Application of Machine Learning in Early Recommendation of Cardiac Resynchronization Therapy

no code implementations • 13 Sep 2021 • Brendan E. Odigwe, Francis G. Spinale, Homayoun Valafar

We have demonstrated that using machine learning approaches can identify HF patients with a high probability of a positive CRT response (95% accuracy), and of equal importance, identify those HF patients that would not derive a functional benefit from CRT.

BIG-bench Machine Learning Decision Making

Modelling of Sickle Cell Anemia Patients Response to Hydroxyurea using Artificial Neural Networks

no code implementations • 25 Nov 2019 • Brendan E. Odigwe, Jesuloluwa S. Eyitayo, Celestine I. Odigwe, Homayoun Valafar

While Hydroxyurea reduces the complications associated with Sickle Cell Anemia in some patients, others do not benefit from this drug and experience deleterious effects since it is also a chemotherapeutic agent.