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During the Corona-Pandemic, information (e.g. from the analysis of balance sheets and payment behavior) traditionally used for corporate credit risk analysis became less valuable because it represents only past circumstances. Therefore, the use of currently published data from social media platforms, which have shown to contain valuable information regarding the financial stability of companies, should be evaluated. In this data e. g. additional information from disappointed employees or customers can be present. In order to analyze in how far this data can improve the information base for corporate credit risk assessment, Twitter data regarding the ten greatest insolvencies of German companies in 2020 and solvent counterparts is analyzed in this paper. The results from t-tests show, that sentiment before the insolvencies is significantly worse than in the comparison group which is in alignment with previously conducted research endeavors. Furthermore, companies can be classified as prospectively solvent or insolvent with up to 70% accuracy by applying the k-nearest-neighbor algorithm to monthly aggregated sentiment scores. No significant differences in the number of Tweets for both groups can be proven, which is in contrast to findings from studies which were conducted before the Corona-Pandemic. The results can be utilized by practitioners and scientists in order to improve decision support systems in the domain of corporate credit risk analysis. From a scientific point of view, the results show, that the information asymmetry between lenders and borrowers in credit relationships, which are principals and agents according to the principal-agent-theory, can be reduced based on user generated content from social media platforms. In future studies, it should be evaluated in how far the data can be integrated in established processes for credit decision making. Furthermore, additional social media platforms as well as samples of companies should be analyzed. Lastly, the authenticity of user generated contend should be taken into account in order to ensure, that credit decisions rely on truthful information only.
Since textual user generated content from social media platforms contains valuable information for decision support and especially corporate credit risk analysis, automated approaches for text classification such as the application of sentiment dictionaries and machine learning algorithms have received great attention in recent user generated content based research endeavors. While machine learning algorithms require individual training data sets for varying sources, sentiment dictionaries can be applied to texts immediately, whereby domain specific dictionaries attain better results than domain independent word lists. We evaluate by means of a literature review how sentiment dictionaries can be constructed for specific domains and languages. Then, we construct nine versions of German sentiment dictionaries relying on a process model which we developed based on the literature review. We apply the dictionaries to a manually classified German language data set from Twitter in which hints for financial (in)stability of companies have been proven. Based on their classification accuracy, we rank the dictionaries and verify their ranking by utilizing Mc Nemar’s test for significance. Our results indicate, that the significantly best dictionary is based on the German language dictionary SentiWortschatz and an extension approach by use of the lexical-semantic database GermaNet. It achieves a classification accuracy of 59,19 % in the underlying three-case-scenario, in which the Tweets are labelled as negative, neutral or positive. A random classification would attain an accuracy of 33,3 % in the same scenario and hence, automated coding by use of the sentiment dictionaries can lead to a reduction of manual efforts. Our process model can be adopted by other researchers when constructing sentiment dictionaries for various domains and languages. Furthermore, our established dictionaries can be used by practitioners especially in the domain of corporate credit risk analysis for automated text classification which has been conducted manually to a great extent up to today.
Introduction: Renal cell carcinoma (RCC), an immunogenic tumor, is the most common form of kidney cancer worldwide. Immune checkpoint inhibitors (ICIs) play an important role in the treatment of metastatic RCC. Programmed death-ligand (PD-L1) has already been proposed as a possible prognosticator for ICIs effectiveness. To elucidate the feasible role of ICIs in neoadjuvant settings, we have assessed the most common PD-L1 expression modalities [tumor proportion score (TPS), combined positivity score (CPS) and inflammatory cell (IC) score] in primary tumors (PTs) and venous tumor thrombi (VTT) in first diagnosed, previously untreated RCC patients with accompanying
VTT.
