Using internet search data to predict new HIV diagnoses in China: a modelling study.

ObjectivesInternet data are important sources of abundant information regarding HIV epidemics and risk factors. A number of case studies found an association between internet searches and outbreaks of infectious diseases, including HIV. In this research, we examined the feasibility of using search query data to predict the number of new HIV diagnoses in China.DesignWe identified a set of search queries that are associated with new HIV diagnoses in China. We developed statistical models (negative binomial generalised linear model and its Bayesian variants) to estimate the number of new HIV diagnoses by using data of search queries (Baidu) and official statistics (for the entire country and for Guangdong province) for 7 years (2010 to 2016).ResultsSearch query data were positively associated with the number of new HIV diagnoses in China and in Guangdong province. Experiments demonstrated that incorporating search query data could improve the prediction performance in nowcasting and forecasting tasks.ConclusionsBaidu data can be used to predict the number of new HIV diagnoses in China up to the province level. This study demonstrates the feasibility of using search query data to predict new HIV diagnoses. Results could potentially facilitate timely evidence-based decision making and complement conventional programmes for HIV prevention

A novel application of blockchain technology and its features in an effort to increase uptake of medications for Opioid Use Disorder

The opioid crisis has impacted the lives of millions of Americans. Digital technology has been applied in both research and clinical practice to mitigate this public health emergency. Blockchain technology has been implemented in healthcare and other industries outside of cryptocurrency, with few studies exploring its utility in dealing with the opioid crisis. This paper explores a novel application of blockchain technology and its features to increase uptake of medications for opioid use disorder. 

On Monetizing Personal Wearable Devices Data: A Blockchain-based Marketplace for Data Crowdsourcing and Federated Machine Learning in Healthcare

Machine learning advancements in healthcare have made data collected through smartphones and wearable devices a vital source of public health and medical insights. While wearable device data helps to monitor, detect, and predict diseases and health conditions, some data owners hesitate to share such sensitive data with companies or researchers due to privacy concerns. Moreover, wearable devices have been recently available as commercial products; thus large, diverse, and representative datasets are not available to most researchers. In this article, we propose an open marketplace where wearable device users securely monetize their wearable device records by sharing data with consumers (e.g., researchers) to make wearable device data more available to healthcare researchers. To secure the data transactions in a privacy-preserving manner, we use a decentralized approach using Blockchain and Non-Fungible Tokens (NFTs). To ensure data originality and integrity with secure validation, our marketplace uses Trusted Execution Environments (TEE) in wearable devices to verify the correctness of health data. The marketplace also allows researchers to train models using Federated Learning with a TEE-backed secure aggregation of data users may not be willing to share. To ensure user participation, we model incentive mechanisms for the Federated Learning-based and anonymized data-sharing approaches using NFTs. We also propose using payment channels and batching to reduce smart contact gas fees and optimize user profits. If widely adopted, we believe that TEE and Blockchain-based incentives will promote the ethical use of machine learning with validated wearable device data in healthcare and improve user participation due to incentives.

Internet searches for opioids predict future emergency department heroin admissions.

BackgroundFor a number of fiscal and practical reasons, data on heroin use have been of poor quality, which has hampered the ability to halt the growing epidemic. Internet search data, such as those made available by Google Trends, have been used as a low-cost, real-time data source for monitoring and predicting a variety of public health outcomes. We aimed to determine whether data on opioid-related internet searches might predict future heroin-related admissions to emergency departments (ED).MethodsAcross nine metropolitan statistical areas (MSAs) in the United States, we obtained data on Google searches for prescription and non-prescription opioids, as well as Substance Abuse and Mental Health Services Administration (SAMHSA) data on heroin-related ED visits from 2004 to 2011. A linear mixed model assessed the relationship between opioid-related Internet searches and following year heroin-related visits, controlling for MSA GINI index and total number of ED visits.ResultsThe best-fitting model explained 72% of the variance in heroin-related ED visits. The final model included the search keywords "Avinza," "Brown Sugar," "China White," "Codeine," "Kadian," "Methadone," and "Oxymorphone." We found regional differences in where and how people searched for opioid-related information.ConclusionsInternet search-based modeling should be explored as a new source of insights for predicting heroin-related admissions. In geographic regions where no current heroin-related data exist, Internet search modeling might be a particularly valuable and inexpensive tool for estimating changing heroin use trends. We discuss the immediate implications for using this approach to assist in managing opioid-related morbidity and mortality in the United States

