Public Health Intelligence and the Internet

The increasing role of Internet and the World Wide Web as a widely accessible health information source creates opportunities, along with challenges, for researchers, healthcare workers and organizations across the globe, enabling them to collect and analyze data to improving patient care, disease surveillance, and delivering online preventive or therapeutic interventions. In this chapter, after reviewing some of the applications of Internet in public health, we analyze the use of online space in clinical trials, experiments, or observations performed in clinical research, in the United States. We provide visual analytics for health data and preliminary findings from a database that systematically comprises clinical trial records. Our study focuses on clinical trial data with an “online” component in its study design. We first parse out historical trajectories of online clinical trials since its first introduction to show the use of the Internet space in health studies.

Social media and personal health monitoring devices (e.g., Fitbit) provide abundant patient-generated health-related data. These open health data, generated via patient engagement and sharing, are referred to as Social Health Records (SHR) as opposed to the EHR (Electronic Health Records) that are created and entered by clinicians. SHRs are changing the healthcare paradigm from the authoritative provider-centric model to a collaborative and patient-oriented healthcare framework. This chapter proposes an SHR Integration and Analytics Framework to leverage Social Health Records for gaining insights into population-level and individual-level healthcare practices and behaviors, as well as emotions. The framework defines a pipeline for generating knowledge from the social health data sources to the end users, including the patients themselves, public health officials, and healthcare providers. The SHR integration and analytics framework build a coherent knowledge base, linking the Social Health Records that are “spilled” in distributed online social media, with other online health information sources, such as results from authoritative medical research. The semantic integration model of heterogeneous health data sources provides population-level health analytics and reasoning capabilities to gain intelligence on public healthcare issues and practices. The SHR is shown to be a valuable resource for epidemic surveillance systems with real-time monitoring. We focus on an approach to quantifying the SHR-based public emotions for measuring health concern levels and for tracking them, and propose SHR-based predictive models to infer individual-level and population-level comorbidity predictions and comorbidity progression trajectories.

The estimation of disease prevalence based on public health surveillance data requires the accurate identification of cases from limited information (e.g., diagnostic codes). These data sources typically consist of routinely collected records of population healthcare utilization, such as administrative and clinical data, that specifies diagnostic codes or terms for each encounter. These data sources include, for example, emergency department visits, pharmaceutical (drug) dispensations, and laboratory test orders. The case definitions depend on the data source and are typically based on the presence of diagnostic codes or key words in a prespecified time frame. Each data source will result in a certain degree of misclassification bias when estimating prevalence. Inaccuracies can occur at each stage from the time the disease process is initiated to the stage at which diagnostic codes are entered into the database. Indeed, when relying on these data sources, asymptomatic cases will be missed, as well as those not seeking health care. Even patients that seek care may be inaccurately diagnosed or the diagnostic code that is entered in the system may not represent the diagnosis or may not be a code or key word used in the definition. In addition to misclassification bias, these data sources are not usually available in a timely manner. Timeliness is an important factor for prevalence estimation in certain contexts such as the prevalence of infectious diseases during an epidemic. For instance, in an influenza pandemic, such estimates must be obtained within days. In recent years, several nonclinical and nontraditional data sources have been introduced to public health surveillance with the potential to provide more timely signals of changing prevalence trends. Ideally, combining the new and traditional data sources, there is greater potential to overcome bias and provide more timely signals. However, building a construct capable of incorporating data from these various sources in a coherent manner is not trivial. In this research, we consider the case of the 2009–2010 H1N1 pandemic as the context of interest and we use media reports of deaths from H1N1 on the web as a nontraditional data source. We propose to use dynamic Bayesian networks from the class of probabilistic graphical models in order to combine this new data source with traditional ones through exploration of the possible probabilistic relationships between these data streams. This is an initial step toward building a framework that can potentially support aggregation of heterogeneous data for a real-time estimation of disease prevalence. Our preliminary results show that the proposed model can be used in accurate prediction of short-term future counts of the data sources. This is particularly useful in timely prediction of epidemic changes over a defined population.

There are an increasing number of health-related communities and forums developed on the Internet, where people discuss certain health issues and exchange social support with each other. However, due to the huge amount and loose structure of user-generated content in the health communities, it is difficult for users to find relevant topics or peers to discuss with. In this paper, we focus on an online smoking cessation forum, QuitStop. We extract user discussion content from the forum, apply machine learning technology to classify posts in the forum, and develop recommendation techniques to help users find valuable topics. Using text and health feature sets, the classifiers are developed and optimized to categorize posts in terms of user intentions and social support types. The recommender systems are then developed to make a recommendation of posts to users, in which the classification results are incorporated in the neighbor-based collaborative filtering approach. It is found that the combination of text and health feature sets can achieve satisfactory classification result. Integrating classification result could help relieving cold start problem in the recommendation. It can greatly improve the recall of recommendation when limited knowledge is known for a thread.

Social media hashtags are useful in many applications, such as tweet classification, clustering, searching, indexing, and social network analysis. In this chapter, we present a Big Data mining technology on social media, and demonstrate how to use it to address the following three problems: discovering relevant hashtags for health concepts, discovering the meaning of health-related hashtags, and identifying hashtags relevant to each other in the health domain. The proposed approach is based on the distributed word representations, which are learned, by applying the state-of-the-art deep learning technology, from billions of tweet words without supervision. The experiment shows that this approach outperformed the baseline approach. To the best of our knowledge, this is the first study of applying distributed language representations to discovering relationships between health concepts and hashtags.

Social media can provide a resource for characterizing communities and targeted populations through activities and content shared online. For instance, studying the armed forces’ use of social media may provide insights into their health and well-being. In this paper, we address three broad research questions: (1) How do military populations use social media? (2) What topics do military users discuss in social media? (3) Do military users talk about health and well-being differently than civilians? Military Twitter users were identified through keywords in the profile description of users who posted geo-tagged tweets at military installations. These military tweets were compared with the tweets from remaining population. Our analysis indicates that military users talk more about military related responsibilities and events, whereas nonmilitary users talk more about school, work, and leisure activities. A significant difference in online content generated by both populations was identified, involving sentiment, health, language, and social media features.

Marijuana has been legalized for medical and recreational use in many states across the U.S. Despite some medical benefits, over the past decade, researchers around the globe have documented the health risks associated with marijuana use in both youths and adults. Monitoring and understanding the related concerns and activities of marijuana use play key roles in preparing and making appropriate policies for public health regulations. However, accurately and efficiently obtaining such information is very challenging due to the unique characteristics of the relevant users, where related activities are usually hidden or undercovered. In this book chapter, we discuss new approaches to reveal the related information of marijuana use in the community by exploiting information exchanged or posted in social networks. We show that data mining approaches can be used to shed some light on the hidden patterns and related activities of marijuana use from information collected in social networks (e.g., Craigslist and Twitter). Our approaches can be utilized as a new way for public health regulators to efficiently monitor and surveil-related activities of marijuana use.

Often times terms such as Big Data, increasing digital footprints in the Internet accompanied with advancing analytical techniques, represent a major opportunity to improve public health surveillance and delivery of interventions. However, early adaption of Big Data in other fields revealed ethical challenges that could undermine privacy and autonomy of individuals and cause stigmatization. This chapter aims to identify the benefits and risks associated with the public health application of Big Data through ethical lenses. In doing so, it highlights the need for ethical discussion and framework towards an effective utilization of technologies. We then discuss key strategies to mitigate potentially harmful aspects of Big Data to facilitate its safe and effective implementation.