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search.filters.applied.f.access: openAccess × End date: 2024 × Start date: 2020 × DDC: 600 × Version: publishedVersion × WGL Institute: TIB ×

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Now showing 1 - 4 of 4
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    Calibrating mini-mental state examination scores to predict misdiagnosed dementia patients
    (Basel : MDPI, 2021) Vyas, Akhilesh; Aisopos, Fotis; Vidal, Maria-Esther; Garrard, Peter; Paliouras, George
    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.
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    Creation of a Knowledge Space by Semantically Linking Data Repository and Knowledge Management System - a Use Case from Production Engineering
    (Laxenburg : IFAC, 2022) Sheveleva, Tatyana; Wawer, Max Leo; Oladazimi, Pooya; Koepler, Oliver; Nürnberger, Florian; Lachmayer, Roland; Auer, Sören; Mozgova, Iryna
    The seamless documentation of research data flows from generation, processing, analysis, publication, and reuse is of utmost importance when dealing with large amounts of data. Semantic linking of process documentation and gathered data creates a knowledge space enabling the discovery of relations between steps of process chains. This paper shows the design of two systems for data deposit and for process documentation using semantic annotations and linking on a use case of a process chain step of the Tailored Forming Technology.
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    Author Correction: Replication and Refinement of an Algorithm for Automated Drusen Segmentation on Optical Coherence Tomography (Scientific Reports, (2020), 10, 1, (7395), 10.1038/s41598-020-63924-6)
    ([London] : Macmillan Publishers Limited, part of Springer Nature, 2021) Wintergerst, Maximilian W. M.; Gorgi Zadeh, Shekoufeh; Wiens, Vitalis; Thiele, Sarah; Schmitz-Valckenberg, Steffen; Holz, Frank G.; Finger, Robert P.; Schultz, Thomas
    Correction to: Scientific Reports https://doi.org/10.1038/s41598-020-63924-6, published online 30 April 2020
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    The SciQA Scientific Question Answering Benchmark for Scholarly Knowledge
    (London : Nature Publishing Group, 2023) Auer, Sören; Barone, Dante A.C.; Bartz, Cassiano; Cortes, Eduardo G.; Jaradeh, Mohamad Yaser; Karras, Oliver; Koubarakis, Manolis; Mouromtsev, Dmitry; Pliukhin, Dmitrii; Radyush, Daniil; Shilin, Ivan; Stocker, Markus; Tsalapati, Eleni
    Knowledge graphs have gained increasing popularity in the last decade in science and technology. However, knowledge graphs are currently relatively simple to moderate semantic structures that are mainly a collection of factual statements. Question answering (QA) benchmarks and systems were so far mainly geared towards encyclopedic knowledge graphs such as DBpedia and Wikidata. We present SciQA a scientific QA benchmark for scholarly knowledge. The benchmark leverages the Open Research Knowledge Graph (ORKG) which includes almost 170,000 resources describing research contributions of almost 15,000 scholarly articles from 709 research fields. Following a bottom-up methodology, we first manually developed a set of 100 complex questions that can be answered using this knowledge graph. Furthermore, we devised eight question templates with which we automatically generated further 2465 questions, that can also be answered with the ORKG. The questions cover a range of research fields and question types and are translated into corresponding SPARQL queries over the ORKG. Based on two preliminary evaluations, we show that the resulting SciQA benchmark represents a challenging task for next-generation QA systems. This task is part of the open competitions at the 22nd International Semantic Web Conference 2023 as the Scholarly Question Answering over Linked Data (QALD) Challenge.