Where can teens find health information? A survey of web portals designed for teen health information seekers

The Web is an important source for health information for most teens with access to the Web (Gray et al, 2005a; Kaiser, 2001). While teens are likely to turn to the Web for health information, research has indicated that their skills in locating, evaluating and using health information are weak (Hansen et al, 2003; Skinner et al, 2003, Gray et al, 2005b). This behaviour suggests that the targeted approach to finding health information that is offered by web portals would be useful to teens. A web portal is the entry point for information on the Web. It is the front end, and often the filter, that users must pass through in order to link to actual content. Unlike general search engines such as Google, content that is linked to a portal has usually been pre-selected and even created by the organization that hosts the portal, assuring some level of quality control. The underlying architecture of the portal is structured and thus offers an organized approach to exploring a specific health topic. This paper reports on an environmental scan of the Web, the purpose of which was to identify and describe portals to general health information, in English and French, designed specifically for teens. It answers two key questions. First of all, what portals exist? And secondly, what are their characteristics? The portals were analyzed through the lens of four attributes: Usability, interactivity, reliability and findability. Usability is a term that incorporates concepts of navigation, layout and design, clarity of concept and purpose, underlying architecture, in-site assistance and, for web content with text, readability. Interactivity relates to the type of interactions and level of engagement required by the user to access health information on a portal. Interaction can come in the form of a game, a quiz, a creative experience, or a communication tool such as an instant messaging board, a forum or blog. Reliability reflects the traditional values of accuracy, currency, credibility and bias, and in the web-based world, durabililty. Findability is simply the ease with which a portal can be discovered by a searcher using the search engine that is most commonly associated with the Web by young people - Google - and using terms related to teen health. Findability is an important consideration since the majority of teens begin their search for health information using search engines (CIBER, 2008; Hansen et al, 2003). The content linked to by the portals was not evaluated, nor was the portals’ efficacy as a health intervention. Teens looking for health information on the Web in English have a wide range of choices available but French-language portals are much rarer and harder to find. A majority of the portals found and reviewed originated from hospitals, associations specializing in a particular disease, and governmental agencies, suggesting that portals for teens on health related topics are generally reliable. However, only a handful of the portals reviewed were easy to find, suggesting that valuable resources for teens remain buried in the Web

Social analytics for health integration, intelligence, and monitoring

Nowadays, patient-generated social health data are abundant and Healthcare is changing from the authoritative provider-centric model to collaborative and patient-oriented care. The aim of this dissertation is to provide a Social Health Analytics framework to utilize social data to solve the interdisciplinary research challenges of Big Data Science and Health Informatics. Specific research issues and objectives are described below. The first objective is semantic integration of heterogeneous health data sources, which can vary from structured to unstructured and include patient-generated social data as well as authoritative data. An information seeker has to spend time selecting information from many websites and integrating it into a coherent mental model. An integrated health data model is designed to allow accommodating data features from different sources. The model utilizes semantic linked data for lightweight integration and allows a set of analytics and inferences over data sources. A prototype analytical and reasoning tool called “Social InfoButtons” that can be linked from existing EHR systems is developed to allow doctors to understand and take into consideration the behaviors, patterns or trends of patients’ healthcare practices during a patient’s care. The tool can also shed insights for public health officials to make better-informed policy decisions. The second objective is near-real time monitoring of disease outbreaks using social media. The research for epidemics detection based on search query terms entered by millions of users is limited by the fact that query terms are not easily accessible by non-affiliated researchers. Publically available Twitter data is exploited to develop the Epidemics Outbreak and Spread Detection System (EOSDS). EOSDS provides four visual analytics tools for monitoring epidemics, i.e., Instance Map, Distribution Map, Filter Map, and Sentiment Trend to investigate public health threats in space and time. The third objective is to capture, analyze and quantify public health concerns through sentiment classifications on Twitter data. For traditional public health surveillance systems, it is hard to detect and monitor health related concerns and changes in public attitudes to health-related issues, due to their expenses and significant time delays. A two-step sentiment classification model is built to measure the concern. In the first step, Personal tweets are distinguished from Non-Personal tweets. In the second step, Personal Negative tweets are further separated from Personal Non-Negative tweets. In the proposed classification, training data is labeled by an emotion-oriented, clue-based method, and three Machine Learning models are trained and tested. Measure of Concern (MOC) is computed based on the number of Personal Negative sentiment tweets. A timeline trend of the MOC is also generated to monitor public concern levels, which is important for health emergency resource allocations and policy making. The fourth objective is predicting medical condition incidence and progression trajectories by using patients’ self-reported data on PatientsLikeMe. Some medical conditions are correlated with each other to a measureable degree (“comorbidities”). A prediction model is provided to predict the comorbidities and rank future conditions by their likelihood and to predict the possible progression trajectories given an observed medical condition. The novel models for trajectory prediction of medical conditions are validated to cover the comorbidities reported in the medical literature

