8 citations · 20 across the 3 of their papers we have counts for
3 papers
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…
Learning Disease vs Participant Signatures: a permutation test approach to detect identity confounding in machine learning diagnostic applications
Elias Chaibub Neto, Abhishek Pratap, Thanneer M Perumal +6
Recently, Saeb et al (2017) showed that, in diagnostic machine learning applications, having data of each subject randomly assigned to both training and test sets (record-wise data…