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Wikidata and Wikibase as complementary research data management services for cultural heritage data
(2022)
The NFDI (German National Research Data Infrastructure) consortia are associations of various institutions within a specific research field, which work together to develop common data infrastructures, guidelines, best practices and tools that conform to the principles of FAIR data. Within the NFDI, a common question is: What is the potential of Wikidata to be used as an application for science and research? In this paper, we address this question by tracing current research usecases and applications for Wikidata, its relation to standalone Wikibase instances, and how the two can function as complementary services to meet a range of research needs. This paper builds on lessons learned through the development of open data projects and software services within the Open Science Lab at TIB, Hannover, in the context of NFDI4Culture – the consortium including participants across the broad spectrum of the digital libraries, archives, and museums field, and the digital humanities.
Background
The business of clinical research has changed in the past two decades, shifting from industrialised Western countries to so-called emerging markets such as Eastern Europe, Latin America and Africa. An appraisal of the trends could identify associated factors that may have implications for the local populations and their endemic diseases.
Objectives
To identify potential reasons why emerging countries have become attractive places for international sponsors to conduct their clinical trials.
Methods
Using ClinicalTrials.gov, the Pan African Clinical Trials Registry, the National Health Research Database and the Nigeria Clinical Trials Registry, trend data on clinical research development were determined for two emerging African markets, Nigeria and South Africa (SA), from 2010 to 2018. Also, health data on the two countries from the fact sheets of health statistics of the World Health Organization were compared, as well as regulatory and ethical conditions. Available data were analysed using descriptive statistics and trend analysis.
Results
The impact of globalisation is evident from the upward trend in clinical trials in SA before 2010, and the clear downward trend thereafter. One reason for this change could be the alignment of SA’s regulatory and ethical frameworks with the Western world. In contrast,
the upward trend is only just beginning in Nigeria, with the introduction of ethical/regulatory frameworks, and supportive institutions making the business of clinical research more attractive on an international level. Although the number of international and local sponsors increased in Nigeria from 2010 to 2018, only the latter increased in SA, with the former decreasing over the same period. Overall, there is a mismatch between country-specific diseases and the drugs being tested, to the extent that leprosy, which is endemic in Nigeria, and tuberculosis in SA were not in the list of top 10 study areas in either country.
Conclusions
The globalisation trend is evident in the clinical trials business, but cannot be generalised to all emerging countries. Timing and intensity vary from country to country relative to factors that advance the existing profit-orientated business models of the sponsors. Furthermore, various diseases have been localised, which entails a diversely increasing need for research.
Objectives:
The aim was to identify theoretically expected as well as actually reported benefits from drug development and the importance of individual patient benefits compared to the collective benefits to society in general.
Background:
Ethical guidelines require that clinical research involving humans offer the potential for benefit. A number of characteristics can be applied to define research benefit. Often benefit is categorized as being either direct or indirect. Indirect benefits can involve collective benefits for society rather than any benefits to the trial patient or subject. The purpose of this review was to examine which potential individual and societal benefits were mentioned as being expected in publications from government experts and which were mentioned in publications describing completed drug development trial results.
Methods:
Literature on research benefit was first identified by searching the PubMed database using several combinations of the key words benefit and clinical research. The search was limited to articles published in English. A Google search with the same combinations of key words but without any language limitation was then performed. Additionally, the reference lists of promising articles were screened for further thematically related articles. Finally, a narrative review was performed of relevant English- and German-language articles published between 1996 and 2016 to identify which of several potential benefits were either theoretically expected or which were mentioned in publications on clinical drug development trial results.
Results:
The principal benefits from drug development discussed included 2 main types of benefit, namely individual benefits for the patients and collective benefits for society. Twenty-one of an overall total of 26 articles discussing theoretically expected benefits focused on individual patient benefits, whereas 17 out of 26 articles mentioned collective benefits to society. In these publications, the most commonly mentioned theoretically expected individual patient benefit was the chance to receive up-to-date care (38.1%). A general increase in knowledge about health care, treatments, or drugs (70.6%) was the most commonly mentioned theoretically expected benefit for society. In contrast, all 13 publications reporting actual benefits of clinical drug development trials focused on personal benefits and only 1 of these publications also mentioned a societal benefit. The most commonly mentioned individual benefit was an increased quality of life (53.9%), whereas the only mentioned collective benefit to society was a general gain of knowledge (100.0%).
