Luciana Moreira Seara
Biographic Data
| ID | 8524554 |
|---|---|
| NAME | Luciana Moreira Seara |
| GIVEN NAMES | Luciana Moreira |
| FAMILY NAME | Seara |
| SIGNATURE | SEARA L M |
| AFFILIATIONS | Ministério da Saúde |
| ORCID | 0000-0001-9822-6056 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| 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 …
No prominent works on this page.
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…
Computer Science (2 works) · 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) · Bypass grafting (1 works) · Cardiology (1 works)