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Mining multimodal records: explainable patterns and anomalies discovery

Grant number: 21/11403-5
Support Opportunities:Scholarships abroad - Research Internship - Post-doctor
Effective date (Start): February 01, 2022
Effective date (End): January 31, 2023
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal Investigator:Marco Antonio Gutierrez
Grantee:Mirela Teixeira Cazzolato
Supervisor: Christos Faloutsos
Host Institution: Instituto do Coração Professor Euryclides de Jesus Zerbini (INCOR). Hospital das Clínicas da Faculdade de Medicina da USP (HCFMUSP). Secretaria da Saúde (São Paulo - Estado). São Paulo , SP, Brazil
Research place: Carnegie Mellon University (CMU), United States  
Associated to the scholarship:20/11258-2 - Interoperability and similarity queries on medical databases, BP.PD


The discovery of anomalies and patterns is essential to many applications and areas, such as COVID-19 detection in chest X-Rays and vertebral fracture detection in MRI for health applications, as well as credit analysis in finance, and bot detection in social networks. The mining approach is application-dependent, and the discovered patterns should be shown to specialists using appropriate tools for boosting explainability and understanding, usually by taking advantage of visualization metaphors. In this internship proposal, we aim at exploring multimodal data from multiple application scenarios and types (e.g., images, graphs, electronic health records, and financial transactions) by proposing modular, scalable, and explainable methods. The internship will be carried out at Carnegie Mellon University (CMU), USA. The objectives meet the interests of both the FAPESP thematic project "Mining, Indexing and Visualizing Big Data in Clinical Decision Support Systems - (MIVisBD)" and the current research carried out at CMU by the supervisor and co-supervisor. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
RAMOS, JONATHAN S.; DE AGUIAR, ERIKSON J.; BELIZARIO, IVAR, V; COSTA, MARCUS V. L.; MACIEL, JAMILLY G.; CAZZOLATO, MIRELA T.; TRAINA, CAETANO, JR.; NOGUEIRA-BARBOSA, MARCELLO H.; TRAINA, AGMA J. M.; SHEN, L; et al. Analysis of vertebrae without fracture on spine MRI to assess bone fragility: A Comparison of Traditional Machine Learning and Deep Learning. 2022 IEEE 35TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS), v. N/A, p. 6-pg., . (21/02412-0, 20/11258-2, 16/17078-0, 21/11403-5, 21/00360-3, 21/08982-3, 18/04266-9)
CAZZOLATO, MIRELA T.; GUTIERREZ, MARCO ANTONIO; TRAINA, CACTANO, JR.; FALOUTSOS, CHRISTOS; TRAINA, AGMA J. M.; ALMEIDA, JR; SPILIOPOULOU, M; ANDRADES, JAB; PLACIDI, G; GONZALEZ, AR; et al. Exploratory Data Analysis in Electronic Health Records Graphs: Intuitive Features and Visualization Tools. 2023 IEEE 36TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS, CBMS, v. N/A, p. 6-pg., . (16/17078-0, 21/11403-5, 20/11258-2)

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