Methods: Between January 1999 and December 2016, 71 patients with a first diagnosed, untreated, locally advanced RCC (aRCC) (≥ pT3a) underwent surgery in Hanover Medical School (MHH). PD-L1 expression was examined separately in PTs and VTT using the CPS, IC score and TPS. We also considered the age at the time of the initial surgery and gender as probable influencing factors. By using a cutoff value of 1 (1%), PD-L1 expression levels in PTs and VTT were assessed to enable the determination of any frequency differences.
Results: Positive scores for PTs were shown by 54 (CPS), 53 (IC score) and 34 (TPS) patients, whereas in VTT, positive scores were evaluated
for a total of 50 (CPS), 47 (IC-score) and 36 (TPS) patients. No statistically significant differences were obtained between the PD-L1 expression immunoscores for PTs and VTT. The covariates age at the time of the initial surgery and gender could not be statistically proven to influence the differences in PD-L1 expression between the
VTT and PTs.
Conclusion: To the best of our knowledge, this research is the largest study to investigate PD-L1 expression in PTs and VTT in 71 cases. It could have relevance for the future development of neoadjuvant immunotherapy options, particularly in aRCC with VTT.
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.
Editorial for the 15th European Networked Knowledge Organization Systems Workshop (NKOS 2016)
(2016)
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 Organisation 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 15th European Networked Knowledge Organization Systems Workshop (NKOS 2016) in Hannover, Germany.
Fall events and their severe consequences represent not only a threatening problem for the affected individual, but also cause a significant burden for health care systems. Our research work aims to elucidate some of the prospects and problems of current sensor-based fall risk assessment approaches. Selected results of a questionnaire-based survey given to experts during topical workshops at international conferences are presented. The majority of domain experts confirmed that fall risk assessment could potentially be valuable for the community and that prediction is deemed possible, though limited. We conclude with a discussion of practical issues concerning adequate outcome parameters for clinical studies and data sharing within the research community. All participants agreed that sensor-based fall risk assessment is a promising and valuable approach, but that more prospective clinical studies with clearly defined outcome measures are necessary.
Background: Fall events contribute significantly to mortality, morbidity and costs in our ageing population. In order to identify persons at risk and to target preventive measures, many scores and assessment tools have been developed. These often require expertise and are costly to implement. Recent research investigates the use of wearable inertial sensors to provide objective data on motion features which can be used to assess individual fall risk automatically. So far it is unknown how well this new method performs in comparison with conventional fall risk assessment tools. The aim of our research is to compare the predictive performance of our new sensor-based method with conventional and established methods, based on prospective data.
Methods: In a first study phase, 119 inpatients of a geriatric clinic took part in motion measurements using a wireless triaxial accelerometer during a Timed Up&Go (TUG) test and a 20 m walk. Furthermore, the St. Thomas Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY) was performed, and the multidisciplinary geriatric care team estimated the patients’ fall risk. In a second follow-up phase of the study, 46 of the participants were interviewed after one year, including a fall and activity assessment. The predictive performances of the TUG, the STRATIFY and team scores are compared. Furthermore, two automatically induced logistic regression models based on conventional clinical and assessment data (CONV) as well as sensor data (SENSOR) are matched.
Results: Among the risk assessment scores, the geriatric team score (sensitivity 56%, specificity 80%) outperforms STRATIFY and TUG. The induced logistic regression models CONV and SENSOR achieve similar performance values (sensitivity 68%/58%, specificity 74%/78%, AUC 0.74/0.72, +LR 2.64/2.61). Both models are able to identify more persons at risk than the simple scores.
Conclusions: Sensor-based objective measurements of motion parameters in geriatric patients can be used to assess individual fall risk, and our prediction model’s performance matches that of a model based on conventional clinical and assessment data. Sensor-based measurements using a small wearable device may contribute significant information to conventional methods and are feasible in an unsupervised setting. More prospective research is needed to assess the cost-benefit relation of our approach.
Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups
(2012)
Background: Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients’ assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2).
Methods: A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital’s data base and matched with fall incident reports (n = 493). A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances.
Results: The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity.
