Comunicações em Eventos - LIM/65

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https://observatorio.fm.usp.br/handle/OPI/35789

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Agora exibindo 1 - 20 de 78

Treatment with empagliflozin improves cardiac function in infarcted animals associated with increased baroreflex sensitivity
(2023) SILVA, B. Da; NASCIMENTO-CARVALHO, B.; SOUZA, L. I. De; SILVA, M. B. Da; MARQUES, J. R.; DOURADO, P. M. M.; CONSOLIM-COLOMBO, F.; IRIGOYEN, M. C. C.
Transseptal puncture using electroanatomical mapping: a safe and cost-effective technique
(2023) BRIGIDO, A. Dantas; RASSI, G. M.; RODRIGUES, L. V.; LOVISI, V. B.; PISANI, C. F.; CHOKR, M. O.; HARDY, C. A.; MELO, S. L.; GONCALVES, A. L. M.; MAYRINK, M. P.; KULCHETSCKI, R. M.; SCANAVACCA, M. I.
A Multi-objective Physiological Control for Continuous Flow Left Ventricular Assist Devices: Comparison of Estimator versus Sensor-based Feedback
(2023) SANTOS, Bruno; CESTARI, Idagene
Left Ventricular Assist Devices have been successfully used for the treatment of Congestive Heart failure in patients who are not eligible for heart transplantation. This paper describes the implementation and comparison of the performance of a pressure sensor-based feedback controller. The strategies were tested on a mock loop of the systemic circulation. The results show that the use of pressure sensors generated a more accurate response of the controller compared to the use of estimators.
Sympathetic Neural Overdrive, Endothelial Dysfunction and Aortic Stiffness in Coronavirus Disease 2019 Survivors: A Short-Term Study of Cardiovascular Sequelae
(2021) FARIA, Diego; TESTA, Laura; MOLL-BERNARDES, Renata; MONIZ, Camila; RODRIGUES, Erika; COSTA-NETO, Abel; SOUSA, Andrea; RODRIGUES, Amanda; OLIVEIRA, Patricia; ALVES, Maria Janieire; SANTOS, Gabriel; SALEMI, Vera; PIMENTA, Ruan; PAIXAO, Camila; SANTOS, Beatriz; RONDON, Maria U.; CRAIGHEAD, Daniel; ROSSMAN, Matthew; CONSOLIM-COLOMBO, Fernanda M.; IRIGOYEN, Maria C.; MARTINEZ-LEMUS, Luis A.
Changes in physical performance after Fontan operation: a follow-up study
(2023) TURQUETTO, A. L. R.; AMATO, L. P.; SOUZA, F. R.; AGOSTINHO, D. R.; LIGEIRO, M. G.; FILHO, A. C. Battaglia; ALVES, M. J. N. N.; OLIVEIRA, P. A.; DIOTTO, F. M.; RONDON, E.; CANEO, L. F.; JATENE, M. B.
Noncontrast Transcatheter Aortic Valve Implantation for Patients With Aortic Stenosis and Chronic Kidney Disease: Long-Term Follow-Up of The Pilot Study
(2023) FILIPPINI, Filippe; FREIRE, Antonio Fernando; NICZ, Pedro; BRATZ, Guilherme; SESSA, Bruno; RIBEIRO, Henrique; ACCORSI, Tarso; LIBERATO, Gabriela; NOMURA, Cesar Higa; CASSAR, Renata; VIEIRA, Marcelo; BIHAN, David Le; BARRETTO, Rodrigo; MATHIAS, Wilson; POMERANTZEFF, Pablo; TARASOUTCHI, Flavio; ABIZAID, Alexandre; BRITO JR., Fabio
Mechanical Esophageal Displacement for Esophageal Safety of Mechanical Esophageal Displacement for Esophageal Thermal Injury Prevention During Atrial Fibrillation Ablation in Swine
(2019) PEREIRA, Renner; PISANI, Cristiano F.; AIELLO, Vera; CESTARI, Idagene A.; MOURA, Daniel; CHOKR, Muhieddine; HARDY, Carina; SISSY, Melo; SCANAVACCA, Mauricio; HACHUL, Denise
OUTCOMES OF CATHETER ABLATION OF ELECTRICAL STORM IN CHAGAS DISEASE
(2023) KULCHETSCKI, Rodrigo; PISANI, Cristiano F.; MAYRINK, Marina; ALEXANDRE, Felipe Kalil; CHOKR, Muhieddine; HARDY, Carina; II, Sissy Lara Melo; SCANAVACCA, Mauricio Ibrahim
DIAGNOSTIC PERFORMANCE OF ECG CRITERIA FOR LEFT VENTRICULAR HYPERTROPHY IN PATIENTS WITH LEFT BUNDLE BRANCH BLOCK: A SYSTEMATIC REVIEW AND META-ANALYSIS
(2023) SOUZA, Isabela Azevedo Ferreira De; GOMES, Cintia; PADRAO, Eduardo; MIYAWAKI, Isabele; MARQUES, Isabela Reis; MOREIRA, Vittoria; LOYOLA JUNIOR, Jose Eduardo Riceto; SILVA, Caroliny; CARDOSO, Rhanderson; OPRYSKO, Carson; TAVARES, Caio A. M.
