DE-Lemma: A Maximum-Entropy Based Lemmatizer for German Medical Text.

When processing written German language, it is helpful, to use the base form (or: lemma) of possibly inflected words, such as verbs, nouns or named entities. However, for German text from the (bio)medical domain, e.g., discharge letters, or entries stored in electronic medical or health records (EMR, EHR), difficulties exist in finding the correct lemma, as, for instance, the medical language has roots in Latin or Greek. In such cases, stemming techniques might provide inaccurate results for text written in German. This study demonstrates a Machine Learning approach for training Apache OpenNLP-based lemmatizer models from publicly available German treebanks. The resulting four "DE-Lemma" models were evaluated against a sample of (bio)medical nouns, randomly selected from real-world discharge letters. The most promising DE-Lemma model achieved an accuracy of 88.0% (F1 = .936).

Readability of English, German, and Russian Disease-Related Wikipedia Pages: Automated Computational Analysis.

BACKGROUND: Wikipedia is a popular encyclopedia for health- and disease-related information in which patients seek advice and guidance on the web. Yet, Wikipedia articles can be unsuitable as patient education materials, as investigated in previous studies that analyzed specific diseases or medical topics with a comparatively small sample size. Currently, no data are available on the average readability levels of all disease-related Wikipedia pages for the different localizations of this particular encyclopedia. OBJECTIVE: This study aimed to analyze disease-related Wikipedia pages written in English, German, and Russian using well-established readability metrics for each language. METHODS: Wikipedia database snapshots and Wikidata metadata were chosen as resources for data collection. Disease-related articles were retrieved separately for English, German, and Russian starting with the main concept of Human Diseases and Disorders (German: Krankheit; Russian: Заболевания человека). In the case of existence, the corresponding International Classification of Diseases, Tenth Revision (ICD-10), codes were retrieved for each article. Next, the raw texts were extracted and readability metrics were computed. RESULTS: The number of articles included in this study for English, German, and Russian Wikipedia was n=6127, n=6024, and n=3314, respectively. Most disease-related articles had a Flesch Reading Ease (FRE) score <50.00, signaling difficult or very difficult educational material (English: 5937/6125, 96.93%; German: 6004/6022, 99.7%; Russian: 2647/3313, 79.9%). In total, 70% (7/10) of the analyzed articles could be assigned an ICD-10 code with certainty (English: 4235/6127, 69.12%; German: 4625/6024, 76.78%; Russian: 2316/3314, 69.89%). For articles with ICD-10 codes, the mean FRE scores were 28.69 (SD 11.00), 20.33 (SD 9.98), and 38.54 (SD 13.51) for English, German, and Russian, respectively. A total of 9 English ICD-10 chapters (11 German and 10 Russian) showed significant differences: chapter F (FRE 23.88, SD 9.95; P<.001), chapter E (FRE 25.14, SD 9.88; P<.001), chapter H (FRE 30.04, SD 10.57; P=.049), chapter I (FRE 30.05, SD 9.07; P=.04), chapter M (FRE 31.17, 11.94; P<.001), chapter T (FRE 32.06, SD 10.51; P=.001), chapter A (FRE 32.63, SD 9.25; P<.001), chapter B (FRE 33.24, SD 9.07; P<.001), and chapter S (FRE 39.02, SD 8.22; P<.001). CONCLUSIONS: Disease-related English, German, and Russian Wikipedia articles cannot be recommended as patient education materials because a major fraction is difficult or very difficult to read. The authors of Wikipedia pages should carefully revise existing text materials for readers with a specific interest in a disease or its associated symptoms. Special attention should be given to articles on mental, behavioral, and neurodevelopmental disorders (ICD-10 chapter F) because these articles were most difficult to read in comparison with other ICD-10 chapters. Wikipedia readers should be supported by editors providing a short and easy-to-read summary for each article.

Temporal and Location Variations, and Link Categories for the Dissemination of COVID-19-Related Information on Twitter During the SARS-CoV-2 Outbreak in Europe: Infoveillance Study.

