{"id":234284,"date":"2024-02-29T14:44:21","date_gmt":"2024-02-29T14:44:21","guid":{"rendered":"https:\/\/covid-ai.uni-koblenz.de\/?page_id=234284"},"modified":"2024-03-27T12:22:27","modified_gmt":"2024-03-27T12:22:27","slug":"abschlussveranstaltung","status":"publish","type":"page","link":"https:\/\/covid-ai.uni-koblenz.de\/?page_id=234284","title":{"rendered":"Abschlussveranstaltung (DE)"},"content":{"rendered":"

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background_layout__hover_enabled=”light”]<\/p>\n

KI und COVID<\/strong><\/h1>\n

<\/strong><\/p>\n

<\/strong><\/p>\n

Erkl\u00e4rbarkeit und Entscheidungsunterst\u00fctzung <\/strong><\/span><\/p>\n

durch KI in Pandemie-Situationen<\/strong><\/span><\/p>\n

<\/strong><\/span><\/p>\n

<\/strong><\/span><\/p>\n

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Motivation & Info<\/p>\n

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Programm<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.24.0″ text_font=”|700||on|||||” text_font_size=”15px” text_letter_spacing=”2px” header_2_font=”||||||||” background_color=”#0a243e” text_orientation=”center” background_layout=”dark” custom_margin=”10px||” custom_padding=”30px|20px|30px|20px|true|true” link_option_url=”#forschende” border_radii=”||||” locked=”off” global_colors_info=”{}” custom_padding__hover=”35px||35px||true” custom_padding__hover_enabled=”on” custom_margin__hover=”0px||” custom_margin__hover_enabled=”on” box_shadow_style__hover=”none” box_shadow_style__hover_enabled=”none” box_shadow_color__hover=”rgba(0,0,0,0.3)” box_shadow_color__hover_enabled=”rgba(0,0,0,0.3)” border_radii__hover_enabled=”on” border_radii__hover=”|10px|10px||”]<\/p>\n

Keynote<\/p>\n

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Abschlussveranstaltung
\u201eKI und COVID: Erkl\u00e4rbarkeit und Entscheidungsunterst\u00fctzung durch KI in Pandemie-Situationen\u201c<\/strong><\/span><\/h1>\n

[\/et_pb_text][et_pb_divider color=”#FFBA5A” divider_weight=”3px” _builder_version=”4.16″ max_width=”80px” height=”3px” global_colors_info=”{}”]
\n[\/et_pb_divider][et_pb_text _builder_version=”4.24.0″ text_font=”||||||||” text_font_size=”16px” text_line_height=”1.8em” header_2_font=”||||||||” min_height=”517.2px” custom_margin=”||0px||false|false” custom_padding=”||0px||false|false” text_font_size_tablet=”14px” text_font_size_phone=”” text_font_size_last_edited=”on|tablet” global_colors_info=”{}”]<\/p>\n

Auch wenn die SARS-CoV-2-Pandemie zur\u00fcckgeht, bleibt die datenbasierte und KI-gest\u00fctzte Forschung zur Wirksamkeit globaler und regionaler Ma\u00dfnahmen und Handlungsstrategien zur Bek\u00e4mpfung von Pandemien wichtig. Die Fortschritte in der Digitalisierung bieten ein breit gef\u00e4chertes Portfolio an datengetriebenen Unterst\u00fctzungsmethoden und -werkzeugen bei der Auswahl und Steuerung geeigneter Ma\u00dfnahmen zur Eind\u00e4mmung einer Pandemie. Anwendungen der K\u00fcnstlichen Intelligenz (KI), insbesondere des maschinellen Lernens und von Deep Learning, haben sich in verschiedenen Kontexten der Epidemiologie und der medizinischen Bildgebung als vielversprechende Ans\u00e4tze auch bei der Bek\u00e4mpfung der SARS-CoV-2-Pandemie erwiesen.<\/span><\/p>\n