Conclusions: Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity) reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack diagnostic precision. High-risk subgroups may be identified automatically from existing geriatric assessment data, especially when combined with domain knowledge in a hybrid classification model. Further work is necessary to validate our approach in a controlled prospective setting.
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.
A nonblinded, positively controlled, noninferiority trial was conducted to evaluate the efficacy of an alternative, nonantibiotic therapy with Masti Veyxym® to reduce ineffective antibiotic usage in the treatment of nonsevere clinical mastitis (CM) in cows with longer lasting udder diseases. The solely intramammary treatment with Masti Veyxym® (three applications, 12 hr apart) and the combined treatment with Masti Veyxym® and antibiotics as usual on the farm according to label of the respective product were compared with the reference treatment of solely antibiotic therapy. The matched field study was conducted on eight free-stall dairy farms located in Eastern Germany. Cases of mild-to-moderate CM in cows with longer lasting high somatic cell counts in preceding dairy herd improvement test days and with previous CM cases in current lactation were randomly allocated to one of the three treatment groups. A foremilk sample of the affected quarter was taken before treatment and again approximately 14 days and 21 days after the end of therapy for cyto-bacteriological examination. Primary outcomes were clinical cure (CC) and no CM recurrence within 60 days after the end of treatment (no R60). Bacteriological cure (BC) and quarter somatic cell count (QSCC) cure were chosen as secondary outcomes although low probabilities of BC and QSCC cure for selected cows were expected. The study resulted in the following findings: the pathogens mostly cultured from pretreatment samples were Streptococcus uberis, followed by Staphylococcus aureus and coagulase-negative staphylococci. There were no significant differences between the two test treatments in comparison with the reference treatment regarding all outcome variables. The sole therapy with Masti Veyxym® resulted in a numerically lower likelihood of BC without significant differences to the reference treatment. The combined therapy group showed a numerically higher nonrecurrence rate than the two other treatment groups and noninferiority compared to the reference treatment was proven. Having regard to the selection criteria of cows in this study, the findings indicated that sole treatment with Masti Veyxym® in nonsevere CM cases may constitute an alternative therapy to reduce antibiotics. However, noninferiority evaluations were mostly inconclusive. Further investigations with a larger sample size are required to confirm the results and to make a clear statement on noninferiority.
Background:
Hereditary angioedema (HAE) is a rare genetic disease and characterized by clinical features such as paroxysmal, recurrent angioedema of the skin, the gastrointestinal tract, and the upper airways. Swelling of the skin occurs primarily in the face, extremities and genitals. Gastrointestinal attacks are accompanied by painful abdominal cramps, vomiting and diarrhea. Due to the low prevalence and the fact that HAE patients often present with rather unspecific symptoms such as abdominal cramps, the final diagnosis is often made after a long delay. The aim of this German-wide survey was to characterize the period between occurrence of first symptoms and final diagnosis regarding self-perceived health, symptom burden and false diagnoses for patients with HAE.
Results:
Overall, 81 patients with HAE were included and participated in the telephone-based survey. Of those, the majority reported their current health status as “good” (47.5%) or “very good” (13.8%), which was observed to be a clear improvement compared to the year before final diagnosis (“good” (16.3%), “very good” (11.3%)). Edema in the extremities (85.2%) and in the gastrointestinal tract (81.5%) were the most currently reported symptoms and occurred earlier than other reported symptoms (mean age at onset 18.1 and 17.8 years, respectively). Misdiagnoses were observed in 50.6% of participating HAE patients with appendicitis and allergy being the most frequently reported misdiagnoses (40.0 and 30.0% of those with misdiagnosis, respectively). Patients with misdiagnosis often received mistreatment (80.0%) with pharmaceuticals and surgical interventions as the most frequently carried out mistreatments (65.6 and 56.3% of those with mistreatment, respectively). The mean observed diagnostic delay was 18.1 years (median 15.0 years). The diagnostic delay was higher in older patients and index patients.