Conclusions:
Both theoretically expected and actually reported benefits in the majority of the included publications emphasized the importance of individual patient benefits from drug development rather than the collective benefits to society in general. The authors of these publications emphasized the right of each individual patient or subject to look for and expect some personal benefit from participating in a clinical trial rather than considering societal benefit as a top priority. From an ethical point of view, the benefits each individual patient receives from his or her participation in a clinical trial might also be seen as a societal benefit, especially when the drug or device tested, if approved for marketing, would eventually be made available for other similar patients from the country in which the clinical trial was conducted.
The NOA project collects and stores images from open access publications and makes them findable and reusable. During the project a focus group workshop was held to determine whether the development is addressing researchers’ needs. This took place before the second half of the project so that the results could be considered for further development since addressing users’ needs is a big part of the project. The focus was to find out what content and functionality they expect from image repositories.
In a first step, participants were asked to fill out a survey about their images use. Secondly, they tested different use cases on the live system. The first finding is that users have a need for finding scholarly images but it is not a routine task and they often do not know any image repositories. This is another reason for repositories to become more open and reach users by integrating with other content providers. The second finding is that users paid attention to image licenses but struggled to find and interpret them while also being unsure how to cite images. In general, there is a high demand for reusing scholarly images but the existing infrastructure has room to improve.
Wearable sensors in healthcare and sensor-enhanced health information systems: all our tomorrows?
(2012)
Wearable sensor systems which allow for remote or self-monitoring of health-related parameters are regarded as one means to alleviate the consequences of demographic change. This paper aims to summarize current research in wearable sensors as well as in sensor-enhanced health information systems. Wearable sensor technologies are already advanced in terms of their technical capabilities and are frequently used for cardio-vascular monitoring. Epidemiologic predictions suggest that neuro-psychiatric diseases will have a growing impact on our health systems and thus should be addressed more intensively. Two current project examples demonstrate the benefit of wearable sensor technologies: long-term, objective measurement under daily-life, unsupervised conditions. Finally, up-to-date approaches for the implementation of sensor-enhanced health information systems are outlined. Wearable sensors are an integral part of future pervasive, ubiquitous and person-centered health
care delivery. Future challenges include their integration into sensor-enhanced health information systems and sound evaluation studies involving measures of workload reduction and costs.
Background: Depletion of ovarian hormone in postmenopausal women has been associated with changes in the locomotor apparatus that may compromise walking function including muscle atrophy/weakness, weight gain, and bone demineralization. Therefore, handgrip strength (HGS), bone mineral density (BMD) and body composition [percentage body fat mass (%BFM), fat mass (FM), Fat-free mass (FFM) and body mass index (BMI)], may significantly vary and predict WB in postmenopausal women. Consequently, the study sought to 1. Explore body composition, BMD and muscle strength differences between premenopausal and postmenopausal women and 2. Explore how these variables [I.e., body composition, BMD and muscle strength] relate to WB in postmenopausal women.
Method: Fifty-one pre-menopausal (35.74 + 1.52) and 50 postmenopausal (53.32 + 2.28) women were selected by convenience sampling and studied. Six explanatory variables (HGS, BMD, %BFM, FFM, BMI and FM) were explored to predict WB in postmenopausal women: Data collected were analyzed using multiple linear regression, ANCOVA, independent t-test and Pearson correlation coefficient at p < 0.05.