Effect of Psychotherapy on Quality of Life and Recurrence of Events in Patients With Recurrent Vasovagal Syncope: A Randomized Study
(2020) SILVA, Renata de Barros e; HACHUL, Denise; SILVA, Pedro Gabriel M. De Barros e; SCANAVACCA, Mauricio I.
Improving deep learning shape consistency with a new loss function for left ventricle segmentation in cardiac MRI
(2023) RIBEIRO, Matheus A. O.; GUTIERREZ, Marco A.; NUNES, Fatima L. S.
Guaranteeing anatomical shape consistency in cardiac magnetic resonance imaging for left ventricle segmentation is a complex task due to its shape-changing during the cardiac cycle, the low contrast and resolution of images, the size change between apical and basal slices, the similarity with nearby organs, and the presence of cardiomyopathies that can deform the heart. Although producing segmentations very close to the ones produced by experts according to standard evaluation metrics, deep learning networks still often produce anatomically inconsistent segmentations. In this work, we propose a new shape-based loss function that favors shape consistency. The loss function uses shape information extracted from distance maps estimated by the network. We validate our approach with the ACDC and Sunnybrook public datasets by using standard metrics as well as a shape similarity metric. The results indicate that the proposed loss is able to improve shape similarity and demonstrate good generalization ability, while presenting competitive performance in the standard evaluation metrics.
Exploratory Data Analysis in Electronic Health Records Graphs: Intuitive Features and Visualization Tools
(2023) CAZZOLATO, Mirela T.; GUTIERREZ, Marco Antonio; TRAINA JR., Cactano; FALOUTSOS, Christos; TRAINA, Agma J. M.
Given a large, unlabeled set of Electronic Health Records (EHRs) acquired from multiple hospitals, how can we analyze the available entities and identify relationships in the data? Also, how can we perform Exploratory Data Analysis (EDA) over such EHR data? Many medical institutions generate EHRs as tabular data with entities and attributes in common. However, due to a large number of records, attributes, and high cardinality, exploring the different datasets and finding patterns and insights become laborious and prone to errors. In this work, we propose GraF-EDA for EDA over EHR data from different institutions. GraF-EDA models EHRs as time-evolving graphs, allowing the interoperability of such data into a single representation. We extract meaningful features from the graph nodes and provide intuitive visualizations to improve data explainability. We evaluate GraF-EDA with four COVID-19 datasets from hospitals of the Sao Paulo state, Brazil, resulting in million-scale graphs. Our method identified correlations, similarities and dissimilarities among medical treatments, exams, clinics, and outcomes. With the visual tools provided by GraF-EDA, we were able to spot cases of interest and check more details about them. Our results indicate that GraF-EDA is a fast, effective, open-sourced tool for EDA of EHRs from multiple institutions.
CARDIORENAL DYSFUNCTION IN MICE SUBMITTED TO AORTIC STENOSIS AND TREATED WITH SODIUM OXALATE
(2023) SILVA, Amanda; MARQUES, Juliana; NASCIMENTO, Bruno; SOUZA, Leandro; SILVA, Maikon; BENETTI, Acaris; IRIGOYEN, Maria Claudia
CardioBERTpt: Transformer-based Models for Cardiology Language Representation in Portuguese
(2023) SCHNEIDER, Elisa Terumi Rubel; GUMIEL, Yohan Bonescki; SOUZA, Joao Vitor Andrioli de; MUKAI, Lilian Mie; OLIVEIRA, Lucas Emanuel Silva e; REBELO, Marina de Sa; GUTIERREZ, Marco Antonio; KRIEGER, Jose Eduardo; TEODORO, Douglas; MORO, Claudia; PARAISO, Emerson Cabrera
Contextual word embeddings and the Transformers architecture have reached state-of-the-art results in many natural language processing (NLP) tasks and improved the adaptation of models for multiple domains. Despite the improvement in the reuse and construction of models, few resources are still developed for the Portuguese language, especially in the health domain. Furthermore, the clinical models available for the language are not representative enough for all medical specialties. This work explores deep contextual embedding models for the Portuguese language to support clinical NLP tasks. We transferred learned information from electronic health records of a Brazilian tertiary hospital specialized in cardiology diseases and pre-trained multiple clinical BERT-based models. We evaluated the performance of these models in named entity recognition experiments, fine-tuning them in two annotated corpora containing clinical narratives. Our pre-trained models outperformed previous multilingual and Portuguese BERT-based models for cardiology and multi-specialty environments, reaching the state-of-the-art for analyzed corpora, with 5.5% F1 score improvement in TempClinBr (all entities) and 1.7% in SemClinBr (Disorder entity) corpora. Hence, we demonstrate that data representativeness and a high volume of training data can improve the results for clinical tasks, aligned with results for other languages.