Conclusions:
This study showed that self-perceived status of health for patients is much better once the final correct diagnosis has been made and specific treatment was available. Further challenge in the future will still be to increase awareness for HAE especially in settings which are normally approached by patients at occurrence of first symptoms to assure early referral to specialists and therefore increase the likelihood of receiving an early diagnosis.
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.
Corynebacterium spp. are frequently detected in bovine quarter milk samples, yet their impact on udder health has not been determined completely. In this longitudinal study, we collected quarter milk samples from a dairy herd of approximately 200 cows, ten times at 14 d intervals. Bacteriologically, Catalase-positive and Gram-positive rods were detected in 22.7% of the samples. For further species diagnosis, colonies were analyzed by MALDITOF MS. Corynebacterium bovis, C. amycolatum, C. xerosis and 10 other Corynebacterium spp. were detected. The three aforementioned species accounted for 88.4%, 8.65% and 0.94% of all cultured Corynebacterium spp., respectively. For further evaluation of infection dynamics, the following three infection definitions were applied: A (2/3 consecutive samples positive for the same species), B (≥1000 cfu/mL in one sample), C (isolated from a clinical mastitis case). Infections according to definition B occurred most frequently and clinical mastitis with Corynebacterium spp. occurred once during sampling. Life tables were used to determine the duration of infection. According to infection definition A, infection durations of 111 d and 98 d were obtained for C. bovis and C. amycolatum, respectively. Exemplarily, longer lasting infections were examined for their strain diversity by RAPD PCR. A low strain diversity was found in the individual quarters that indicates a longer colonization of the udder parenchyma by C. bovis and C. amycolatum.
In this species differentiation study of Corynebacterium spp. (C. spp.), quarter foremilk samples from 48 farms were included. These were obtained from both clinically healthy cows and those with clinical mastitis. First, all samples were examined cyto-microbiologically and all catalase-positive rods were differentiated using the direct transfer method in MALDI-TOF MS. C. bovis, C. amycolatum, C. xerosis, and five other species were identified with proportions of 90.1%, 7.7%, and 0.8% for the named species, respectively, and 1.4% for the remaining unnamed species. In addition, somatic cell count (SCC) was determined by flow cytometry. Based on this, the isolates were classified into four udder health groups: “latent infection”, “subclinical mastitis”, “clinical mastitis” and “others”. Approximately 90% of isolates of C. bovis and C. amycolatum were from latently and subclinically infected quarters. Of the C. bovis isolates, 5.8% were obtained from milk samples from clinical mastitis, whereas C. amycolatum was not present in clinical mastitis. The distribution of groups in these two species differed significantly. The geometric mean SCC of all species combined was 76,000 SCC/mL, almost the same as the SCC of C. bovis. With 50,000 SCC/mL, the SCC of C. amycolatum was slightly below the SCC of C. bovis. Through the species-level detection and consideration of SCC performed here, it is apparent that individual species differ in terms of their pathogenicity. Overall, their classification as minor pathogens with an SCC increase is confirmed.
In this paper, we consider the route coordination problem in emergency evacuation of large smart buildings. The building evacuation time is crucial in saving lives in emergency situations caused by imminent natural or man-made threats and disasters. Conventional approaches to evacuation route coordination are static and predefined. They rely on evacuation plans present only at a limited number of building locations and possibly a trained evacuation personnel to resolve unexpected contingencies. Smart buildings today are equipped with sensory infrastructure that can be used for an autonomous situation-aware evacuation guidance optimized in real time. A system providing such a guidance can help in avoiding additional evacuation casualties due to the flaws of the conventional evacuation approaches. Such a system should be robust and scalable to dynamically adapt to the number of evacuees and the size and safety conditions of a building. In this respect, we propose a distributed route recommender architecture for situation-aware evacuation guidance in smart buildings and describe its key modules in detail. We give an example of its functioning dynamics on a use case.