Knowledge Management approaches to model pathophysiological mechanisms and discover drug targets in Multiple Sclerosis

Multiple Sclerosis (MS) is one of the most prevalent neurodegenerative diseases for which a cure is not yet available. MS is a complex disease for numerous reasons; its etiology is unknown, the diagnosis is not exclusive, the disease course is unpredictable and therapeutic response varies from patient to patient. There are four established subtypes of MS, which are segregated based on different characteristics. Many environmental and genetic factors are considered to play a role in MS etiology, including viral infection, vitamin D deficiency, epigenetical changes and some genes. Despite the large body of diverse scientific knowledge, from laboratory findings to clinical trials, no integrated model which portrays the underlying mechanisms of the disease state of MS is available. Contemporary therapies only provide reduction in the severity of the disease, and there is an unmet need of efficient drugs. The present thesis provides a knowledge-based rationale to model MS disease mechanisms and identify potential drug candidates by using systems biology approaches. Systems biology is an emerging field which utilizes the computational methods to integrate datasets of various granularities and simulate the disease outcome. It provides a framework to model molecular dynamics with their precise interaction and contextual details. The proposed approaches were used to extract knowledge from literature by state of the art text mining technologies, integrate it with proprietary data using semantic platforms, and build different models (molecular interactions map, agent based models to simulate disease outcome, and MS disease progression model with respect to time). For better information representation, disease ontology was also developed and a methodology of automatic enrichment was derived. The models provide an insight into the disease, and several pathways were explored by combining the therapeutics and the disease-specific prescriptions. The approaches and models developed in this work resulted in the identification of novel drug candidates that are backed up by existing experimental and clinical knowledge

FAIR, ethical, and coordinated data sharing for COVID-19 response: a scoping review and cross-sectional survey of COVID-19 data sharing platforms and registries

Data sharing is central to the rapid translation of research into advances in clinical medicine and public health practice. In the context of COVID-19, there has been a rush to share data marked by an explosion of population-specific and discipline-specific resources for collecting, curating, and disseminating participant-level data. We conducted a scoping review and cross-sectional survey to identify and describe COVID-19-related platforms and registries that harmonise and share participant-level clinical, omics (eg, genomic and metabolomic data), imaging data, and metadata. We assess how these initiatives map to the best practices for the ethical and equitable management of data and the findable, accessible, interoperable, and reusable (FAIR) principles for data resources. We review gaps and redundancies in COVID-19 data-sharing efforts and provide recommendations to build on existing synergies that align with frameworks for effective and equitable data reuse. We identified 44 COVID-19-related registries and 20 platforms from the scoping review. Data-sharing resources were concentrated in high-income countries and siloed by comorbidity, body system, and data type. Resources for harmonising and sharing clinical data were less likely to implement FAIR principles than those sharing omics or imaging data. Our findings are that more data sharing does not equate to better data sharing, and the semantic and technical interoperability of platforms and registries harmonising and sharing COVID-19-related participant-level data needs to improve to facilitate the global collaboration required to address the COVID-19 crisis

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Review of Semantic Importance and Role of using Ontologies in Web Information Retrieval Techniques

