Artificial Intelligence Innovation Center for Health (CIIA-Health)

• Grant number: 20/09866-4
• Support Opportunities: Research Grants - Research Centers in Engineering Program
• Start date: August 01, 2023
• End date: July 31, 2028
• Field of knowledge: Interdisciplinary Subjects
• Agreement: MCTI/MC
• Principal Investigator: Virgilio Augusto Fernandes Almeida
• Grantee: Virgilio Augusto Fernandes Almeida
• Host Institution: Instituto de Ciências Exatas (ICEx). Universidade Federal de Minas Gerais (UFMG). Ministério da Educação (Brasil). Belo Horizonte , SP, Brazil
• City: Belo Horizonte
• Partner institutions: UNIDADE NAO MAPEADA
• Pesquisadores principais:( Atuais ): Altigran Soares da Silva ; Ana Paula Couto da Silva ; Antonio Luiz Pinho Ribeiro ; Carisi Anne Polanczyk ; Carla Maria Dal Sasso Freitas ; Deborah Carvalho Malta ; Gilberto Medeiros Ribeiro ; Gisele Lobo Pappa ; Jaime Arturo Ramírez ; Jussara Marques de Almeida ; Marco Aurelio Romano Silva ; Marcos André Gonçalves ; Mariangela Leal Cherchiglia ; Mario Fernando Montenegro Campos ; Mauro Martins Teixeira ; Philippe Olivier Alexandre Navaux ; Wagner Meira Junior ; Walmir Matos Caminhas ; Zilma Silveira Nogueira Reis
• Pesquisadores principais:( Anteriores ): Cesar Gomes Victora
• Associated researchers: Adriano Alonso Veloso ; Adriano Cesar Machado Pereira ; Airton Tetelbom Stein ; Alexandre Salles da Cunha ; Alipio Monteiro Barbosa ; Aluisio Jardim Dornellas de Barros ; Ana Beatriz Gorini da Veiga ; Ana Cristina Simões e Silva ; Ana Lúcia Cetertich Bazzan ; Ana Luiza Silva Maia ; Ana Paula Couto da Silva ; Anderson Maciel ; André Grahl Pereira ; André Luiz da Costa Carvalho ; Anisio Mendes Lacerda ; Antonio Alfredo Ferreira Loureiro ; Antonio de Padua Braga ; Augusto Afonso Guerra Júnior ; Augusto Buchweitz ; Beatriz D'Agord Schaan ; Bernardo Lanza Queiroz ; Bruce Bartholow Duncan ; Bruno Hochhegger ; Cassio Maldonado Turra ; Claudio Rosito Jung ; Daniel Fernandes Macedo ; Dante Augusto Couto Barone ; Debora Marques de Miranda ; Dorgival Olavo Guedes Neto ; Duncan Dubugras Alcoba Ruiz ; Edleno Silva de Moura ; Eduardo Martín Tarazona Santos ; Eduardo Mazoni Andrade Marçal Mendes ; Eliana de Faria Garcia Horta ; Erickson Rangel do Nascimento ; Fabiola Guerra Nakamura ; Fabrício Benevenuto de Souza ; Felipe Gomes Naveca ; Felipe Rech Meneguzzi ; Fernanda Guilhon Simplicio ; Fernando Marcos dos Reis ; Frederico Gadelha Guimaraes ; Frederico Gualberto Ferreira Coelho ; George Luiz Medeiros Teodoro ; Gisele Lobo Pappa ; Gisely Cardoso de Melo ; Guilherme Vianna Raffo ; Gustavo Medeiros Freitas ; Hector Henrique Ferreira Koolen ; Heitor Soares Ramos Filho ; Humberto Correa da Silva Filho ; Italo Fernando Scotá Cunha ; João Luiz Dihl Comba ; João Trindade Marques ; Jorge Gustavo Velasquez Melendez ; José Marcos Silva Nogueira ; Juliana Silva Herbert ; Karen Cristine Abrão ; Karin Becker ; Kenya Valeria Micaela de Souza Noronha ; Leandro Malard Moreira ; Leonardo Barbosa e Oliveira ; Lester de Abreu Faria ; Lisandro Zambenedetti Granville ; LUCIANA SALETE BURIOL ; Luciano Cunha de Araújo Pimenta ; Luiz Armando Cunha de Marco ; Luiz Chaimowicz ; Luiz Filipe Menezes Vieira ; Luiz Gonzaga Vaz Coelho ; Luiz Gustavo de Oliveira Lopes Cançado ; Manuel Menezes de Oliveira Neto ; Marcio Sarroglia Pinho ; Marcos Augusto Menezes Vieira ; Marcus Vinícius Guimarães de Lacerda ; Maria Inês Schmidt ; Mariana Recamonde Mendoza Guerreiro ; Mariangela Leal Cherchiglia ; Moises Gomes de Carvalho ; Mônica Viegas Andrade ; Omar Paranaiba Vilela Neto ; Patricia Ashton Prolla ; Paulo Roberto Ferreira Junior ; Rafael Giusti ; Rafael Heitor Bordini ; Rafael Prikladnicki ; Raquel Oliveira Prates ; Reinaldo Martinez Palhares ; Renata Vieira ; Renato Antonio Celso Ferreira ; Ricardo Hiroshi Caldeira Takahashi ; Ricardo Matsumura de Araujo ; Rodrigo Coelho Barros ; Rodrigo da Rosa Righi ; Rodrygo Luis Teodoro Santos ; Rosa Maria Vicari ; Sandhi Maria Barreto ; Sandro Jose Rigo ; Silvio Cesar Cazella ; Soraia Raupp Musse ; Stefanie Costa Pinto Lopes ; Tayana Uchôa Conte ; Vanderson de Souza Sampaio ; Viviane Pereira Moreira ; Wuelton Marcelo Monteiro