BACKGROUND: The spread of the 2019 novel coronavirus disease, COVID-19, across Asia and Europe sparked a significant increase in public interest and media coverage, including on social media platforms such as Twitter. In this context, the origin of information plays a central role in the dissemination of evidence-based information about the SARS-CoV-2 virus and COVID-19. On February 2, 2020, the World Health Organization (WHO) constituted a "massive infodemic" and argued that this situation "makes it hard for people to find trustworthy sources and reliable guidance when they need it." OBJECTIVE: This infoveillance study, conducted during the early phase of the COVID-19 pandemic, focuses on the social media platform Twitter. It allows monitoring of the dynamic pandemic situation on a global scale for different aspects and topics, languages, as well as regions and even whole countries. Of particular interest are temporal and geographical variations of COVID-19-related tweets, the situation in Europe, and the categories and origin of shared external resources. METHODS: Twitter's Streaming application programming interface was used to filter tweets based on 16 prevalent hashtags related to the COVID-19 outbreak. Each tweet's text and corresponding metadata as well as the user's profile information were extracted and stored into a database. Metadata included links to external resources. A link categorization scheme-introduced in a study by Chew and Eysenbach in 2009-was applied onto the top 250 shared resources to analyze the relative proportion for each category. Moreover, temporal variations of global tweet volumes were analyzed and a specific analysis was conducted for the European region. RESULTS: Between February 9 and April 11, 2020, a total of 21,755,802 distinct tweets were collected, posted by 4,809,842 distinct Twitter accounts. The volume of #covid19-related tweets increased after the WHO announced the name of the new disease on February 11, 2020, and stabilized at the end of March at a high level. For the regional analysis, a higher tweet volume was observed in the vicinity of major European capitals or in densely populated areas. The most frequently shared resources originated from various social media platforms (ranks 1-7). The most prevalent category in the top 50 was "Mainstream or Local News." For the category "Government or Public Health," only two information sources were found in the top 50: US Centers for Disease Control and Prevention at rank 25 and the WHO at rank 27. The first occurrence of a prevalent scientific source was Nature (rank 116). CONCLUSIONS: The naming of the disease by the WHO was a major signal to address the public audience with public health response via social media platforms such as Twitter. Future studies should focus on the origin and trustworthiness of shared resources, as monitoring the spread of fake news during a pandemic situation is of particular importance. In addition, it would be beneficial to analyze and uncover bot networks spreading COVID-19-related misinformation.

Barriers and Facilitators to the Implementation of eHealth Services: Systematic Literature Analysis.

BACKGROUND: The field of eHealth has a history of more than 20 years. During that time, many different eHealth services were developed. However, factors influencing the adoption of such services were seldom the main focus of analyses. For this reason, organizations adopting and implementing eHealth services seem not to be fully aware of the barriers and facilitators influencing the integration of eHealth services into routine care. OBJECTIVE: The objective of this work is to provide (1) a comprehensive list of relevant barriers to be considered and (2) a list of facilitators or success factors to help in planning and implementing successful eHealth services. METHODS: For this study, a twofold approach was applied. First, we gathered experts' current opinions on facilitators and barriers in implementing eHealth services via expert discussions at two health informatics conferences held in Europe. Second, we conducted a systematic literature analysis concerning the barriers and facilitators for the implementation of eHealth services. Finally, we merged the results of the expert discussions with those of the systematic literature analysis. RESULTS: Both expert discussions (23 and 10 experts, respectively) identified 15 barriers and 31 facilitators, whereas 76 barriers and 268 facilitators were found in 38 of the initial 56 articles published from 12 different countries. For the analyzed publications, the count of distinct barriers reported ranged from 0 to 40 (mean 10.24, SD 8.87, median 8). Likewise, between 0 and 48 facilitators were mentioned in the literature (mean 9.18, SD 9.33, median 6). The combination of both sources resulted in 77 barriers and 292 facilitators for the adoption and implementation of eHealth services. CONCLUSIONS: This work contributes a comprehensive list of barriers and facilitators for the implementation and adoption of eHealth services. Addressing barriers early, and leveraging facilitators during the implementation, can help create eHealth services that better meet the needs of users and provide higher benefits for patients and caregivers.

Computer-Based Readability Testing of Information Booklets for German Cancer Patients.

Understandable health information is essential for treatment adherence and improved health outcomes. For readability testing, several instruments analyze the complexity of sentence structures, e.g., Flesch-Reading Ease (FRE) or Vienna-Formula (WSTF). Moreover, the vocabulary is of high relevance for readers. The aim of this study is to investigate the agreement of sentence structure and vocabulary-based (SVM) instruments. A total of 52 freely available German patient information booklets on cancer were collected from the Internet. The mean understandability level L was computed for 51 booklets. The resulting values of FRE, WSTF, and SVM were assessed pairwise for agreement with Bland-Altman plots and two-sided, paired t tests. For the pairwise comparison, the mean L values are LFRE = 6.81, LWSTF = 7.39, LSVM = 5.09. The sentence structure-based metrics gave significantly different scores (P < 0.001) for all assessed booklets, confirmed by the Bland-Altman analysis. The study findings suggest that vocabulary-based instruments cannot be interchanged with FRE/WSTF. However, both analytical aspects should be considered and checked by authors to linguistically refine texts with respect to the individual target group. Authors of health information can be supported by automated readability analysis. Health professionals can benefit by direct booklet comparisons allowing for time-effective selection of suitable booklets for patients.