IoT Medical Device Architecture to Estimate Non-invasive Arterial Blood Pressure
(2022) MORENO, Ramon; DIAS, Felipe; ARRUDA, Marcelo; OLIVEIRA, Filipe; BULHOES, Thiago; KRIEGER, Jose; GUTIERREZ, Marco
High blood pressure (BP) is the leading cause of death worldwide. Besides being a treatable condition, alongside medication and a healthy diet, it requires regular BP measurements to assess whether a patient is properly responding to treatment. There have been many attempts to use the photoplethysmography (PPG) signal to estimate BP continuously, but there has yet to be an effective solution. This work presents our efforts to develop a new method for estimating BP from PPG and infrastructure to collect, process, and store this information. PPG signal is measured from a smartband; our App reads the data from the smartband to a smartphone, processes them using a machine learning method, and estimates BP, which is sent to a server that stores and displays the data
THE ASSOCIATION OF ELECTRICAL LEFT AXIS DEVIATION WITH CARDIAC STRUCTURAL ABNORMALITIES IN PATIENTS WITH ADVANCED AGE: AN OBSERVATIONAL STUDY.
(2023) TAVARES, Caio A. M.; SAMESIMA, Nelson; GUIMARAES, Patricia; PADRAO, Eduardo; FACIN, Mirella Esmanhotto; NETO, Felippe Lazar; FERREIRA, Elisa; HAJJAR, Ludhmila; PASTORE, Carlos Alberto
Multilevel Clustering Explainer: An Explainable Approach to Electronic Health Records
(2021) CLEMENTINO JR., Jose M.; FAICAL, Bruno S.; BONES, Christian C.; TRAINA JR., Caetano; GUTIERREZ, Marco A.; TRAINA, Agma J. M.
Machine learning (ML) algorithms have been used in many areas of activity, and their results can often be applied without further human intervention. The ML algorithms have also been widely used in medical contexts, but in this area, the result needs to be thoroughly confirmed by a specialist, who needs explanatory information on how the results were obtained. Aimed at such scenarios, we propose the Multilevel Clustering Explainer (MCE), a method capable of providing explanatory information to health professionals about the knowledge discovery process. The MCE was developed for the analysis of medical data, providing a synthesis of explanatory information for the specialist to quickly and clearly understand how the results were obtained.
Vitamin C Protects Against Doxorubicin-Induced Muscle Oxidative Stress
(2022) NASCIMENTO FILHO, Antonio Viana Do; STOYELL-CONTI, Filipe F.; AKOLKAR, Gauri; MIRANDA, Victor Hugo M. De; SINGAL, Pawan; IRIGOYEN, Maria Claudia; ANGELIS, Katia De; DIAS, Danielle Da Silva
Baroreceptor Deafferentation Impairs the Exercise Training-and Drug Treatment-Induced Adaptations in a Hypertensive Model of Menopause
(2022) FERREIRA, Maycon J.; SILVA, Gabriel Do Carmo; BERNARDES, Nathalia; ARAUJO, Amanda A. De; DIAS, Danielle Da Silva; IRIGOYEN, Maria C.; ANGELIS, Katia De
DIURETIC ASSOCIATED WITH CONCURRENT EXERCISE PROMOTES ADDITIONAL PHYSICAL, AUTONOMIC AND INFLAMMATORY ADAPTATIONS IN HYPERTENSIVE RATS SUBMITTED TO OVARIAN HORMONE PRIVATION
(2022) FERREIRA, Maycon Junior; SILVA, Gabriel Do Carmo; BERNARDES, Nathalia; ARAUJO, Amanda Aparecida De; DIAS, Danielle Da Silva; IRIGOYEN, Maria Claudia; ANGELIS, Katia De

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