The mPower Study, Parkinson Disease Mobile Data Collected Using Researchkit

Current measures of health and disease are often insensitive, episodic, and subjective. Further, these measures generally are not designed to provide meaningful feedback to individuals. The impact of high-resolution activity data collected from mobile phones is only beginning to be explored. Here we present data from mPower, a clinical observational study about Parkinson disease conducted purely through an iPhone app interface. The study interrogated aspects of this movement disorder through surveys and frequent sensor-based recordings from participants with and without Parkinson disease. Benefitting from large enrollment and repeated measurements on many individuals, these data may help establish baseline variability of real-world activity measurement collected via mobile phones, and ultimately may lead to quantification of the ebbs-and-flows of Parkinson symptoms. App source code for these data collection modules are available through an open source license for use in studies of other conditions. We hope that releasing data contributed by engaged research participants will seed a new community of analysts working collaboratively on understanding mobile health data to advance human health

Crowdbreaks: Tracking Health Trends using Public Social Media Data and Crowdsourcing

In the past decade, tracking health trends using social media data has shown great promise, due to a powerful combination of massive adoption of social media around the world, and increasingly potent hardware and software that enables us to work with these new big data streams. At the same time, many challenging problems have been identified. First, there is often a mismatch between how rapidly online data can change, and how rapidly algorithms are updated, which means that there is limited reusability for algorithms trained on past data as their performance decreases over time. Second, much of the work is focusing on specific issues during a specific past period in time, even though public health institutions would need flexible tools to assess multiple evolving situations in real time. Third, most tools providing such capabilities are proprietary systems with little algorithmic or data transparency, and thus little buy-in from the global public health and research community. Here, we introduce Crowdbreaks, an open platform which allows tracking of health trends by making use of continuous crowdsourced labelling of public social media content. The system is built in a way which automatizes the typical workflow from data collection, filtering, labelling and training of machine learning classifiers and therefore can greatly accelerate the research process in the public health domain. This work introduces the technical aspects of the platform and explores its future use cases

Determinants of women´s health in Europe: using large open data collections to unveil the hidden part of the iceberg

Open data collections can be powerful, providing democratic tools to illustrate women’s health across Europe. This article discusses the benefits offered by the large volume of open-access data in comparison with access-restrictive big data, and provides an overview of the main databases publically available which gather sex-disaggregated data information, as well as of their strengths and limitations (The World Health Organization European Health for All database, EUROSTAT, Institute for Health Metrics and Evaluation – Global Burden of Disease data and OECD data). Examples are provided to illustrate the outcomes that can be obtained from the different databases, with special emphasis on the socioeconomic determinants of women’s health (education, income and wealth, employment and place of residence) in the European Region. Open online data collections accessible to all can be used as tools to argue in favour of not only the implementation of health-care policies, but also social and economic policies aimed at improving women’s health in Europe. However, open-access online data collections have certain drawbacks worth considering such as the need for continuous monitoring and updating, ensuring the reliability of data provided by all countries, and guaranteeing that individuals cannot be identified through links between clinical and socioeconomic data

The advantages of UK Biobank's open access strategy for health research

Ready access to health research studies is becoming more important as researchers, and their funders, seek to maximize the opportunities for scientific innovation and health improvements. Large‐scale population‐based prospective studies are particularly useful for multidisciplinary research into the causes, treatment and prevention of many different diseases. UK Biobank has been established as an open‐access resource for public health research, with the intention of making the data as widely available as possible in an equitable and transparent manner. Access to UK Biobank's unique breadth of phenotypic and genetic data has attracted researchers worldwide from across academia and industry. As a consequence, it has enabled scientists to perform world‐leading collaborative research. Moreover, open access to an already deeply characterized cohort has encouraged both public and private sector investment in further enhancements to make UK Biobank an unparalleled resource for public health research and an exemplar for the development of open‐access approaches for other studies

To identify and examine the different causes of liver disease in Sri Lanka

© 2017 The Authors. Published by International Journal of Science and Research. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://www.ijsr.net/get_abstract.php?paper_id=ART20178732Liver disease is one of the main causes for deaths in Sri Lanka, this is the second most common disease causing deaths in hospitals in Sri Lanka after the heart disease. Sri Lanka ranks eighty-nine (89) in the world rankings for liver disease causing 3349 deaths according to the data published by the World Health Organization (WHO) for the 2014 calendar year and an average of 15.28 deaths per hundred thousand. The two most common forms of this disease is non-alcoholic fatty liver disease and alcoholic fatty liver disease. The data collected by the WHO is analyzed and the different causes for the liver disease is identified between the period of 1980-2010, using the different factors responsible for the cause of the disease data is distinguished. Of the data collected and analyzed most causes of liver disease in Sri Lanka is due to the non-alcoholic fatty liver disease (NAFLD) or alcoholic fatty liver disease which leads to severe complications such as renal failure, liver cirrhosis and eventually deat

Open access to the research literature : a funders perspective

In a declaration to commemorate the publication of the first draft of the human genome, UK Prime Minister Tony Blair and US President Bill Clinton commented that, “unencumbered access to this information will promote discoveries that will reduce the burden of disease, improve health around the world and enhance the quality of life for all human kind” (quoted in BBC, 2000). One of the major funders of the human genome project was the Wellcome Trust , an independent charity that funds research to improve human and animal health. And, having been at the forefront of the decision to make the genome sequencing data freely available, it was perhaps inevitable that this funding body would lead the way in advocating free access to the research literature. If, as the Wellcome believes, it makes sense for scientists to have free access to raw, genomic data – to help realise the promise of this research – then it makes equal sense for scientists to be able to access the outputs (journal articles), to enable this research to be built on and developed. This chapter considers the issues around open access from the perspective of a research funder

GI Systems for public health with an ontology based approach

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Health is an indispensable attribute of human life. In modern age, utilizing technologies for health is one of the emergent concepts in several applied fields. Computer science, (geographic) information systems are some of the interdisciplinary fields which motivates this thesis. Inspiring idea of the study is originated from a rhetorical disease DbHd: Database Hugging Disorder, defined by Hans Rosling at World Bank Open Data speech in May 2010. The cure of this disease can be offered as linked open data, which contains ontologies for health science, diseases, genes, drugs, GEO species etc. LOD-Linked Open Data provides the systematic application of information by publishing and connecting structured data on the Web. In the context of this study we aimed to reduce boundaries between semantic web and geo web. For this reason a use case data is studied from Valencia CSISP- Research Center of Public Health in which the mortality rates for particular diseases are represented spatio-temporally. Use case data is divided into three conceptual domains (health, spatial, statistical), enhanced with semantic relations and descriptions by following Linked Data Principles. Finally in order to convey complex health-related information, we offer an infrastructure integrating geo web and semantic web. Based on the established outcome, user access methods are introduced and future researches/studies are outlined

Addressing Class Imbalance in Electronic Health Records Data Imputation

Imputing missing values in imbalanced datasets remains an open challenge. Most methods assume data are missing at random or follow a standard distribution, lacking robustness for complex real-world data. Electronic health records exhibit severe class imbalance with non-random missingness, hindering model performance. We propose M3-BRITS for greater scalability and flexibility, modeling temporal and cross-feature correlations to impute missing data, by optimizing sample similarity with deep metric learning for self-supervised learning. Evaluating imputation alone avoids reduced diversity and model bias from joint downstream tasks. Our model achieves superior performance to all baseline methods on four real-world datasets. This shows promise for increasing model scalability and flexibility to handle complex real-world data