Das Ministerium f\u00fcr Wissenschaft und Gesundheit (MWG) des Landes Rheinland-Pfalz f\u00f6rderte das Projekt \u201eKI und COVID: Erkl\u00e4rbarkeit und Entscheidungsunterst\u00fctzung durch KI in Pandemie-Situationen<\/em>\u201c (KI&COVID) an der Universit\u00e4t Koblenz. KI&COVID hatte zum Ziel, innovative KI-basierte Technologien und Datenanalyseverfahren zu erforschen und zu einem KI-basierten Expertensystem zu aggregieren, um relevante Akteure und Entscheidungstr\u00e4ger bei pandemiebedingten Herausforderungen \u2013 wie z.B. zur Verringerung des COVID-19 Infektionsrisikos in der Bev\u00f6lkerung \u2013 zu unterst\u00fctzen.<\/span><\/p>\n

Die Forschenden aus dem Fachbereich 3: Mathematik und Naturwissenschaften und dem Fachbereich 4: Informatik haben mittels verschiedener datengetriebener und KI-basierter Ans\u00e4tze unterschiedliche Ma\u00dfnahmen und Handlungsempfehlungen f\u00fcr die Pandemiebek\u00e4mpfung (sowohl auf Mikro- als auch auf Makroebene) untersucht und L\u00f6sungsans\u00e4tze entwickelt.<\/span><\/p>\n

Zum Projektabschluss wurden die Ergebnisse am 14. M\u00e4rz 2024 in einer \u00f6ffentlichen Abschlussveranstaltung an der Universit\u00e4t Koblenz vorgestellt.<\/span><\/p>\n

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Programm
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14:00 Begr\u00fc\u00dfung<\/strong> durch die Hochschulleitung und die Projektleitung der Universit\u00e4t Koblenz<\/p>\n

14:15 Keynote<\/strong> zur KI-basierten und datengetriebenen Analyse von Pandemiedaten, Dr. Sobhan Moazemi, Fraunhofer-Institute for Algorithms and Scientific Computing (SCAI), inkl. Diskussion, Download der Pr\u00e4sentationsfolien<\/a><\/p>\n

15:00 Pr\u00e4sentation des \u201eCOVID und KI\u201c Projekts<\/strong> und seiner zentralen Bausteine durch die Sprecherin, Prof. Dr. Maria A. Wimmer, Download der Pr\u00e4sentationsfolien<\/a><\/p>\n

15:15 Pause<\/em><\/p>\n

15:30 Poster Session mit Ergebnispr\u00e4sentationen<\/strong> der Arbeiten durch die Post-Docs<\/p>\n

Dr. Shayan Ahmadian: A secure and privacy-friendly reference architecture for COVID-analytic systems<\/strong>, Download der Pr\u00e4sentationsfolien<\/a><\/p>\n

Moritz Sch\u00e4fer: Optimal control of the spread of diseases: An integro-differential model approach <\/strong>und SIR-model for households<\/strong>, Download der Pr\u00e4sentationsfolien<\/a><\/p>\n

Dr. Radomir Pestow: The Impact on Well-Being of Cognitive Bias about Infectious Diseases<\/strong>, Download der Pr\u00e4sentationsfolien<\/a><\/p>\n

Dr. Radomir Pestow, Dr. Ulf Lotzmann und weitere Beteiligte: Erkenntnisse aus der empirischen Erhebung zu COVID an der Universit\u00e4t Koblenz im Sommer 2023<\/strong>, Download der Pr\u00e4sentationsfolien<\/a><\/p>\n

Dr. Ulf Lotzmann und weitere Beteiligte: From COVID data to recommendations: \u000bExamples of visualisation approaches<\/strong>, Download der Pr\u00e4sentationsfolien<\/a><\/strong><\/p>\n

Dr. Hamed Khalili, Prof. Dr. Maria A. Wimmer und Dr. Ulf Lotzmann: Analyzing European countries <\/b>SARS-COV-2 policies<\/b>, Download der Pr\u00e4sentationsfolien<\/a><\/p>\n

17:15 Abschluss und Networking<\/strong> an den Postern<\/p>\n

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Keynote
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\n[\/et_pb_divider][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.23.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.23.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.24.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n

Titel:<\/strong><\/span> <\/strong>KI-basierten und datengetriebenen Analyse von Pandemiedaten<\/p>\n

Vortragender:<\/strong><\/span> Dr. Sobhan Moazemi, Fraunhofer-Institute for Algorithms and Scientific Computing (SCAI)<\/p>\n

Abstract:<\/strong> <\/span>The recent COVID-19 pandemic has highlighted the urgent need for innovative solutions to combat such global health emergencies. Artificial intelligence (AI) has emerged as a powerful tool in this fight, offering unique capabilities for analyzing vast amounts of data, identifying patterns, and making predictions that can inform critical decisions.<\/p>\n

