Search Results
Calibrating mini-mental state examination scores to predict misdiagnosed dementia patients
Mini-Mental State Examination (MMSE) is used as a diagnostic test for dementia to screen a patient’s cognitive assessment and disease severity. However, these examinations are often inaccurate and unreliable either due to human error or due to patients’ physical disability to correctly interpret the questions as well as motor deficit. Erroneous data may lead to a wrong assessment of a specific patient. Therefore, other clinical factors (e.g., gender and comorbidities) existing in electronic health records, can also play a significant role, while reporting her examination results. This work considers various clinical attributes of dementia patients to accurately determine their cognitive status in terms of the Mini-Mental State Examination (MMSE) Score. We employ machine learning models to calibrate MMSE score and classify the correctness of diagnosis among patients, in order to assist clinicians in a better understanding of the progression of cognitive impairment and subsequent treatment. For this purpose, we utilize a curated real-world ageing study data. A random forest prediction model is employed to estimate the Mini-Mental State Examination score, related to the diagnostic classification of patients.This model uses various clinical attributes to provide accurate MMSE predictions, succeeding in correcting an important percentage of cases that contain previously identified miscalculated scores in our dataset. Furthermore, we provide an effective classification mechanism for automatically identifying patient episodes with inaccurate MMSE values with high confidence. These tools can be combined to assist clinicians in automatically finding episodes within patient medical records where the MMSE score is probably miscalculated and estimating what the correct value should be. This provides valuable support in the decision making process for diagnosing potential dementia patients.
Die Rolle der ORCID iD in der Wissenschaftskommunikation: Der Beitrag des ORCID-Deutschland-Konsortiums und das ORCID-DE-Projekt
ORCID’s services such as the unambiguous linking of researchers and their research output form the basis of modern scholarly communication. The ORCID Germany Consortium offers a reduced ORCID premium membership fee and supports its members during ORCID integration. Services include a dialogue platform that provides German-language information and additional support services. Another major success factor is an all-encompassing communication strategy: members of the ORCID implementation can resort to established organizational communication channels. Together and with the support of the ORCID DE project they contribute significantly to the successful distribution of ORCID in Germany.
Persistent Identification Of Instruments
Instruments play an essential role in creating research data. Given the importance of instruments and associated metadata to the assessment of data quality and data reuse, globally unique, persistent and resolvable identification of instruments is crucial. The Research Data Alliance Working Group Persistent Identification of Instruments (PIDINST) developed a community-driven solution for persistent identification of instruments which we present and discuss in this paper. Based on an analysis of 10 use cases, PIDINST developed a metadata schema and prototyped schema implementation with DataCite and ePIC as representative persistent identifier infrastructures and with HZB (Helmholtz-Zentrum Berlin für Materialien und Energie) and BODC (British Oceanographic Data Centre) as representative institutional instrument providers. These implementations demonstrate the viability of the proposed solution in practice. Moving forward, PIDINST will further catalyse adoption and consolidate the schema by addressing new stakeholder requirements.
An AI-based open recommender system for personalized labor market driven education
Attaining those skills that match labor market demand is getting increasingly complicated, not in the last place in engineering education, as prerequisite knowledge, skills, and abilities are evolving dynamically through an uncontrollable and seemingly unpredictable process. Anticipating and addressing such dynamism is a fundamental challenge to twenty-first century education. The burgeoning availability of data, not only on the demand side but also on the supply side (in the form of open educational resources) coupled with smart technologies, may provide a fertile ground for addressing this challenge. In this paper, we propose a novel, Artificial Intelligence (AI) driven approach to the development of an open, personalized, and labor market oriented learning recommender system, called eDoer. We discuss the complete system development cycle starting with a systematic user requirements gathering, and followed by system design, implementation, and validation. Our recommender prototype (1) derives the skill requirements for particular occupations through an analysis of online job vacancy announcements
Leben und Werk des Karl Hahn
Von 1905 bis zu seinem Lebensende 1946 stellte der Musiklehrer und Botaniker Karl Hahn eine umfangreiche Sammlung der Mecklenburger Flora insbesondere der Moose in der Umgebung von Neukloster und Grabow zusammen. Neben den noch vorhandenen Belegen hat er diverse Veröffentlichungen im Archiv der Freunde der Naturgeschichte in Mecklenburg hinterlassen, die neben der Beschreibung der einzelnen Funde auch Wanderbeschreibungen und Naturbeobachtungen thematisierten. In diesem Beitrag werden alle von ihm als „Neu für Mecklenburg“ bezeichneten Moosarten erstmals in einer Veröffentlichung zusammengetragen sowie Anregungen für zukünftige Arbeiten gegeben.