TY - JOUR A1 - D'Elia, Domenica A1 - Truu, Jaak A1 - Lahti, Leo A1 - Berland, Magali A1 - Papoutsoglou, Georgios A1 - Ceci, Michelangelo A1 - Zomer, Aldert A1 - Lopes, Marta B. A1 - Ibrahimi, Eliana A1 - Gruca, Aleksandra A1 - Nechyporenko, Alina A1 - Frohme, Marcus A1 - Klammsteiner, Thomas A1 - Carrillo de Santa Pau, Enrique A1 - Marcos-Zambrano, Laura Judith A1 - Hron, Karel A1 - Pio, Gianvito A1 - Simeon, Andrea A1 - Suharoschi, Ramona A1 - Moreno-Indias, Isabel A1 - Temko, Andriy A1 - Nedyalkova, Miroslava A1 - Apostol, Elena-Simona A1 - Truică, Ciprian-Octavian A1 - Shigdel, Rajesh A1 - Telalović, Jasminka Hasić A1 - Bongcam-Rudloff, Erik A1 - Przymus, Piotr A1 - Jordamović, Naida Babić A1 - Falquet, Laurent A1 - Tarazona, Sonia A1 - Sampri, Alexia A1 - Isola, Gaetano A1 - Pérez-Serrano, David A1 - Trajkovik, Vladimir A1 - Klucar, Lubos A1 - Loncar-Turukalo, Tatjana A1 - Havulinna, Aki S. A1 - Jansen, Christian A1 - Bertelsen, Randi J. A1 - Claesson, Marcus Joakim T1 - Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action JF - Frontiers in Microbiology N2 - The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish “gold standard” protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory ‘omics’ features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices. KW - microbiome KW - machine learning KW - artificial intelligence KW - standard KW - best practice Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-18004 SN - 1664-302X VL - 14 PB - Frontiers ER - TY - JOUR A1 - Loos, Eugène A1 - Gröpler, Johanna A1 - Goudeau, Marie-Louise Sophie T1 - Using ChatGPT in Education: Human Reflection on ChatGPT’s Self-Reflection JF - Societies N2 - ChatGPT is a fascinating AI text generator tool. It is a language model developed by OpenAI, a research and deployment company with the mission, according to OpenAI’s website: “to ensure that artificial general intelligence benefits all of humanity”. ChatGPT is able to generate human-like texts. But how does it work? What about the quality of the texts it provides? And is it capable of being self-reflective? Information sources must be efficient, effective and reliable in education, in order to enhance students’ learning process. For this reason, we started a dialogue with ChatGPT-3 while using, among others, a SWOT analysis it generated about its own functioning in an educational setting. This enabled us, as human authors, to analyze the extent to which this AI system is able to practice self-reflection. Finally, the paper sketches implications for education and future research. KW - ChatGPT KW - AI KW - text generating KW - education KW - SWOT analysis KW - self-reflection KW - hallucination KW - artificial intelligence Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-17966 SN - 2075-4698 VL - 13 IS - 8 PB - MDPI ER - TY - JOUR A1 - Langenberg, Anna A1 - Ma, Shih-Chi A1 - Ermakova, Tatiana A1 - Fabian, Benjamin T1 - Formal Group Fairness and Accuracy in Automated Decision Making JF - Mathematics N2 - Most research on fairness in Machine Learning assumes the relationship between fairness and accuracy to be a trade-off, with an increase in fairness leading to an unavoidable loss of accuracy. In this study, several approaches for fair Machine Learning are studied to experimentally analyze the relationship between accuracy and group fairness. The results indicated that group fairness and accuracy may even benefit each other, which emphasizes the importance of selecting appropriate measures for performance evaluation. This work provides a foundation for further studies on the adequate objectives of Machine Learning in the context of fair automated decision making. KW - AI KW - machine learning KW - automated decision making KW - algorithmic bias KW - metric KW - group fairness KW - artificial intelligence Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-17323 SN - 2227-7390 VL - 11 IS - 8 PB - MDPI ER - TY - JOUR A1 - Alekseeva, Victoriia A1 - Nechyporenko, Alina A1 - Frohme, Marcus A1 - Gargin, Vitaliy A1 - Meniailov, Ievgen A1 - Chumachenko, Dmytro T1 - Intelligent Decision Support System for Differential Diagnosis of Chronic Odontogenic Rhinosinusitis Based on U-Net Segmentation JF - Electronics N2 - The share of chronic odontogenic rhinosinusitis is 40% among all chronic rhinosinusitis. Using automated information systems for differential diagnosis will improve the efficiency of decision-making by doctors in diagnosing chronic odontogenic rhinosinusitis. Therefore, this study aimed to develop an intelligent decision support system for the differential diagnosis of chronic odontogenic rhinosinusitis based on computer vision methods. A dataset was collected and processed, including 162 MSCT images. A deep learning model for image segmentation was developed. A 23 convolutional layer U-Net network architecture has been used for the segmentation of multi-spiral computed tomography (MSCT) data with odontogenic maxillary sinusitis. The proposed model is implemented in such a way that each pair of repeated 3 × 3 convolutions layers is followed by an Exponential Linear Unit instead of a Rectified Linear Unit as an activation function. The model showed an accuracy of 90.09%. To develop a decision support system, an intelligent chatbot allows the user to conduct an automated patient survey and collect patient examination data from several doctors of various profiles. The intelligent information system proposed in this study made it possible to combine an image processing model with a patient interview and examination data, improving physician decision-making efficiency in the differential diagnosis of Chronic Odontogenic Rhinosinusitis. The proposed solution is the first comprehensive solution in this area. KW - CT data KW - deep learning KW - image segmentation KW - U-Net KW - decision support system KW - chronic odontogenic rhinosinusitis KW - machine learning KW - artificial intelligence Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-17088 SN - 2079-9292 VL - 12 IS - 5 PB - MDPI ER -