Predicting Response to Antiretroviral Treatment by Machine Learning: The EuResist Project

By Maurizio Zazzi, F. Incardona, Michal Rosen-Zvi, Mattia C. F. Prosperi, Thomas Lengauer, André Altmann, A. Sonnerborg, T. Laveé, Eugen Schülter, R. Kaiser. 2012

59 citations recorded by the source corpus.

Subfields: Infectious Diseases, Virology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Infectious Diseases

1. Vukosi Marivate
2. Michael L. Littman
3. Maurizio Zazzi
4. Jasmina Bogojeska

In Virology

1. Vukosi Marivate
2. Finale Doshi-Velez
3. Maurizio Zazzi
4. Jasmina Bogojeska
5. Sonali Parbhoo

Citations in the corpus

• Beyond Sparsity: Tree Regularization of Deep Models for Interpretability (2017, methodology): “• HIV Therapy Outcome (HIV): We use the EuResist Integrated Database (Zazzi et al. 2012) for 53 236 patients diagnosed with HIV.”
• Combining Kernel and Model Based Learning for HIV Therapy Selection (2017, methodology): “Cohorts The data used for this research comes from the EuResist database [16].”
• Quantifying Uncertainty in Batch Personalized Sequential Decision Making (2014, methodology): “We used an HIV (Human Immunodeficiency Virus) dataset from the EUResist 2We do not model uncertainty due to the local EM search, but we can later assess how well our approach performs in terms of capturing real individuals’ returns. project (Zazzi et al. 2012).”
• Improved empirical methods in reinforcement-learning evaluation (2015, methodology): “The dataset was originally envisioned as a tool for researchers to build models that predict whether a drug would be effective or not on a patient (Zazzi et al., 2012; Prosperi et al., 2009).”