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- ItemDigitalisierung in den Gesundheitsberufen([Leverkusen] : Verlag Barbara Budrich, 2024) Weyland, Ulrike; Koschel, Wilhelm; Reiber, Karin; Dorin, Lena; Peters, Miriam; Arndt, Laura; Behr, Dominik; Bergmann, Dana; Buchmann, Ulrike; Ebbighausen, Marc; Engl, Anna-Teresa; Ettl, Katrin; Fathi, Madjid; Fischer, Andreas; Freese, Christiane; Haussmann, Andreas; Hiestand, Stefanie; Hofstetter, Sebastian; Hüttner, Aneli; Jahn, Patrick; Jürgensen, Anke; Kaiser, Sophie; Kaufhold, Marisa; Kismihók, Gábor; Klus, Christina; Kobus, Julia; Köhler, Sonja; Kraft, Bernhard; Makowsky, Katja; Meng, Michael; Michel, Natalie; Nagel, Lisa; Nauerth, Annette; Nerdel, Claudia; Paulicke, Denny; Preißler, Ronja; Rasheed, Hasan A.; Rechl, Friederike; Richter, Katja E.; Richter, Patrick; Schröder, Martina; Schröer, Laura; Schwarz, Karsten; Seltrecht, Astrid; Steindorff, Jenny-Victoria; Stirner, Alexander; Stoevesandt, Dietrich; Völz, Silke; Wagner-Herrbach, Cornelia; Weber, Christian; Wittmann, Eveline; Zepelin, Lyn Anne von; Ziegler, Sven; Zilezinski, MaxDigitale Technologien führen zu veränderten Kommunikations-, Lern- und Arbeitsformen. Für die Gesundheitsberufe ergeben sich durch die Digitalisierung vielfältige Veränderungen und Herausforderungen, die bei positiver Wendung auch als Chance verstanden werden können. Wenn Digitalisierungsprozesse in den Gesundheitsberufen aktiv durch die Berufsgruppen mitgestaltet werden, so können positive Ansätze für die Versorgung hilfs- und pflegebedürftiger Menschen entwickelt werden, aber ebenso für die Professionalisierung der Fachkräfte und des beruflichen Bildungspersonals. Dieser Band dokumentiert die Beiträge zum AG-BFN-Forum „Digitalisierung in den Gesundheitsberufen“, das im Oktober 2021 an der Universität Münster stattfand. Im Fokus stehen aktuelle Entwicklungen in den Bereichen Digitalität in pflege- und gesundheitsberuflichen Handlungsfeldern, Professionalisierung des Bildungspersonals und digital gestützte Lehr-/Lernszenarien in den Gesundheitsberufen.
- ItemTraditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)-Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study(Toronto : [Verlag nicht ermittelbar], 2022) Benítez-Andrades, José Alberto; Alija-Pérez, José-Manuel; Vidal, Maria-Esther; Pastor-Vargas, Rafael; García-Ordás, María TeresaBackground: Eating disorders affect an increasing number of people. Social networks provide information that can help. Objective: We aimed to find machine learning models capable of efficiently categorizing tweets about eating disorders domain. Methods: We collected tweets related to eating disorders, for 3 consecutive months. After preprocessing, a subset of 2000 tweets was labeled: (1) messages written by people suffering from eating disorders or not, (2) messages promoting suffering from eating disorders or not, (3) informative messages or not, and (4) scientific or nonscientific messages. Traditional machine learning and deep learning models were used to classify tweets. We evaluated accuracy, F1 score, and computational time for each model. Results: A total of 1,058,957 tweets related to eating disorders were collected. were obtained in the 4 categorizations, with The bidirectional encoder representations from transformer-based models had the best score among the machine learning and deep learning techniques applied to the 4 categorization tasks (F1 scores 71.1%-86.4%). Conclusions: Bidirectional encoder representations from transformer-based models have better performance, although their computational cost is significantly higher than those of traditional techniques, in classifying eating disorder-related tweets.
- ItemAn Artificial Intelligence-Based Tool for Data Analysis and Prognosis in Cancer Patients: Results from the Clarify Study(Basel : MDPI, 2022) Torrente, María; Sousa, Pedro A.; Hernández, Roberto; Blanco, Mariola; Calvo, Virginia; Collazo, Ana; Guerreiro, Gracinda R.; Núñez, Beatriz; Pimentao, Joao; Sánchez, Juan Cristóbal; Campos, Manuel; Costabello, Luca; Novacek, Vit; Menasalvas, Ernestina; Vidal, María Esther; Provencio, MarianoBackground: Artificial intelligence (AI) has contributed substantially in recent years to the resolution of different biomedical problems, including cancer. However, AI tools with significant and widespread impact in oncology remain scarce. The goal of this study is to present an AI-based solution tool for cancer patients data analysis that assists clinicians in identifying the clinical factors associated with poor prognosis, relapse and survival, and to develop a prognostic model that stratifies patients by risk. Materials and Methods: We used clinical data from 5275 patients diagnosed with non-small cell lung cancer, breast cancer, and non-Hodgkin lymphoma at Hospital Universitario Puerta de Hierro-Majadahonda. Accessible clinical parameters measured with a wearable device and quality of life questionnaires data were also collected. Results: Using an AI-tool, data from 5275 cancer patients were analyzed, integrating clinical data, questionnaires data, and data collected from wearable devices. Descriptive analyses were performed in order to explore the patients’ characteristics, survival probabilities were calculated, and a prognostic model identified low and high-risk profile patients. Conclusion: Overall, the reconstruction of the population’s risk profile for the cancer-specific predictive model was achieved and proved useful in clinical practice using artificial intelligence. It has potential application in clinical settings to improve risk stratification, early detection, and surveillance management of cancer patients.
- ItemNon Thermal Plasma Sources of Production of Active Species for Biomedical Uses: Analyses, Optimization and Prospect(London : IntechOpen, 2011) Yousfi, M.; Merbahi, N.; Sarrette, P. J.; Eichwald, O.; Ricard, A.; Gardou, J.P.; Ducasse, O.; Benhenni, M.; Fazel-Rezai, Reza[no abstract available]
- ItemThe systems biology format converter(London : BioMed Central, 2016) Rodriguez, Nicolas; Pettit, Jean-Baptiste; Dalle Pezze, Piero; Li, Lu; Henry, Arnaud; van Iersel, Martijn P.; Jalowicki, Gael; Kutmon, Martina; Natarajan, Kedar N.; Tolnay, David; Stefan, Melanie I.; Evelo, Chris T.; Le Novère, NicolasBackground: Interoperability between formats is a recurring problem in systems biology research. Many tools have been developed to convert computational models from one format to another. However, they have been developed independently, resulting in redundancy of efforts and lack of synergy. Results: Here we present the System Biology Format Converter (SBFC), which provide a generic framework to potentially convert any format into another. The framework currently includes several converters translating between the following formats: SBML, BioPAX, SBGN-ML, Matlab, Octave, XPP, GPML, Dot, MDL and APM. This software is written in Java and can be used as a standalone executable or web service. Conclusions: The SBFC framework is an evolving software project. Existing converters can be used and improved, and new converters can be easily added, making SBFC useful to both modellers and developers. The source code and documentation of the framework are freely available from the project web site.