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Nowadays, smartphones and sensor devices can provide a variety of information about a user’s current situation. So far, many recommender systems neglect this kind of information and thus cannot provide situationspecific recommendations. Situation-aware recommender systems adapt to changes in the user’s environment and therefore are able to offer recommendations that are more appropriate for the current situation. In this paper, we present a software architecture that enables situation awareness for arbitrary recommendation techniques. The proposed system considers both (semi-)static user profiles and volatile situational knowledge to obtain meaningful recommendations. Furthermore, the implementation of the architecture in a museum of natural history is presented, which uses Complex Event Processing to achieve situation awareness.
A systematic review of the literature on survey questionnaires to assess self-medication practices
(2017)
Self-medication is of great public health importance as it often bypasses regulatory mechanisms to assure quality of health care. Nevertheless there are no established standards on how to assess self-medication. We therefore intended to systematically retrieve questionnaires and survey tools used to capture self-medication, with the aim to identify the scope of information investigated in this context and commonalities between the tools. We conducted a systematic review of the literature on questionnaires used for self-medication assessment by searching PubMed and Web of Science databases using the combinations of following keywords; self-medication, self-prescription, non-prescription, questionnaire. Truncation was used to ensure retrieval of all possible variations of search terms. The search was limited to articles published between 1st January 2000 and 31st December 2015, human studies and English language. Duplicate and irrelevant studies were excluded from the final review. A total of 158 studies were included in the review. Studies were from diverse geographical locations, most of the studies were from Nigeria 16 (10.1%) followed by India 10 (6.3%) and Iran 8 (5%). Forty-three studies (27.2%) focused on antibiotic self-medication. Majority of the studies (106; 67%) were done with adult populations. The components addressed by the questionnaires covered: reasons for self-medications in 147 (93%) studies, purchasing source in 136 (86%) studies, medical conditions to be treated in 153 (96.8%) studies, adverse events in 67 (42.4%) studies, use of prescribing information in 24 (15.1%) studies and antibiotic resistance awareness in 20 (46.5%) antibiotic studies. For 74 (46.8%) studies, survey questionnaires were self-administered and most studies (57; 36%) were done at homes of respondents. Thirty-seven (23.4%) studies did not report any recall period for self-medication practices. Study response rates varied from 17.9% to 100%, and while validity of the study questionnaire was reported for 100 (63.3%) studies, 15 (9.5%) studies reported reliability test of the study questionnaire. There is a large variety of questionnaires being used for investigating self-medication practices making comparability and meta-analyses very difficult. It is desirable to have a basic set of standardized survey questions on this topic to make available for future research groups in this field.
Antimicrobial resistance in livestock is a matter of general concern. To develop hygiene measures and methods for resistance prevention and control, epidemiological studies on a population level are needed to detect factors associated with antimicrobial resistance in livestock holdings. In general, regression models are used to describe these relationships between environmental factors and resistance outcome. Besides the study design, the correlation structures of the different outcomes of antibiotic resistance and structural zero measurements on the resistance outcome as well as on the exposure side are challenges for the epidemiological model building process. The use of appropriate regression models that acknowledge these complexities is essential to assure valid epidemiological interpretations. The aims of this paper are (i) to explain the model building process comparing several competing models for count data (negative binomial model, quasi-Poisson model, zero-inflated model, and hurdle model) and (ii) to compare these models using data from a cross-sectional study on antibiotic resistance in animal husbandry. These goals are essential to evaluate which model is most suitable to identify potential prevention measures. The dataset used as an example in our analyses was generated initially to study the prevalence and associated factors for the appearance of cefotaxime-resistant Escherichia coli in 48 German fattening pig farms. For each farm, the outcome was the count of samples with resistant bacteria. There was almost no overdispersion and only moderate evidence of excess zeros in the data. Our analyses show that it is essential to evaluate regression models in studies analyzing the relationship between environmental factors and antibiotic resistances in livestock. After model comparison based on evaluation of model predictions, Akaike information criterion, and Pearson residuals, here the hurdle model was judged to be the most appropriate model.
This document concerns IT security in production facilities. It is intended for small and medium-sized enterprises that are looking for a simple procedural model for ensuring IT security in production areas.
In order to raise readers’ awareness of IT security in production facilities, security incidents are presented in section 2. It is clear that cyber attacks on production facilities in this day and age are not random, but are instead based on a targeted process.
An overview of the most important standards and recommendations on the topic of “IT security in production” then follows in section 3.
Section 4 develops a concept for setting up an IT security system for small and medium-sized enterprises (SMEs) on the basis of a ten-point plan. The focus is not only on technical measures, but also in particular on the most frequently neglected organizational measures.
Section 5 then describes the outlook for future requirements and solutions in the context of Industry 4.0.
For indexing archived documents the Dutch Parliament uses a specialized thesaurus. For good results for full text retrieval and automatic classification it turns out to be important to add more synonyms to the existing thesaurus terms. In the present work we investigate the possibilities to find synonyms for terms of the parliaments thesaurus automatically. We propose to use distributional similarity (DS). In an experiment with pairs of synonyms and non-synonyms we train and test a classifier using distributional similarity and string similarity. Using ten-fold cross validation we were able to classify 75% of the pairs of a set of 6000 word pairs correctly.
