FP7-288408) projects, in collaboration with several other European projects and initiatives supported by the European Commission. i~HD is a European not-for-profit body, registered in Belgium through Royal Assent. i~HD has been established to tackle areas of challenge in the successful scaling up of innovations that critically rely on high-quality and interoperable health data. It will specifically address obstacles and opportunities to using health data by collating, developing, and promoting best practices in information governance and in semantic interoperability. It will help to sustain and propagate the results of health information and communication technology (ICT) research that enables better use of health data, assessing and optimizing their novel value wherever possible. i~HD has been formed after wide consultation and engagement of many stakeholders to develop methods, solutions, and services that can help to maximize the value obtained by all stakeholders from health data. It will support innovations in health maintenance, health care delivery, and knowledge discovery while ensuring compliance with all legal prerequisites, especially regarding the insurance of patient's privacy protection. It is bringing multiple stakeholder groups together so as to ensure that future solutions serve their collective needs and can be readily adopted affordably and at scale

Health Data and Privacy in the Digital Era

In 2010, the social networking site Facebook launched a platform allowing private companies to request users’ permission to access personal data. Few users were aware of the platform, which was integrated into Facebook’s terms of service. In 2014, Cambridge Analytica, a UK-based political consulting firm, developed a data-harvesting app. That app prompted Facebook users to provide psychological profiles, including responses such as “I get upset easily” and “I have frequent mood-swings” as part of a “research project.” The Facebook platform allowed users to share their friends’ data as well, enabling Cambridge Analytica to access tens of millions of personal profiles, identifying voters’ political preferences. The controversy revealed risks to identifiable health data posed by social media and web services companies’ practices. After the Cambridge Analytica controversy, Facebook suspended a project that aimed to link data about users’ medical conditions with information about their social networks. Individuals often reveal detailed, sensitive health information online. Through wearable devices, social media posts, traceable web searches, and online patient communities, users generate large volumes of health data. Although some individuals participate in online patient forums and wellness information sharing apps under their own names, others participate via pseudonyms, assuming their privacy is preserved. Many users believe their data will be shared only with those they designate

Inequality Measurement forOrdered Response Health Data

When health status is an ordered response variable, Allison and Foster (2004)postulate that a distribution Q ?exhibits more inequality than a distribution P ?if Q ?isobtained from P ?via a sequence of median preserving spreads. This paper introduces aparametric family of inequality indices which are founded on the Allison and Fosterordering.Self-reported health status, inequality orderings, inequalitymeasures.

Informed Alaskans Initiative: Public Health Data in Alaska

This article describes the national and state public health data made available online through the Alaska Division of Public Health's Informed Alaskans Initiative.[Introduction] / AK-IBIS / Health Indicators / Indicator Reports / Interactive Health Maps / Help for Website Users / What’s Next / Conclusion / [SIDEBAR:] Public Health Data Resource

Tennessee Population 2020

https://digitalcommons.memphis.edu/govpubs-tn-dept-health-general-health-data-population/1003/thumbnail.jp