Readability and topics of the German Health Web: Exploratory study and text analysis.

BACKGROUND: The internet has become an increasingly important resource for health information, especially for lay people. However, the information found does not necessarily comply with the user's health literacy level. Therefore, it is vital to (1) identify prominent information providers, (2) quantify the readability of written health information, and (3) to analyze how different types of information sources are suited for people with differing health literacy levels. OBJECTIVE: In previous work, we showed the use of a focused crawler to "capture" and describe a large sample of the "German Health Web", which we call the "Sampled German Health Web" (sGHW). It includes health-related web content of the three mostly German speaking countries Germany, Austria, and Switzerland, i.e. country-code top-level domains (ccTLDs) ".de", ".at" and ".ch". Based on the crawled data, we now provide a fully automated readability and vocabulary analysis of a subsample of the sGHW, an analysis of the sGHW's graph structure covering its size, its content providers and a ratio of public to private stakeholders. In addition, we apply Latent Dirichlet Allocation (LDA) to identify topics and themes within the sGHW. METHODS: Important web sites were identified by applying PageRank on the sGHW's graph representation. LDA was used to discover topics within the top-ranked web sites. Next, a computer-based readability and vocabulary analysis was performed on each health-related web page. Flesch Reading Ease (FRE) and the 4th Vienna formula (WSTF) were used to assess the readability. Vocabulary was assessed by a specifically trained Support Vector Machine classifier. RESULTS: In total, n = 14,193,743 health-related web pages were collected during the study period of 370 days. The resulting host-aggregated web graph comprises 231,733 nodes connected via 429,530 edges (network diameter = 25; average path length = 6.804; average degree = 1.854; modularity = 0.723). Among 3000 top-ranked pages (1000 per ccTLD according to PageRank), 18.50%(555/3000) belong to web sites from governmental or public institutions, 18.03% (541/3000) from nonprofit organizations, 54.03% (1621/3000) from private organizations, 4.07% (122/3000) from news agencies, 3.87% (116/3000) from pharmaceutical companies, 0.90% (27/3000) from private bloggers, and 0.60% (18/3000) are from others. LDA identified 50 topics, which we grouped into 11 themes: "Research & Science", "Illness & Injury", "The State", "Healthcare structures", "Diet & Food", "Medical Specialities", "Economy", "Food production", "Health communication", "Family" and "Other". The most prevalent themes were "Research & Science" and "Illness & Injury" accounting for 21.04% and 17.92% of all topics across all ccTLDs and provider types, respectively. Our readability analysis reveals that the majority of the collected web sites is structurally difficult or very difficult to read: 84.63% (2539/3000) scored a WSTF ≥ 12, 89.70% (2691/3000) scored a FRE ≤ 49. Moreover, our vocabulary analysis shows that 44.00% (1320/3000) web sites use vocabulary that is well suited for a lay audience. CONCLUSIONS: We were able to identify major information hubs as well as topics and themes within the sGHW. Results indicate that the readability within the sGHW is low. As a consequence, patients may face barriers, even though the vocabulary used seems appropriate from a medical perspective. In future work, the authors intend to extend their analyses to identify trustworthy health information web sites.

Crawling the German Health Web: Exploratory Study and Graph Analysis.