• Associated scholarship(s): 25/07691-6 - Monitoring of high-complexity cardiovascular care indicators based on continuous automated analysis of electronic medical records, BP.IC; 25/01959-7 - Predicting Clinical and Economic Outcomes Using Multimodal Semantic Representations of Patients Resilient to Temporal Drifts., BP.TT; 25/00335-0 - Machine learning-derived algorithm in the identification of referable diabetic retinopathy: A multicenter study with cost-effectiveness analysis, BP.IC; + associated scholarships 25/02141-8 - THE USE OF ARTIFICIAL INTELLIGENCE PREDICTIVE MODELS IN THE PREVENTION OF CHRONIC NON-COMMUNICABLE DISEASES, BP.IC; 25/00133-8 - Prediction of Clinical and Economic Outcomes Through Multimodal Semantic Representations of Patients Resilient to Temporal Drifts, BP.TT; 24/22266-7 - Artificial Intelligence Platform for the research and development of multi-target drugs for the treatment of Neglected Diseases, BP.TT; 24/20690-6 - Automated Interpretation of Pap Smear Tests for Cervical Cancer Screening, BP.PD; 24/15693-6 - The Use of Artificial Intelligence Predictive Models in the Prevention of Chronic Non-Communicable Diseases, BP.TT; 24/14836-8 - Artificial Intelligence platform for the research and development of multi-target drugs for the treatment of Neglected Diseases, BP.PD; 24/14589-0 - Data Governance and Artificial Intelligence Models in Healthcare: Structuring, Implementation, and Evaluation of Accountability Policies and Practices, BP.TT; 24/14917-8 - Center for Innovation in Artificial Intelligence for Health (CIIA-Saúde) (Subproject: Study of Association and Prediction of Severe Psychiatric Outcomes in Children and Adolescents Using Artificial Intelligence), BP.IC; 24/15869-7 - Center for Innovation in Artificial Intelligence for Health (CIIA-Saúde) (Subproject: Study of Association and Prediction of Severe Psychiatric Outcomes in Children and Adolescents Using Artificial Intelligence), BP.IC; 24/16653-8 - The Use of Artificial Intelligence Predictive Models in the Prevention of Noncommunicable Chronic Diseases, BP.IC; 24/14916-1 - Center for Innovation in Artificial Intelligence for Health (CIIA-Saúde) (Subproject: Study of Association and Prediction of Severe Psychiatric Outcomes in Children and Adolescents Using Artificial Intelligence), BP.IC; 24/14918-4 - Center for Innovation in Artificial Intelligence for Health (CIIA-Saúde) (Subproject: Study of Association and Prediction of Severe Psychiatric Outcomes in Children and Adolescents Using Artificial Intelligence), BP.IC; 24/07524-0 - THE USE OF ARTIFICIAL INTELLIGENCE PREDICTIVE MODELS IN THE PREVENTION OF CHRONIC NON-COMMUNICABLE DISEASES, BP.PD; 24/07302-7 - Center for Innovation in Artificial Intelligence for Health (CIIA-Saúde) (Subproject: Prediction of Clinical and Economic Outcomes through Multimodal Semantic Representations of Patients Resilient to Temporal Drifts), BP.TT; 24/03126-0 - Prediction of Clinical and Economic Outcomes through Multimodal Semantic Representations of Patients Resilient to Temporal Drifts, BP.TT; 24/01403-6 - Monitoring of high-complexity cardiovascular care indicators based on continuous automated analysis of electronic medical records, BP.IC; 23/16539-8 - Center for Innovation in Artificial Intelligence for Health (CIIA-Saúde) - IC (Diagnosis), BP.IC - associated scholarships

