Analyzing complex data from COVID-19 to support decision making and prognosis

• Grant number: 20/07200-9
• Support Opportunities: Regular Research Grants
• Duration: July 01, 2020 - March 31, 2023
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Agma Juci Machado Traina
• Grantee: Agma Juci Machado Traina
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Associated researchers: Caetano Traina Junior ; Diego Armando Cardona Cardenas ; Elaine Parros Machado de Sousa ; José Fernando Rodrigues Júnior ; José Raniery Ferreira Junior ; Leticia Rittner ; Marcel Koenigkam Santos ; Marco Antonio Gutierrez ; Paulo Mazzoncini de Azevedo Marques ; Roberto de Alencar Lotufo ; Robson Leonardo Ferreira Cordeiro
• Associated research grant: 16/17078-0 - Mining, indexing and visualizing Big Data in clinical decision support systems (MIVisBD), AP.TEM
• Associated scholarship(s): 20/12528-3 - Database development and support for machine learning aimed at radiomic studies for COVID-19, BP.TT; 20/14180-4 - Development of radiographic quantitative markers for Precision Medicine regarding acute respiratory syndrome, BP.PD; 20/10902-5 - Handling similarity queries over incomplete data in a Relational DBMS, BP.TT

Abstract

This supplementary research project aims at proposing, exploring and developing new methods and algorithms to be used in decision-making processes for medical diagnosis and prognosis of patients in the context of COVID-19. These methods and algorithms will be instantiated in systems and applications that will be made available to the scientific community to support this decision-making process quickly and accurately. The challenges to be overcome begin with the assembly of databases and images from different and often incomplete platforms, and the application of the radiomic technique on X-ray (RX) and computed tomography (CT) images, with the premise of that massive quantitative and qualitative characteristics on X-ray images can bring the necessary information for the diagnosis of COVID-19, in the same way that CT provides. The advantage of this approach that will be investigated is the lower cost and greater availability of X-rays, allowing more patients to benefit from the results of this proposal. In addition to providing cured and consistent material for research and advances in the area of COVID-19. In addition, cured databases and images for research will be made available to the community of the area. (AU)

Scientific publications

(6)

(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)

ALVES, MATHEUS A. C.; CORDEIRO, ROBSON L. F.. Effective and unburdensome forecast of highway traffic flow with adaptive computing. KNOWLEDGE-BASED SYSTEMS, v. 212, JAN 5 2021. (20/07200-9, 18/05714-5, 16/17078-0)

GIUNTINI, FELIPE TALIAR; DE MORAES, KAUE L.; CAZZOLATO, MIRELA T.; KIRCHNER, LUZIANE DE FATIMA; DOS REIS, MARIA DE JESUS D.; TRAINA, AGMA J. M.; CAMPBELL, ANDREW T.; UEYAMA, JO. Modeling and Assessing the Temporal Behavior of Emotional and Depressive User Interactions on Social Networks. IEEE ACCESS, v. 9, p. 93182-93194, 2021. (13/07375-0, 20/11258-2, 18/24414-2, 18/17335-9, 16/17078-0, 20/07200-9)

GIUNTINI, FELIPE TALIAR; DE MORAES, KAUE L. P.; CAZZOLATO, MIRELA T.; KIRCHNER, LUZIANE DE FATIMA; DOS REIS, MARIA DE JESUS D.; TRAINA, AGMA J. M.; CAMPBELL, ANDREW T.; UEYAMA, JO. Tracing the Emotional Roadmap of Depressive Users on Social Media Through Sequential Pattern Mining. IEEE ACCESS, v. 9, p. 97621-97635, 2021. (18/24414-2, 20/07200-9, 13/07375-0, 16/17078-0, 20/11258-2, 18/17335-9)

BRANDOLI, BRUNO; DE GEUS, ANDRE R.; SOUZA, JEFFERSON R.; SPADON, GABRIEL; SOARES, AMILCAR; RODRIGUES, JR., JOSE F.; KOMOROWSKI, JERZY; MATWIN, STAN. Aircraft Fuselage Corrosion Detection Using Artificial Intelligence. SENSORS, v. 21, n. 12, JUN 2021. (17/08376-0, 18/17620-5, 19/04461-9, 20/07200-9, 16/17078-0, 14/25337-0)

CAZZOLATO, MIRELA T.; RAMOS, JONATHAN S.; RODRIGUES, LUCAS S.; SCABORA, LUCAS C.; CHINO, DANIEL Y. T.; JORGE, ANA E. S.; DE AZEVEDO-MARQUES, PAULO MAZZONCINI; JR, CAETANO TRAINA; TRAINA, AGMA J. M.. The UTrack framework for segmenting and measuring dermatological ulcers through telemedicine. COMPUTERS IN BIOLOGY AND MEDICINE, v. 134, JUL 2021. (18/24414-2, 16/17078-0, 16/17330-1, 20/07200-9, 20/11258-2, 20/10902-5)

CAZZOLATO, MIRELA T.; TRAINA, AGMA J. M.; BOHM, KLEMENS. Establishing trajectories of moving objects without identities: The intricacies of cell tracking and a solution. INFORMATION SYSTEMS, v. 105, MAR 2022. (20/11258-2, 16/17078-0, 20/07200-9, 18/24414-2)