Tânia Moreira Grillo Pedrosa
Biographic Data
| ID | 8524142 |
|---|---|
| NAME | Tânia Moreira Grillo Pedrosa |
| GIVEN NAMES | Tânia Moreira Grillo |
| FAMILY NAME | Pedrosa |
| SIGNATURE | PEDROSA T M G |
| AFFILIATIONS | Faculdade de Ciências Médicas de Minas Gerais |
| ORCID | 0000-0002-0042-8125 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
Development of a machine learning modelto estimate length of stay in coronaryartery bypass grafting
OBJECTIVE: To develop and validate a predictive model utilizing machine-learning techniques for estimating the length of hospital stay among patients who underwent coronary artery bypass grafting.METHODS: Three machine learning models (random forest, extreme gradient boosting and neural networks) and three traditional regression models (Poisson regression, linear regression, negative binomial regression) were trained in a dataset of 9,584 patient…
Covid-19 vaccination priorities defined on machine learning
OBJECTIVE: Defining priority vaccination groups is a critical factor to reduce mortality rates. METHODS: We sought to identify priority population groups for covid-19 vaccination, based on in-hospital risk of death, by using Extreme Gradient Boosting Machine Learning (ML) algorithm. We performed a retrospective cohort study comprising 49,197 patients (18 years or older), with RT-PCR-confirmed for covid-19, who were hospitalized in any of the 336 …
Factors associated with the length of hospital stay of women undergoing cesarean section
OBJECTIVE: To evaluate whether age group, complications or comorbidities are associated with the length of hospitalization of women undergoing cesarean section. METHODS: A cross-sectional study was carried out between June 2012 and July 2017, with 64,437 women undergoing cesarean section and who did not acquire conditions during their hospital stay. Hospital discharge data were collected from national health institutions, using the Diagnosis-Rela…
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Factors associated with the length of hospital stay of women undergoing cesarean section
OBJECTIVE: To evaluate whether age group, complications or comorbidities are associated with the length of hospitalization of women undergoing cesarean section. METHODS: A cross-sectional study was carried out between June 2012 and July 2017, with 64,437 women undergoing cesarean section and who did not acquire conditions during their hospital stay. Hospital discharge data were collected from national health institutions, using the Diagnosis-Rela…
Covid-19 vaccination priorities defined on machine learning
OBJECTIVE: Defining priority vaccination groups is a critical factor to reduce mortality rates. METHODS: We sought to identify priority population groups for covid-19 vaccination, based on in-hospital risk of death, by using Extreme Gradient Boosting Machine Learning (ML) algorithm. We performed a retrospective cohort study comprising 49,197 patients (18 years or older), with RT-PCR-confirmed for covid-19, who were hospitalized in any of the 336 …
Development of a machine learning modelto estimate length of stay in coronaryartery bypass grafting
OBJECTIVE: To develop and validate a predictive model utilizing machine-learning techniques for estimating the length of hospital stay among patients who underwent coronary artery bypass grafting.METHODS: Three machine learning models (random forest, extreme gradient boosting and neural networks) and three traditional regression models (Poisson regression, linear regression, negative binomial regression) were trained in a dataset of 9,584 patient…
Medicine (3 works) · Computer Science (2 works) · Internal Medicine (2 works) · Internal Medicine (2 works) · Sepsis Diagnosis and Treatment (2 works) · 2019-20 coronavirus outbreak (1 works) · Artery (1 works) · Artificial Intelligence (1 works) · Artificial Intelligence (1 works) · Betacoronavirus (1 works)