Result: Postmenopausal women had higher BMI(t = + 1.72; p = 0.04), %BFM(t = + 2.77; p = .003), FM(t = + 1.77; p = 0.04) and lower HGS(t = − 3.05; p = 0.001),compared to the premenopausal women. The predicted main effect of age on HGS was not significant, F(1, 197) = 0.03, p = 0.06, likewise the interaction between age and %BFM, F(1, 197) = 0.02, p = 0.89; unlike the predicted main effect of %BFM, F(1, 197) = 10.34, p = .002, on HGS. HGS was the highest predictor of WB (t = 2.203; β=0.3046) in postmenopausal women and combined with T-score right big toe (Tscorert) to produce R2 = 0.11;F (2, 47)=4.11;p = 0.02 as the best fit for the predictive model. The variance (R2) change was significant from HGS model (R2 = 0.09;p = 0.03) to HGS + Tscorert model (R2 = 0.11;p = 0.02). The regression model equation was therefore given as: WB =5.4805 + 0.1578(HGS) + (− 1.3532) Tscorert.
Conclusion: There are differences in body composition suggesting re-compartmentalization of the body, which may adversely impact the (HGS) muscle strength in postmenopausal women. Muscle strength and BMD areassociated with WB, although, only contribute to a marginal amount of the variance for WB. Therefore, other factors in addition to musculoskeletal health are necessary to mitigate fall risk in postmenopausal women.
Scientific papers from all disciplines contain many abbreviations and acronyms. In many cases these acronyms are ambiguous. We present a method to choose the contextual correct definition of an acronym that does not require training for each acronym and thus can be applied to a large number of different acronyms with only few instances. We constructed a set of 19,954 examples of 4,365 ambiguous acronyms from image captions in scientific papers along with their contextually correct definition from different domains. We learn word embeddings for all words in the corpus and compare the averaged context vector of the words in the expansion of an acronym with the weighted average vector of the words in the context of the acronym. We show that this method clearly outperforms (classical) cosine similarity. Furthermore, we show that word embeddings learned from a 1 billion word corpus of scientific exts outperform word embeddings learned from much larger general corpora.
Using openEHR Archetypes for Automated Extraction of Numerical Information from Clinical Narratives
(2019)
Up to 80% of medical information is documented by unstructured data such as clinical reports written in natural language. Such data is called unstructured because the information it contains cannot be retrieved automatically as straightforward as from structured data. However, we assume that the use of this flexible kind of documentation will remain a substantial part of a patient’s medical record, so that clinical information systems have to deal appropriately with this type of information description. On the other hand, there are efforts to achieve semantic interoperability between clinical application systems through information modelling concepts like HL7 FHIR or openEHR. Considering this, we propose an approach to transform unstructured documented information into openEHR archetypes. Furthermore, we aim to support the field of clinical text mining by recognizing and publishing the connections between openEHR archetypes and heterogeneous phrasings. We have evaluated our method by extracting the values to three openEHR archetypes from unstructured documents in English and German language.
For the introduction of technical nursing care innovations, a usability assessment survey is conducted by nursing staff. The questionnaire is used before and after the introduction of technical products. This poster contribution shows the latest comparison of pre- and post-surveys on selected products.
Type 2 Diabetes Mellitus: Risk Evaluation and Advice in Undergraduate Students in Ashrafieh, Lebanon
(2016)
Type 2 diabetes mellitus (T2DM) is a chronic lifestyle disease. It has become evident that T2DM occurs even among the younger age groups.1 In Lebanon, T2DM has a major public health impact through high disease prevalence, significant downstream pathophysiologic effects, and enormous financial liabilities.2
Introduction
Atopic dermatitis (AD) is a common inflammatory skin disease. Many patients are initiating a systemic therapy, if the disease is not adequately controlled with topical treatment only. Currently, there is little real-world evidence on the AD-related medical care situation in Germany. This study analyzed patient characteristics, treatment patterns, healthcare resource utilization and costs associated with systemically treated AD for the German healthcare system.
Methods
In this descriptive, retrospective cohort study, aggregated anonymized German health claims data from the InGef research database were used. Within a representative sample of four million insured individuals, patients with AD and systemic drug therapy initiation (SDTI) in the index year 2017 were identified and included into the study cohort. Systemic drug therapy included dupilumab, systemic corticosteroids (SCS) and systemic immunosuppressants (SIS). Patients were observed for one year starting from the date of SDTI in 2017.