Abstract

Health, defined by the World Health Organization (WHO) as a state of complete physical, mental and social well-being, and not just the absence of illness or infirmity, is a basic dimension of life, whether for individuals or for societies and nations. A complex set of policies and actions have been structured by nations and their governments, at different levels, to ensure the adequate prevention, diagnosis and treatment of diseases and to achieve individual and social well-being. The cost of the systems associated with health care represents about 10% of the world GDP and, despite the progress of the last decades, it is still challenging to diagnose and treat numerous clinical conditions, and even to ensure that everyone has access to quality health care. Artificial intelligence (AI), defined as the simulation of human intelligence in machines, includes learning, reasoning and perception, and related technologies, has been a disruptive innovation in health and medicine. The focus of the Innovation Center for Artificial Intelligence in Health (CIIA-Saúde) is the research and development of artificial intelligence (AI) techniques and solutions to enable and demonstrate their potential to assist individuals and patients in self-care, health professionals in the diagnosis and treatment of diseases and health managers to plan preventive actions and care, optimizing the use of resources and broadly improving the health of the Brazilian population. It is a multidisciplinary and multi-institutional center integrating STEM and health areas with headquarters at UFMG, in association with 9 higher education institutions in the Southeast, South and North regions of the country, and in partnership with four companies in the areas of health, technology and education. The team consists of 130 researchers, 95 of which are CNPq productivity fellows (19 are level 1A). The health areas of the center are structured as five fundamental axes that include: 1) prevention and quality of life; 2) diagnosis, prognosis and screening; 3) therapeutic and personalized medicine; 4) health systems and management; and 5) epidemics and disasters. Associated with health axes and challenges, the focus of research in AI is concentrated on the following areas: I) Ethics and Human Values, II) Models and Algorithms, III) Data Management and Engineering, and IV) Computer Systems. The expected results of CIIA-Saúde may be grouped into research, innovation and training of human resources, including the publication of scientific articles in prestigious national and international journals and conferences, the development of proof of concept and prototype solutions, together with health companies and institutions, as well as the transfer of technology and know-how to the market (with the support of CTIT-UFMG); and the training of doctors and masters. Education and knowledge dissemination actions for elementary and high school students are also planned, including short courses and educational videos, as well as a lato sensu graduate course (360h) for health professionals and short courses for the general population. The Center also plans an intense international activity, through collaboration with researchers and institutions of excellence in advanced research. CIIA-Saúde will have a lean administrative structure including a Board of Directors, International Scientific Committee, Executive Committee, Technical-Scientific Coordination and Administrative Secretariat, and will have the support of UFMG's Research and Development Foundation (Fundep) for administrative and financial management. (AU)

Scientific publications

(4)

(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)

MARTINS, PAULO; DA SILVA, ALTIGRAN SOARES; AFONSO, ARIEL; CAVALCANTI, JOAO; DE MOURA, EDLENO. Supporting Schema References in Keyword Queries Over Relational Databases. IEEE ACCESS, v. 11, p. 26-pg., 2023-01-01. (20/09866-4)

RIBEIRO, ANTONIO H.; RIBEIRO, ANTONIO LUIZ P.. AI-ECG and prediction of new atrial fibrillation: when the heart tells the age. EUROPEAN HEART JOURNAL, v. N/A, p. 3-pg., 2024-12-09. (20/09866-4)

PAIVA, BRUNO; GONCALVES, MARCOS ANDRE; DUTRA DA ROCHA, LEONARDO CHAVES; MARCOLINO, MILENA SORIANO; BARBOSA LANA, FERNANDA CRISTINA; SOUZA-SILVA, MAIRA VIANA REGO; ALMEIDA, JUSSARA M.; PEREIRA, POLIANNA DELFINO; VALIENSE DE ANDRADE, CLAUDIO MOISES; DOS REIS GOMES, ANGELICA GOMIDES; et al. A New Natural Language Processing-Inspired Methodology (Detection, Initial Characterization, and Semantic Characterization) to Investigate Temporal Shifts (Drifts) in Health Care Data: Quantitative Study. JMIR MEDICAL INFORMATICS, v. 12, p. 24-pg., 2024-01-01. (20/09866-4)

BAETA, THAIS; ROCHA, ANA LUIZA LUNARDI; OLIVEIRA, JULIANA ALMEIDA; DA SILVA, ANA PAULA COUTO; REIS, ZILMA SILVEIRA NOGUEIRA. Accuracy of machine learning and traditional statistical models in the prediction of postpartum haemorrhage: a systematic review. BMJ OPEN, v. 15, n. 3, p. 9-pg., 2025-03-03. (20/09866-4)