Our research focused on utilizing these capabilities of AI in the fight against COVID-19. We developed an AI model predicting patient mortality with high accuracy and identified dementia-related pathways involved in severe cases. We showed that molecular mechanisms related to dementia, one of the relevant predictors in our model, intersect with those associated to COVID-19. Most notably, among these molecules was tyrosine kinase 2 (TYK2), a protein that has been patented as drug target in Alzheimer’s Disease but also genetically associated with severe COVID-19 outcomes. This led us to explore existing drugs targeting these pathways, discovering potential treatments with existing anti-cancer drugs.<\/p>\n

Furthermore, the World Health Organization (WHO) emphasized the critical need for new medications alongside vaccines to combat the COVID-19 pandemic. One promising strategy involves identifying target proteins that could be effectively disrupted by existing drugs, potentially offering rapid therapeutic options for COVID-19 patients. One possible strategy is to identify target proteins, for which a perturbation by an existing compound is likely to benefit COVID-19 patients. To this end, we created a web tool, namely, GuiltyTargets-COVID-19, to identify novel drug targets for COVID-19. This tool analyzes gene expression and protein interaction data to prioritize potential drug targets and assess their druggability, considering potential side effects of existing drugs targeting them.<\/p>\n

Lastly, machine learning (ML) models hold promise for easing the burden on healthcare systems during pandemics such as COVID-19. They can analyze routinely collected electronic health records (EHRs) to identify patients at high risk of severe disease progression. However, EHR data presents challenges due to irregular timestamps. As another ML-based solution, we introduced ExMedBERT, utilizing transformer-based models, extending the Med-BERT model to incorporate additional patient information and improve risk prediction for COVID-19 patients. This model outperformed other models and provided insights into the risk factors.<\/p>\n

In conclusion, these studies showcase the diverse applications of AI and ML in the fight against COVID-19, ranging from patient risk prediction and drug discovery to repurposing existing medications.<\/p>\n

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Short Bio:<\/strong> Dr. rer. nat. Sobhan Moazemi is Senior Biomedical Data Scienctist at the Fraunhofer-Institute for Algorithms and Scientific Computing (SCAI) AI & Data Science Group, Department of Bioinformatics in Sankt Augustin<\/a>. With a background in data science and medical AI, Dr. Sobhan Moazemi joined Fraunhofer SCAI as a Senior Postdoc Biomedical Data Scientist in October 2023. His research mainly contributed to the applications of AI and ML in personalized medicine. In his current role, he is responsible for project management and conducting applied research in generative AI and synthetic health data.<\/p>\n

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KI und COVID Erkl\u00e4rbarkeit und Entscheidungsunterst\u00fctzung durch KI in Pandemie-Situationen Motivation & InfoProgrammKeynoteAbschlussveranstaltung \u201eKI und COVID: Erkl\u00e4rbarkeit und Entscheidungsunterst\u00fctzung durch KI in Pandemie-Situationen\u201c Auch wenn die SARS-CoV-2-Pandemie zur\u00fcckgeht, bleibt die datenbasierte und KI-gest\u00fctzte Forschung zur Wirksamkeit globaler und regionaler Ma\u00dfnahmen und Handlungsstrategien zur Bek\u00e4mpfung von Pandemien wichtig. Die Fortschritte in der Digitalisierung bieten ein breit […]<\/p>\n","protected":false},"author":4,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"class_list":["post-234284","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/covid-ai.uni-koblenz.de\/index.php?rest_route=\/wp\/v2\/pages\/234284","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/covid-ai.uni-koblenz.de\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/covid-ai.uni-koblenz.de\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/covid-ai.uni-koblenz.de\/index.php?rest_route=\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/covid-ai.uni-koblenz.de\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=234284"}],"version-history":[{"count":18,"href":"https:\/\/covid-ai.uni-koblenz.de\/index.php?rest_route=\/wp\/v2\/pages\/234284\/revisions"}],"predecessor-version":[{"id":234589,"href":"https:\/\/covid-ai.uni-koblenz.de\/index.php?rest_route=\/wp\/v2\/pages\/234284\/revisions\/234589"}],"wp:attachment":[{"href":"https:\/\/covid-ai.uni-koblenz.de\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=234284"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}