Results
9975 patients were included (57.8% female, mean age 39.6 years [SD 25.5]). In the one-year observation period, the most common systemic drug therapy was SCS (> 99.0%). Administrations of dupilumab (0.3%) or dispensations of SIS were rare (cyclosporine: 0.5%, azathioprine: 0.6%, methotrexate: 0.1%). Median treatment duration of SCS, cyclosporine and azathioprine was 27 days, 102 days, and 109 days, respectively. 2.8% of the patients received phototherapy; 41.6% used topical corticosteroids and/or topical calcineurin inhibitor. Average annual costs for medications amounted to € 1237 per patient. Outpatient services were used by 99.6% with associated mean annual costs of € 943; 25.4% had at least one hospitalization (mean annual costs: € 5836). 5.3% of adult patients received sickness benefits with associated mean annual costs of € 5026.
Conclusions
Despite unfavorable risk–benefit profile, this study demonstrated a common treatment with SCS, whereas other systemic drug therapy options were rarely used. Furthermore, the results suggest a substantial economic burden for patients with AD and SDTI.
Background: One of the major challenges in pediatric intensive care is the detection of life-threatening health conditions under acute time constraints and performance pressure. This includes the assessment of pediatric organ dysfunction (OD) that demands extraordinary clinical expertise and the clinician’s ability to derive a decision based on multiple information and data sources. Clinical decision support systems (CDSS) offer a solution to support medical staff in stressful routine work. Simultaneously, detection of OD by using computerized decision support approaches has been scarcely investigated, especially not in pediatrics.
Objectives: The aim of the study is to enhance an existing, interoperable, and rulebased CDSS prototype for tracing the progression of sepsis in critically ill children by augmenting it with the capability to detect SIRS/sepsis-associated hematologic OD, and to determine its diagnostic accuracy.
Methods: We reproduced an interoperable CDSS approach previously introduced by our working group: (1) a knowledge model was designed by following the commonKADS methodology, (2) routine care data was semantically standardized and harmonized using openEHR as clinical information standard, (3) rules were formulated and implemented in a business rule management system. Data from a prospective diagnostic study, including 168 patients, was used to estimate the diagnostic accuracy of the rule-based CDSS using the clinicians’ diagnoses as reference
Objectives: Injury to major white matter pathways during language-area associated glioma surgery often leads to permanent loss of neurological function. The aim was to establish standardized tractography of language pathways as a predictor of language outcome in clinical neurosurgery.
Methods: We prospectively analyzed 50 surgical cases of patients with left perisylvian, diffuse gliomas. Standardized preoperative Diffusion-Tensor-Imaging (DTI)-based tractography of the 5 main language tracts (Arcuate Fasciculus [AF], Frontal Aslant Tract [FAT], Inferior Fronto-Occipital Fasciculus [IFOF], Inferior Longitudinal Fasciculus [ILF], Uncinate Fasciculus [UF]) and spatial analysis of tumor and tracts was performed. Postoperative imaging and the resulting resection map were analyzed for potential surgical injury of tracts. The language status was assessed preoperatively, postoperatively and after 3 months using the Aachen Aphasia Test and Berlin Aphasia Score. Correlation analyses, two-step cluster analysis and binary logistic regression were used to analyze associations of tractography results with language outcome after surgery.
Results: In 14 out of 50 patients (28%), new aphasic symptoms were detected 3 months after surgery. The preoperative infiltration of the AF was associated with functional worsening (cc = 0.314; p = 0.019). Cluster analysis of tract injury profiles revealed two areas particularly related to aphasia: the temporo-parieto-occipital junction (TPO; temporo-parietal AF, middle IFOF, middle ILF) and the temporal stem/peri-insular white matter (middle IFOF, anterior ILF, temporal UF, temporal AF). Injury to these areas (TPO: OR: 23.04; CI: 4.11 – 129.06; temporal stem: OR: 21.96; CI: 2.93 – 164.41) was associated with a higher-risk of persisting aphasia.
Conclusions: Tractography of language pathways can help to determine the individual aphasia risk profile presurgically. The TPO and temporal stem/peri-insular white matter were confirmed as functional nodes particularly sensitive to surgical injuries.
