Data Analytics in Biomedicine
CALL FOR PAPERS
The exponential growth of data generated from various textual sources presents both a challenge and a huge opportunity. A key challenge lies in effectively managing and extracting valuable insights from this vast amount of unstructured and heterogeneous data. To address this issue, advanced data analytics techniques ranging from data and text mining to semantic network analysis, as well as the recent advancements in large language models (LLMs) have become indispensable tools for researchers and practitioners.
This is particularly relevant in the realm of biomedicine, where text mining has shown the ability to enable researchers to uncover hidden patterns, trends, and associations that would otherwise remain buried in the vast amount of health-related textual data, as for instance research articles, clinical reports, and electronic health records (EHRs).
On the other hand, semantic network analysis that focus on understanding the structure and dynamics of networks formed by the entities and their interconnections derived from text mining processes, can facilitate a deeper understanding of the complex interrelationships within biomedical data. By analyzing properties like centrality, modularity, and community structures, researchers can identify key nodes and critical pathways in biological networks, predict disease associations, and explore the functional organization of biological systems.
The integration of text mining, semantic network analysis, and large language models offers a powerful approach not only to enhance the ability to generate new hypotheses and insights but also to support the development of more effective diagnostics, treatments, and interventions
The workshop represents an opportunity to explore the latest advancements in data analytics and text mining applied to biomedicine. Attendees will gain insights into developing more interpretable models, handling large-scale biomedical datasets, and implementing scalable solutions for real-world healthcare applications.
Moreover, the workshop is highly relevant due to its potential to significantly improve the safety, effectiveness, and efficiency of biomedical interventions through advanced data analytics.
TOPIC OF INTEREST
We invite submissions on a wide range of topics, including but not limited to:
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Novel techniques and measures for assessing textual data quality and handing data integration.
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Advanced text mining techniques for biomedical data
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Construction and analysis of semantic networks in biomedicine
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Case studies on integrated text mining and semantic network analysis
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Applications of LLMs in biomedicine
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Data analytics in precision medicine
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Text-driven approaches to drug discovery
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Interpretable or scalable data analytics approaches
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Application of data analytics and network science in narrative medicine
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Computational methods for disease modeling and prediction
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Ethical considerations in biomedical data analytics
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Multimodal biomedical data analytics
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Future trends and challenges in biomedical data analytics
PROGRAM
The workshop will take place on (To Be Announced). The program is not available yet.
PAPER SUBMISSION, REGISTRATION AND PUBLICATION
Please refer to socio.org.uk/ddp/paper-submission/
IMPORTANT DATES
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Submission of Papers: August 05, 2024
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Review and Notification: August 31, 2024
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Camera-ready: Sep. 25, 2024
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Workshop Date: Oct. 01, 2024
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Post-conference proceedings: Nov. 30, 2024
WORKSHOP ORGANIZERs
Chiara Zucco, University Magna Graecia of Catanzaro, Italy
Mario Cannataro, University Magna Graecia of Catanzaro, Italy
Marianna Milano, University Magna Graecia of Catanzaro, Italy
PROGRAM COMMITTEE (TO BE CONFIRMED)
Marzia Settino, University of Calabria, Italy
Mario Cannataro, University Magna Graecia of Catanzaro, Italy
Maria Chiara Martinis, University Magna Graecia of Catanzaro, Italy
Giuseppe Agapito, University Magna Graecia of Catanzaro, Italy
Pietro Cinaglia, University Magna Graecia of Catanzaro, Italy
Ilaria Lazzaro, University Magna Graecia of Catanzaro, Italy