BACKGROUND: The internet has become an increasingly important resource for health information. However, with a growing amount of web pages, it is nearly impossible for humans to manually keep track of evolving and continuously changing content in the health domain. To better understand the nature of all web-based health information as given in a specific language, it is important to identify (1) information hubs for the health domain, (2) content providers of high prestige, and (3) important topics and trends in the health-related web. In this context, an automatic web crawling approach can provide the necessary data for a computational and statistical analysis to answer (1) to (3). OBJECTIVE: This study demonstrates the suitability of a focused crawler for the acquisition of the German Health Web (GHW) which includes all health-related web content of the three mostly German speaking countries Germany, Austria and Switzerland. Based on the gathered data, we provide a preliminary analysis of the GHW's graph structure covering its size, most important content providers and a ratio of public to private stakeholders. In addition, we provide our experiences in building and operating such a highly scalable crawler. METHODS: A support vector machine classifier was trained on a large data set acquired from various German content providers to distinguish between health-related and non-health-related web pages. The classifier was evaluated using accuracy, recall and precision on an 80/20 training/test split (TD1) and against a crowd-validated data set (TD2). To implement the crawler, we extended the open-source framework StormCrawler. The actual crawl was conducted for 227 days. The crawler was evaluated by using harvest rate and its recall was estimated using a seed-target approach. RESULTS: In total, n=22,405 seed URLs with country-code top level domains .de: 85.36% (19,126/22,405), .at: 6.83% (1530/22,405), .ch: 7.81% (1749/22,405), were collected from Curlie and a previous crawl. The text classifier achieved an accuracy on TD1 of 0.937 (TD2=0.966), a precision on TD1 of 0.934 (TD2=0.954) and a recall on TD1 of 0.944 (TD2=0.989). The crawl yields 13.5 million presumably relevant and 119.5 million nonrelevant web pages. The average harvest rate was 19.76%; recall was 0.821 (4105/5000 targets found). The resulting host-aggregated graph contains 215,372 nodes and 403,175 edges (network diameter=25; average path length=6.466; average degree=1.872; average in-degree=1.892; average out-degree=1.845; modularity=0.723). Among the 25 top-ranked pages for each country (according to PageRank), 40% (30/75) were web sites published by public institutions. 25% (19/75) were published by nonprofit organizations and 35% (26/75) by private organizations or individuals. CONCLUSIONS: The results indicate, that the presented crawler is a suitable method for acquiring a large fraction of the GHW. As desired, the computed statistical data allows for determining major information hubs and important content providers on the GHW. In the future, the acquired data may be used to assess important topics and trends but also to build health-specific search engines.

Characterization of Drosophila Nidogen/entactin reveals roles in basement membrane stability, barrier function and nervous system patterning.

Basement membranes (BMs) are specialized layers of extracellular matrix (ECM) mainly composed of Laminin, type IV Collagen, Perlecan and Nidogen/entactin (NDG). Recent in vivo studies challenged the initially proposed role of NDG as a major ECM linker molecule by revealing dispensability for viability and BM formation. Here, we report the characterization of the single Ndg gene in Drosophila. Embryonic Ndg expression was primarily observed in mesodermal tissues and the chordotonal organs, whereas NDG protein localized to all BMs. Although loss of Laminin strongly affected BM localization of NDG, Ndg-null mutants exhibited no overt changes in the distribution of BM components. Although Drosophila Ndg mutants were viable, loss of NDG led to ultrastructural BM defects that compromised barrier function and stability in vivo Moreover, loss of NDG impaired larval crawling behavior and reduced responses to vibrational stimuli. Further morphological analysis revealed accompanying defects in the larval peripheral nervous system, especially in the chordotonal organs and the neuromuscular junction (NMJ). Taken together, our analysis suggests that NDG is not essential for BM assembly but mediates BM stability and ECM-dependent neural plasticity during Drosophila development.

Solving Interoperability in Translational Health. Perspectives of Students from the International Partnership in Health Informatics Education (IPHIE) 2016 Master Class.

BACKGROUND: In the summer of 2016 an international group of biomedical and health informatics faculty and graduate students gathered for the 16th meeting of the International Partnership in Health Informatics Education (IPHIE) masterclass at the University of Utah campus in Salt Lake City, Utah. This international biomedical and health informatics workshop was created to share knowledge and explore issues in biomedical health informatics (BHI). OBJECTIVE: The goal of this paper is to summarize the discussions of biomedical and health informatics graduate students who were asked to define interoperability, and make critical observations to gather insight on how to improve biomedical education. METHODS: Students were assigned to one of four groups and asked to define interoperability and explore potential solutions to current problems of interoperability in health care. RESULTS: We summarize here the student reports on the importance and possible solutions to the "interoperability problem" in biomedical informatics. Reports are provided from each of the four groups of highly qualified graduate students from leading BHI programs in the US, Europe and Asia. CONCLUSION: International workshops such as IPHIE provide a unique opportunity for graduate student learning and knowledge sharing. BHI faculty are encouraged to incorporate into their curriculum opportunities to exercise and strengthen student critical thinking to prepare our students for solving health informatics problems in the future.

Proactive Support of Internet Browsing when Searching for Relevant Health Information.