Immunization is the most cost-effective intervention for infectious diseases, which are the major cause of morbidity and mortality worldwide. Vaccines not only protect the individual who is vaccinated but also reduce the burden of infectious vaccine-preventable diseases for the entire community.
1 Adult vaccination is very important given that >25% of mortality is due to infectious diseases.
2 There is a scarcity of information on the vaccination status of young adults and the role of socioeconomic conditions in India.
The world health organization defines musculoskeletal disorder (MSD) as “a disorder of muscles, tendons, peripheral vascular system not directly resulting from an acute or instantaneous event.1 Work related MSDs are one of the most important occupational hazards.1 Among many other occupations, dentistry is a highly demanding profession that requires good visual acuity, hearing, depth perception, psychomotor skills, manual dexterity, and ability to maintain occupational postures over long periods.
Nanotechnology is emerging as one of the key technologies of the 21st century and is expected to enable developments across a wide range of sectors that can benefit citizens. Nanomedicine is an application of nanotechnology in the areas of healthcare, disease diagnosis, treatment and prevention of disease. Nanomedicines pose problem of nanotoxicity related to factors like size, shape, specific surface area, surface morphology, and crystallinity. Currently, nanomedicines are regulated as medicinal products or as medical devices and there is no specific regulatory framework for nanotechnology-based products neither in the EU nor in the USA. This review presents a scheme for classification and regulatory approval process for nanotechnology based medicines.
Medical devices are health care products distinguished from drugs for regulatory purposes in most countries based on mechanism of action. Unlike drugs, medical devices operate via physical or mechanical means and are not dependent on metabolism to accomplish their primary intended effect. Developing new medical devices requires clinical investigations and approval process goes through similar process like drugs. Medical device approvals in the period of 2010 to 2014 were searched from USFDA website. Disease burden data in the similar period was searched from centers for disease control and prevention website. Collected data was analyzed to know number of approved devices, top therapy areas, and mechanism of action of these devices. Out of a total of 200 medical devices approvals in the time period of 2010 to 2014, maximum number of devices (51; 25.5%) were approved in the year 2011, cardiovascular (78; 39%) was the top therapy area. Highest number (180; 90%) of approved medical devices belonged to the category III and maximum number (73; 36.5%) of approved medical devices had ―mechanical‖ mechanism of action. The top 3 causes of deaths in USA during 2010 to 2014 were heart disease, cancer and followed by respiratory infection. There was a match between the top diseases and the medical device approvals for top 2 diseases in USA i.e. heart disease, and cancer. With respect to respiratory infections and ailments which was the 3rd leading cause of death only one device was approved out of 200 approvals in total.
Background: Antimicrobial resistance has become a serious global problem. A potential post-antibiotic era is threatening present and future medical advances. In Pakistan, the usage of antibiotic is unnecessarily high and due to over exposure to these drugs, bacteria are developing resistance against these drugs. It is necessary to improve public awareness about the rational use of antibiotics in order to bring a change in consumer’s behaviour. Therefore, present study was undertaken to assess the existing knowledge, attitude and practices related to antibiotic usage among university students.
Methods: A cross-sectional study was carried out among university students from Karachi, Pakistan during May-June 2018. 200 students were approached to participate in the study of which 159 agreed to participate (males: 70, females: 89). Pretested questionnaire was distributed to the study subjects and the collected data was analyzed using IBM SPSS version 23.
Results: Substantial number of (33% and 50%) participants were unaware about the differences in antibiotic: anti-inflammatory drugs and antibiotic: antipyretics respectively. 29% of the participants thought it is right to stop antibiotics only based on symptomatic improvement. Thirty nine percent and eighty three percent participants believed that antibiotics should always be prescribed to treat flu like symptoms and pneumonia respectively.
Conclusions: Participants demonstrated average knowledge about antibiotics. Similarly, their attitude and practice toward antibiotic use was associated with misconceptions. An educational intervention is necessary to make them aware about rational use of antibiotics.