Outputs (2)

Respiratory Volume Monitoring: A Machine-Learning Approach to the Non-Invasive Prediction of Tidal Volume and Minute Ventilation (2020)
Journal Article
Hurtado, D. E., Chávez, J. A. P., Mansilla, R., Lopez, R., & Abusleme, A. (2020). Respiratory Volume Monitoring: A Machine-Learning Approach to the Non-Invasive Prediction of Tidal Volume and Minute Ventilation. IEEE Access, 8, 227936-227944. https://doi.org/10.1109/ACCESS.2020.3045603

Continuous monitoring of ventilatory parameters such as tidal volume (TV) and minute ventilation (MV) has shown to be effective in the prevention of respiratory compromise events in hospitalized patients. However, the non-invasive estimation of respi... Read More about Respiratory Volume Monitoring: A Machine-Learning Approach to the Non-Invasive Prediction of Tidal Volume and Minute Ventilation.

Model Class Reliance for Random Forests (2020)
Presentation / Conference Contribution
Smith, G., Mansilla Lobos, R., & Goulding, J. (2020). Model Class Reliance for Random Forests. In Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020)

Variable Importance (VI) has traditionally been cast as the process of estimating each variable's contribution to a predictive model's overall performance. Analysis of a single model instance, however, guarantees no insight into a variables relevance... Read More about Model Class Reliance for Random Forests.