Technology Adoption, Motivational Aspects, and Privacy Concerns of Wearables in the German Running Community: Field Study.

BACKGROUND: Despite the availability of a great variety of consumer-oriented wearable devices, perceived usefulness, user satisfaction, and privacy concerns have not been fully investigated in the field of wearable applications. It is not clear why healthy, active citizens equip themselves with wearable technology for running activities, and what privacy and data sharing features might influence their individual decisions. OBJECTIVE: The primary aim of the study was to shed light on motivational and privacy aspects of wearable technology used by healthy, active citizens. A secondary aim was to reevaluate smart technology adoption within the running community in Germany in 2017 and to compare it with the results of other studies and our own study from 2016. METHODS: A questionnaire was designed to assess what wearable technology is used by runners of different ages and sex. Data on motivational factors were also collected. The survey was conducted at a regional road race event in May 2017, paperless via a self-implemented app. The demographic parameters of the sample cohort were compared with the event's official starter list. In addition, the validation included comparison with demographic parameters of the largest German running events in Berlin, Hamburg, and Frankfurt/Main. Binary logistic regression analysis was used to investigate whether age, sex, or course distance were associated with device use. The same method was applied to analyze whether a runner's age was predictive of privacy concerns, openness to voluntary data sharing, and level of trust in one's own body for runners not using wearables (ie, technological assistance considered unnecessary in this group). RESULTS: A total of 845 questionnaires were collected. Use of technology for activity monitoring during events or training was prevalent (73.0%, 617/845) in this group. Male long-distance runners and runners in younger age groups (30-39 years: odds ratio [OR] 2.357, 95% CI 1.378-4.115; 40-49 years: OR 1.485, 95% CI 0.920-2.403) were more likely to use tracking devices, with ages 16 to 29 years as the reference group (OR 1). Where wearable technology was used, 42.0% (259/617) stated that they were not concerned if data might be shared by a device vendor without their consent. By contrast, 35.0% (216/617) of the participants would not accept this. In the case of voluntary sharing, runners preferred to exchange tracked data with friends (51.7%, 319/617), family members (43.4%, 268/617), or a physician (32.3%, 199/617). A large proportion (68.0%, 155/228) of runners not using technology stated that they preferred to trust what their own body was telling them rather than trust a device or an app (50-59 years: P<.001; 60-69 years: P=.008). CONCLUSIONS: A total of 136 distinct devices by 23 vendors or manufacturers and 17 running apps were identified. Out of 4, 3 runners (76.8%, 474/617) always trusted in the data tracked by their personal device. Data privacy concerns do, however, exist in the German running community, especially for older age groups (30-39 years: OR 1.041, 95% CI 0.371-0.905; 40-49 years: OR 1.421, 95% CI 0.813-2.506; 50-59 years: OR 2.076, 95% CI 1.813-3.686; 60-69 years: OR 2.394, 95% CI 0.957-6.183).

Analyzing the Readability of Health Information Booklets on Cardiovascular Diseases.

A low level of patient health literacy represents a major reason for worse prognosis or reduced therapy adherence. Health information booklets are a major tool for improving patient's health literacy. This paper presents a computer-based readability analysis of patient information booklets from the cardiovascular domain. The study relies on 34 English booklets mostly on heart disease, prevention and procedures. It compares five different, well-established readability instruments. On average, readers of the assessed booklets have to visit school at least until the 9th U.S. school grade when applying the Flesch-Kincaid formula. According to the Gunning-Fog metric, readers would have to attend school until the 11th grade. The presented study demonstrates the feasibility of a fully automated text processing tool-chain for patient information booklets. The results reveal that readability metrics should be carefully interpreted and only be interchanged with caution.

Understandability of Patient Information Booklets for Patients with Cancer.

The improvement of health literacy in general and the information of individual patient is a major concern of the German national cancer plan and similar initiatives in other western countries. The aim of our study was to assess the readability and understandability of information booklets for cancer patients available at German Web sites. A support vector machine (SVM) was used to discriminate between laymen- and expert-centric patient information booklets about nine most common tumor types. All booklets had to be available for free at the Internet. A total of 52 different patient booklets were downloaded and assessed. Overall, the assessment of all booklets showed that an understandability level L of 4.6 and therefore increased medical background knowledge is required to understand a random text selected from the sample. The assessed information booklets on cancer show very limited suitability for laymen. We were able to demonstrate that a medical background is necessary to understand the examined booklets. The current study highlights the need to create information material adjusted to the needs of laymen. Assessing understandability before publication, especially for laymen with low health literacy, could ensure the suitability and thus quality of the information material.

