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Cardiovascular Disease Prediction Modelling: A Machine Learning Approach, Usmaan Al-Shehab, Maduka Gunasinghe, Yousuf Elkhoga, Nimay Patel, Juliana Yang
Cardiovascular Disease Prediction Modelling: A Machine Learning Approach, Usmaan Al-Shehab, Maduka Gunasinghe, Yousuf Elkhoga, Nimay Patel, Juliana Yang
Rowan-Virtua Research Day
The objective of this project is to utilize the UCI Heart Disease dataset to identify physiological biomarkers that are highly correlated with heart disease incidence. A predictive model can then be developed using these biomarkers to estimate the likelihood of someone having or developing a heart-related condition. This study compares the efficacy of predicting cardiovascular disease as an outcome using three machine learning algorithms: Support Vector Machine, Gaussian Naive Bayes, and logistic regression. Support Vector Machine works by creating hyperplanes between data points to conduct classification. Gaussian Naive Bayes works by using the conditional probabilities of events to classify the …
Management And Considerations In The Usage Of Txa In Hemorrhagic Trauma Patients, Matthew Petterson, James Espinosa, Alan Lucerna
Management And Considerations In The Usage Of Txa In Hemorrhagic Trauma Patients, Matthew Petterson, James Espinosa, Alan Lucerna
Rowan-Virtua Research Day
Tranexamic acid is a synthetic reversible competitive inhibitor that exerts its action on plasminogen, preventing plasmin formation and deterring fibrinolysis.1 Current FDA-approved indications of TXA include heavy menstrual bleeding and short-term blood loss prevention in patients with hemophilia following tooth extraction.1 TXA has more recently been utilized in the management of massive hemorrhagic trauma patients despite this being an off-label use. While TXA has shown promise as a hemostatic agent for this patient population, considerations in the pre-hospital and hospital settings must be examined for its integration into massive hemorrhage protocols.