Many people use the Internet as one of the primary sources of health information. This is due to the high volume and easy access of freely available information regarding diseases, diagnoses and treatments. However, users may find it difficult to retrieve information which is easily understandable and does not require a deep medical background. In this paper, we present a new kind of Web browser add-on, in order to proactively support users when searching for relevant health information. Our add-on not only visualizes the understandability of displayed medical text but also provides further recommendations of Web pages which hold similar content but are potentially easier to comprehend.

Expertizer: a tool to assess the expert level of online health websites.

Health-related Web sites have become a primary resource to search for information on diseases, diagnoses or treatment options. Various Web sites offer a great variety of such information. However, lay people might have difficulties to assess whether a certain article or Web site fits their individual level of understandability. Hence, they might get overwhelmed with the delivered complexity of medical information. In this paper, we present a Web browser plugin, Expertizer that supports users in order to easily assess the expert level of textual medical Web content. The plugin communicates with a Web service, which leverages pre-computed classification models based on a Support Vector Machine.

How Google Glass could support patients with diabetes mellitus in daily life.

Even long before it was published, many people regarded Google Glass as a Swiss army knife for nearly every task. There are some fields of application in which the best known wearable device could simplify daily life, such as car navigation or reading recipes. But does this also apply for medicine and health care? This paper will at first explain what Google Glass is and how it works. Afterwards, diabetes mellitus (DM), is discussed. Moreover, we try to answer the question whether a Glass-like device could support and improve people with DM. Therefore, several use cases for Glass-enabled chronic disease care are outlined.

GlassAllergy: a Google Glass-based solution to empower patients with skin allergies.

A variety of substances contained in cosmetic products can lead to allergic reactions for certain individuals. The names of such substances are predominantly printed onto a product in small-sized expert language. For this reason, consumers often have difficulties to assess whether some of the ingredients might be harmful for them. Consequently, patients are exposed to a risk of buying a cosmetic product that might cause a minor to severe allergic reaction. A Google Glass-based software solution for consumers suffering from skin allergies is presented. It enables users to check cosmetic products in a mobile context and empowers patients to make informed buying decisions. In particular, the solution could help to avoid or reduce the risk for allergic reactions.

Automatically Assessing the Expert Degree of Online Health Content using SVMs.

More and more people search for health information regarding diseases, diagnoses and treatments over the Web. However, lay people often have difficulties in assessing the understandability of related articles. Therefore, they could benefit from a system, which computes the medical expert degree of a corresponding piece of text in advance. In this paper we present an approach to automatically compute this expert degree using a machine learning approach. For evaluation purposes we constructed a large text corpus and tested our trained text classifier, which is based on Support Vector Machines.

Health recommender systems: concepts, requirements, technical basics and challenges.

During the last decades huge amounts of data have been collected in clinical databases representing patients' health states (e.g., as laboratory results, treatment plans, medical reports). Hence, digital information available for patient-oriented decision making has increased drastically but is often scattered across different sites. As as solution, personal health record systems (PHRS) are meant to centralize an individual's health data and to allow access for the owner as well as for authorized health professionals. Yet, expert-oriented language, complex interrelations of medical facts and information overload in general pose major obstacles for patients to understand their own record and to draw adequate conclusions. In this context, recommender systems may supply patients with additional laymen-friendly information helping to better comprehend their health status as represented by their record. However, such systems must be adapted to cope with the specific requirements in the health domain in order to deliver highly relevant information for patients. They are referred to as health recommender systems (HRS). In this article we give an introduction to health recommender systems and explain why they are a useful enhancement to PHR solutions. Basic concepts and scenarios are discussed and a first implementation is presented. In addition, we outline an evaluation approach for such a system, which is supported by medical experts. The construction of a test collection for case-related recommendations is described. Finally, challenges and open issues are discussed.

Building a widespread public health education system for developing countries in Africa.

Many developing countries struggle to move their health care system into the information age. Millions of people in Africa do not have any access to online resources to satisfy their need for adequate individual health information. Access to high quality content available in public spots could have an immense impact on people's daily life. Our browser-based health education application might help to provide a better understanding of diseases for people in developing countries. We encourage other researchers to adopt our vision for a widespread public health education system in Africa.