The Web contains an enormous amount of information, which is managed to accumulate, researched, and regularly used by many users. The nature of the Web is multilingual and growing very fast with its diverse nature of data including unstructured or semi-structured data such as Websites, texts, journals, and files. Obtaining critical relevant data from such vast data with its diverse nature has been a monotonous and challenging task. Simple key phrase data gathering systems rely heavily on statistics, resulting in a word incompatibility problem related to a specific word's inescapable semantic and situation variants. As a result, there is an urgent need to arrange such colossal data systematically to find out the relevant information that can be quickly analyzed and fulfill the users' needs in the relevant context. Over the years ontologies are widely used in the semantic Web to contain unorganized information systematic and structured manner. Still, they have also significantly enhanced the efficiency of various information recovery approaches. Ontological information gathering systems recover files focused on the semantic relation of the search request and the searchable information. This paper examines contemporary ontology-based information extraction techniques for texts, interactive media, and multilingual data types. Moreover, the study tried to compare and classify the most significant developments utilized in the search and retrieval techniques and their major disadvantages and benefits

Big Data for personalized healthcare

Big Data, often defined according to the 5V model (volume, velocity, variety, veracity and value), is seen as the key towards personalized healthcare. However, it also confronts us with new technological and ethical challenges that require more sophisticated data management tools and data analysis techniques. This vision paper aims to better understand the technological and ethical challenges we face when using and managing Big Data in healthcare as well as the way in which it impacts our way of working, our health, and our wellbeing. A mixed-methods approach (including a focus group, interviews, and an analysis of social media) was used to gain a broader picture about the pros and cons of using Big Data for personalized healthcare from three different perspectives: Big Data experts, healthcare workers, and the online public. All groups acknowledge the positive aspects of applying Big Data in healthcare, touching upon a wide array of issues, both scientifically and socially. By sharing health data, value can be created that goes beyond the individual patient. The Big Data revolution in healthcare is seen as a promising and innovative development. Yet potential facilitators and barriers need to be faced first to reach its full potential. Concerns were raised about privacy, trust, reliability, safety, purpose limitation, liability, profiling, data ownership, and loss of autonomy. Also, the importance of adding the people-centered view to the rather data-centered 5V model is stressed, in order to get a grip on the opportunities for using Big Data in personalized healthcare. People should be aware that the development of Big Data advancements is not self-evident

Barriers to Healthcare Access for Refugees and Immigrants with Limited English Proficiency in Nebraska: A Systematic Review of the Literature

Refugees and immigrants in the United States from a multitude of backgrounds are impacted by limited English proficiency (LEP), resulting in a health disparity. LEP in vulnerable populations can create obstacles in healthcare. In healthcare settings LEP patients experience adverse health events such as longer hospital stays, negative drug reaction, and patient dissatisfaction (Berdahl & Kirby, 2019). The following project aims to identify barriers for refugees and immigrants with limited English proficiency (LEP) in primary healthcare settings in Nebraska. The project utilized a systematic literature review using peer-reviewed and gray literature. The research was conducted utilizing databases: PsycINFO, PubMed, Google Scholar, EMBASE, and CINAHL. Key terms for the search criteria included “LEP in Nebraska\u27\u27 and “Healthcare”, “Language disparities in the United States\u27\u27, and “LEP” and “Health disparities\u27\u27. Literature outside the United States was excluded to maintain area-specific relevance. Literature with measurable demographic information (English proficiency level, county/city, native language) between 2015-2022 was included. The barriers include healthcare access, and quality, alongside health education. The study expands the understanding of the topic by providing public awareness to generate research further. By providing recommendations, the study hopes to incorporate implementable solutions to further combat the matter. The literature included four articles focusing on LEP barriers in Nebraska, two others on the Midwest region, and the final two identifying barriers in other US neighboring states. The project provided different barriers found in Nebraska LEP refugees and immigrants including lack of accommodation, health system complexity, and discrimination/racism. The project includes implications for developing primary healthcare professionals with the necessary training in assisting LEP patients, and research implications to broaden knowledge on the subject. Due to the increased rates of immigration and resettlement in Nebraska, it is crucial to account for LEP in healthcare, so that the barriers are fully addressed