The amount of papers published yearly increases since decades. Libraries need to make these resources accessible and available with classification being an important aspect and part of this process. This paper analyzes prerequisites and possibilities of automatic classification of medical literature. We explain the selection, preprocessing and analysis of data consisting of catalogue datasets from the library of the Hanover Medical School, Lower Saxony, Germany. In the present study, 19,348 documents, represented by notations of library classification systems such as e.g. the Dewey Decimal Classification (DDC), were classified into 514 different classes from the National Library of Medicine (NLM) classification system. The algorithm used was k-nearest-neighbours (kNN). A correct classification rate of 55.7% could be achieved. To the best of our knowledge, this is not only the first research conducted towards the use of the NLM classification in automatic classification but also the first approach that exclusively considers already assigned notations from other
classification systems for this purpose.
Editorial for the 17th European Networked Knowledge Organization Systems Workshop (NKOS 2017)
(2017)
Knowledge Organization Systems (KOS), in the form of classification systems, thesauri, lexical databases, ontologies, and taxonomies, play a crucial role in digital information management and applications generally. Carrying semantics in a well-controlled and documented way, Knowledge Organization Systems serve a variety of important functions: tools for representation and indexing of information and documents, knowledge-based support to information searchers, semantic road maps to domains and disciplines, communication tool by providing conceptual framework, and conceptual basis for knowledge based systems, e.g. automated classification systems. New networked KOS (NKOS) services and applications are emerging, and we have reached a stage where many KOS standards exist and the integration of linked services is no longer just a future scenario. This editorial describes the workshop outline and overview of presented papers at the 17th European Networked Knowledge Organization Systems Workshop (NKOS 2017) which was held during the TPDL 2017 Conference in Thessaloniki, Greece.
Background: Physician-rating websites (PRWs) may lead to quality improvements in case they enable and establish a peer-to-peer communication between patients and physicians. Yet, we know little about whether and how physicians respond on the Web to patient ratings.
Objective: The objective of this study was to describe trends in physicians’ Web-based responses to patient ratings over time, to identify what physician characteristics influence Web-based responses, and to examine the topics physicians are likely to respond to.
Methods: We analyzed physician responses to more than 1 million patient ratings displayed on the German PRW, jameda, from 2010 to 2015. Quantitative analysis contained chi-square analyses and the Mann-Whitney U test. Quantitative content techniques were applied to determine the topics physicians respond to based on a randomly selected sample of 600 Web-based ratings and corresponding physician responses.
Results: Overall, physicians responded to 1.58% (16,640/1,052,347) of all Web-based ratings, with an increasing trend over time from 0.70% (157/22,355) in 2010 to 1.88% (6377/339,919) in 2015. Web-based ratings that were responded to had significantly worse rating results than ratings that were not responded to (2.15 vs 1.74, P<.001). Physicians who respond on the Web to patient ratings differ significantly from nonresponders regarding several characteristics such as gender and patient recommendation results (P<.001 each). Regarding scaled-survey rating elements, physicians were most likely to respond to the waiting time within the practice (19.4%, 99/509) and the time spent with the patient (18.3%, 110/600). Almost one-third of topics in narrative comments were answered by the physicians (30.66%, 382/1246).
Conclusions: So far, only a minority of physicians have taken the chance to respond on the Web to patient ratings. This is likely because of (1) the low awareness of PRWs among physicians, (2) the fact that only a few PRWs enable physicians to respond on the Web to patient ratings, and (3) the lack of an active moderator to establish peer-to-peer communication. PRW providers should foster more frequent communication between the patient and the physician and encourage physicians to respond on the Web to patient ratings. Further research is needed to learn more about the motivation of physicians to respond or not respond to Web-based patient ratings.
Objective: To evaluate the impact of different dissemination channels on the awareness and usage of hospital performance reports among referring physicians, as well as the usefulness of such reports from the referring physicians’ perspective.
Data sources/Study setting: Primary data collected from a survey with 277 referring physicians (response rate = 26.2%) in Nuremberg, Germany (03–06/2016).
Study design: Cluster-randomised controlled trial at the practice level. Physician practices were randomly assigned to one of two conditions: (1) physicians in the control arm could become aware of the performance reports via mass media channels (Mass Media, npr MM=132, nph MM=147); (2) physicians in the intervention arm also received a printed version of the report via mail (Mass and Special Media, npr MSM=117; nph MSM=130). <br> Principal findings: Overall, 68% of respondents recalled hospital performance reports and 21% used them for referral decisions. Physicians from the Mass and Special Media group were more likely to be aware of the performance reports (OR 4.16; 95% CI 2.16–8.00, p < .001) but not more likely to be influenced when referring patients into hospitals (OR 1.73; 95% CI 0.72–4.12, p > .05). On a 1 (very good) to 6 (insufficient) scale, the usefulness of the performance reports was rated 3.67 (±1.40). Aggregated presentation formats were rated more helpful than detailed hospital quality information.
Conclusions: Hospital quality reports have limited impact on referral practices. To increase the latter, concerns raised by referring physicians must be given more weight. Those principally refer to the underlying data, the design of the reports, and the lack of important information.
The development of Artificial Intelligence (AI) has profound implications for improving human and computational productivity in the future. However, it also is an existential risk to human life because it could exceed human capabilities. As such, information about the technology, the direction of the development and its purpose is important. This can be achieved through openness and transparency of processes. Indeed, companies hold property rights over AI and monopolies of software, data and experts. As a countermovement to leading AI companies, the “Open AI Movement” has evolved to push open-source AI research and products, to empower users, and to bridge the digital divide through participation and access. In this thesis, the implications of the declaration of AI as a commons have been analyzed through interviews with AI experts in the United States. The legal placement of AI is controversial but it could be seen as a basic human right. Other findings are that this field is very competitive and that the best approach is to collaboratively develop software that adds additional value on the edge of the commons.