116 citations · 136 across the 3 of their papers we have counts for
5 papers · 1 filter
Causality-based tests to detect the influence of confounders on mobile health diagnostic applications: a comparison with restricted permutations
Elias Chaibub Neto, Meghasyam Tummalacherla, Lara Mangravite +1
Machine learning practice is often impacted by confounders. Confounding can be particularly severe in remote digital health studies where the participants self-select to enter the…
Using permutations to assess confounding in machine learning applications for digital health
Elias Chaibub Neto, Abhishek Pratap, Thanneer M Perumal +4
Clinical machine learning applications are often plagued with confounders that can impact the generalizability and predictive performance of the learners. Confounding is especially…
On the analysis of personalized medication response and classification of case vs control patients in mobile health studies: the mPower case study
Elias Chaibub Neto, Thanneer M Perumal, Abhishek Pratap +3
In this work we provide a couple of contributions to the analysis of longitudinal data collected by smartphones in mobile health applications. First, we propose a novel statistical…
Reducing overfitting in challenge-based competitions
Elias Chaibub Neto, Bruce R Hoff, Chris Bare +10
Over-fitting is a dreaded foe in challenge-based competitions. Because participants rely on public leaderboards to evaluate and refine their models, there is always the danger they…
Causal graphical models in systems genetics: A unified framework for joint inference of causal network and genetic architecture for correlated phenotypes
Elias Chaibub Neto, Mark P. Keller, Alan D. Attie +1
Causal inference approaches in systems genetics exploit quantitative trait loci (QTL) genotypes to infer causal relationships among phenotypes. The genetic architecture of each phe…