Accuracy and Adoption of Wearable Technology Used by Active Citizens: A Marathon Event Field Study.

BACKGROUND: Today, runners use wearable technology such as global positioning system (GPS)-enabled sport watches to track and optimize their training activities, for example, when participating in a road race event. For this purpose, an increasing amount of low-priced, consumer-oriented wearable devices are available. However, the variety of such devices is overwhelming. It is unclear which devices are used by active, healthy citizens and whether they can provide accurate tracking results in a diverse study population. No published literature has yet assessed the dissemination of wearable technology in such a cohort and related influencing factors. OBJECTIVE: The aim of this study was 2-fold: (1) to determine the adoption of wearable technology by runners, especially "smart" devices and (2) to investigate on the accuracy of tracked distances as recorded by such devices. METHODS: A pre-race survey was applied to assess which wearable technology was predominantly used by runners of different age, sex, and fitness level. A post-race survey was conducted to determine the accuracy of the devices that tracked the running course. Logistic regression analysis was used to investigate whether age, sex, fitness level, or track distance were influencing factors. Recorded distances of different device categories were tested with a 2-sample t test against each other. RESULTS: A total of 898 pre-race and 262 post-race surveys were completed. Most of the participants (approximately 75%) used wearable technology for training optimization and distance recording. Females (P=.02) and runners in higher age groups (50-59 years: P=.03; 60-69 years: P<.001; 70-79 year: P=.004) were less likely to use wearables. The mean of the track distances recorded by mobile phones with combined app (mean absolute error, MAE=0.35 km) and GPS-enabled sport watches (MAE=0.12 km) was significantly different (P=.002) for the half-marathon event. CONCLUSIONS: A great variety of vendors (n=36) and devices (n=156) were identified. Under real-world conditions, GPS-enabled devices, especially sport watches and mobile phones, were found to be accurate in terms of recorded course distances.

A Smartwatch-Driven Medication Management System Compliant to the German Medication Plan.

Medication adherence is an important factor for the outcome of medical therapies. To support high adherence levels, smartwatches can be used to assist the patient. However, a successful integration of such devices into clinicians' or general practitioners' information systems requires the use of standards. In this paper, a medication management system supplied with smartwatch generated feedback events is presented. It allows physicians to manage their patients' medications and track their adherence in real time. Moreover, it fosters interoperability via a ISO/IEC 16022 data matrix which encodes related medication data in compliance with the German Medication Plan specification.

Consumer Health Informatics in the Context of Engaged Citizens and eHealth Services - A New CHI Meta Model.

Consumer Health Informatics (CHI) is a relatively new and interdisciplinary field in Medical Informatics. It focuses on consumer- rather than professional-centered services. However, the definitions and understanding of a) what is a "consumer"? or b) what is health technology in the context of CHI? and c) what factors and actors influence the usage of eHealth services? vary widely. The CHI special interest group (SIG) - associated with the German Association for Medical Informatics, Biometry and Epidemiology - conducted two workshops in 2015 to improve the common understanding on these topics. The workshop outcomes, the derived CHI-specific meta model and examples how to apply this model are presented in this paper. The model supports the definition of multi-actor contexts, as it not solely reflects the conventional patient-physician relationship but also allows for the description of second health market providers.

[Patient guidelines in oncology: objectives, procedures and first experiences with this format]. / Patientenleitlinien in der Onkologie: Zielsetzung, Vorgehen und erste Erfahrungen mit dem Format.

For several years patient versions of guidelines have become mandatory in the German Guidelines Program in Oncology (GGPO). Based on the methodology that has been developed for the German National Disease Management Guidelines Program, patient versions of guidelines translate the recommendations of clinical practice guideline into plain language and provide information about the harms and benefits of the interventions being addressed in the guideline. They are developed by a group of guideline authors (experts as well as patients), they are consensus-based and aim to create transparency in recommendations for physicians and their rationales. An automated analysis of readability shows that patient versions of guidelines are specific to the target group of educated lay people. Moreover, the responses to a reader feedback questionnaire indicate that comprehensibility, level of detail and depth of information are considered highly relevant and positive by users. Thus, patient versions of guidelines meet the needs of a specific target group. Nevertheless, the development of other formats for readers with low levels of health literacy or cognitive competencies is desirable. Currently it remains unclear if these simplified formats are able to reflect the complexity of high quality clinical practice guidelines.

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.