Reimagining On-Campus Student Employment as a Foundation for Career Readiness: Student Employment in Virginia Commonwealth University’s Division of Student Affairs

Many undergraduate students participate in on-campus employment while pursuing their academic programs. Increasingly, institutions of higher education recognize student employment not only as a resource to address institutional workforce needs or students’ pressing financial needs, but also to promote students’ overall learning, development, and success. To better understand on-campus undergraduate student employment in the context of Virginia Commonwealth University’s (VCU) Division of Student Affairs (DSA), this descriptive study employed a mixed-methods approach using surveys, interviews, focus groups, secondary data analysis, and artifact analysis. These methods were applied to explore the perspectives of multiple stakeholders, including undergraduate student employees, supervisors of student employees, and administrative leaders. Findings underscore that decentralized employment policies and practices present access barriers for student employees and provide inadequate support for supervisors. Crucial next steps will be to develop a shared language and culture that promote on-campus student employment as a High-Impact Practice (HIP) and measure student employees’ learning outcomes in terms of Career-Readiness Competencies. Recommendations are four-fold: provide centralized oversight for on-campus employment; build a more supportive ecosystem for student employees; elevate the role of supervisors of student employees; and assess student employees’ learning outcomes. Implementing these recommendations can foster a more supportive environment for undergraduate student employees and their supervisors, ultimately promoting enhanced learning outcomes, career development, and satisfaction for both of these stakeholders. Insights about effective practices for on-campus student employment may be applicable in other higher education settings, allowing for the dissemination of best practices to a broader range of institutions

Feasibility of using Grindr™ to distribute HIV self-test kits to men who have sex with men in Los Angeles, California

Background: Our study aimed to determine if Grindr™ is an effective means of reaching high-risk men who have sex with men (MSM) for HIV testing. In Los Angeles (LA), Black and Latino MSM have the highest rate of HIV infection, and Black MSM in LA are four-fold more likely than white MSM to not know they are infected with HIV. Those MSM are also major users of social networking apps. Grindr™ was used to provide access to free HIV self-testing. Methods: Free HIV self-test kits were advertised on Grindr™ from 13 October to 11 November 2014, consisting of 300 000 banner ads and three broadcast messages targeting a high-risk HIV population in LA. Eligible participants, Black or Latino, MSM and who were aged ≥18 years of age, were invited to take a survey 2 weeks after test delivery. Results: The website received 4389 unique visitors and 333 test requests, of which 247 (74%) were requests for mailed tests, 58 (17%) were for vouchers and 28 (8%) were for vending machines. Of the 125 participants, 74% reported at least one episode of condomless anal intercourse in the past 3 months, 29% last tested for HIV over 1 year ago and 9% had never been tested. Conclusions: It was feasible to use Grindr™ to distribute HIV self-test kits. Users are willing to provide personal information in exchange for a free self-test and found self-tests acceptable and easy to use. HIV self-testing promotion through apps has a high potential to reach untested high-risk populations

Knowledge sharing between design and manufacture

The aim of this research is to develop a representation method that allows knowledge to be readily shared between collaborating systems (agents) in a design/manufacturing environment. Improved mechanisms for interpreting the terms used to describe knowledge across system boundaries are proposed and tested. The method is also capable of handling complex product designs and realistic manufacturing scenarios involving several parties. This is achieved using an agent-architecture to simulate the effects of individual manufacturing facilities (e.g. machine tools and foundries) on product features. It is hypothesised that knowledge sharing between such agents can be enhanced by integrating common product and manufacturing information models with a shared ontology, and that the shared ontology can be based largely on The Process Specification Language (PSL)

Knowledge sharing between design and manufacture

Object-oriented modelling has become an established technique for product and manufacturing knowledge representation. Various models offering generalised classes and class hierarchies have been proposed for this purpose. Additional bespoke classes are however typically required for specific domain representations. This causes problems when knowledge needs to be shared between domains using different models to describe common entities. These issues are especially complex when several systems are involved. For example, a designer accessing product, manufacturing, and third party systems may face multiple definitions of components, facilities and processes. This paper proposes a model that addresses some of these issues. The proposed model can describe manufacturing knowledge without additional bespoke classes. The detailed semantics of the model are based on recent work on ontologies, notably the Process Specification Language (PSL). Whilst PSL provides detailed semantics, it is not inherently object-oriented. The integration of PSL with object-oriented modelling methods is therefore the principle contribution of this work