Appropriate data models are essential for the systematic collection, aggregation, and integration of health data and for subsequent analysis. However, recommendations for modeling health data are often not publicly available within specific projects. Therefore, the project Zukunftslabor Gesundheit investigates recommendations for modeling. Expert interviews with five experts were conducted and analyzed using qualitative content analysis. Based on the condensed categories “governance”, “modeling” and “standards”, the project team generated eight hypotheses for recommendations on health data modeling. In addition, relevant framework conditions such as different roles, international cooperation, education/training and political influence were identified. Although emerging from interviewing a small convenience sample of experts, the results help to plan more extensive data collections and to create recommendations for health data modeling.
In the context of the ongoing digitization of interdisciplinary subjects, the need for digital literacy is increasing in all areas of everyday life. Furthermore, communication between science and society is facing new challenges, not least since the COVID-19 pandemic. In order to deal with these challenges and to provide target-oriented online teaching, new educational concepts for the transfer of knowledge to society are necessary. In the transfer project “Zukunftslabor Gesundheit” (ZLG), a didactic concept for the creation of E-Learning classes was developed. A key factor for the didactic concept is addressing heterogeneous target groups to reach the broadest possible spectrum of participants. The concept has already been used for the creation of the first ZLG E-Learning courses. This article outlines the central elements of the developed didactic concept and addresses the creation of the ZLG courses. The courses created so far appeal to different target groups and convey diverse types of knowledge at different levels of difficulty.
In this poster we present the ongoing development of an integrated free and open source toolchain for semantic annotation of digitised cultural heritage. The toolchain development involves the specification of a common data model that aims to increase interoperability across diverse datasets and to enable new collaborative research approaches.
Research information, i.e., data about research projects, organisations, researchers or research outputs such as publications or patents, is spread across the web, usually residing in institutional and personal web pages or in semi-open databases and information systems. While there exists a wealth of unstructured information, structured data is limited and often exposed following proprietary or less-established schemas and interfaces. Therefore, a holistic and consistent view on research information across organisational and national boundaries is not feasible. On the other hand, web crawling and information extraction techniques have matured throughout the last decade, allowing for automated approaches of harvesting, extracting and consolidating research information into a more coherent knowledge graph. In this work, we give an overview of the current state of the art in research information sharing on the web and present initial ideas towards a more holistic approach for boot-strapping research information from available web sources.
From an ethical perspective, clinical research involving humans is only acceptable if it involves the potential for benefit. Various characteristics can be applied to differentiate research benefit. Often benefit is categorized in direct or indirect benefit, whereby indirect benefit might be further differentiated in collective or benefit for the society, excluding or including the trial patient in the long term. Ethical guidelines, such as the Declaration of Helsinki in its latest version, do not precisely favor a particular type of benefit.
The practice, attitude, and knowledge of complementary and alternative medicine in Mumbai, India
(2020)
Background: In the recent times, there has been a resurging interest in the use of complementary and alternative medicine (CAM) in India. The present study was conducted to examine the prevalence of CAM use in Mumbai, the knowledge and attitude regarding CAM regarding its safety and efficacy and the reasons for the use of CAM.
Methods: A cross-sectional study was conducted among the general population of Mumbai and its adjoining regions during January-July 2020. 205 residents participated in the study and were asked to fill a pretested questionnaire. The collected data was analyzed using IBM SPSS version 23.
Results: Out of the 205 responses, 163 (79.51%) agreed to have used CAM at least once in their life. Of these, 108 (52.68%) respondents used Ayurveda and 105 (51.21%) used homeopathy. 60 (36.81%) of the respondents practicing CAM used it for common gastrointestinal (GIT)-related disorder with a 100% recovery rate, 125 (76.67%) for infectious diseases with a 93.6% recovery rate. 99 (60.74%) of the respondents preferring CAM for its safety profile, 68 (41.72%) believed that CAM is time tested and thus is efficacious. An integrative approach was suggested by 118 (57.56%) of all the respondents.
Conclusions: There is a disparity between the high prevalence in the use of CAM and its knowledge. However, a general consensus suggests that CAM is efficacious and is practiced for various indications.