17 citations · 34 across the 5 of their papers we have counts for
5 papers · 1 filter
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…
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…
Towards personalized causal inference of medication response in mobile health: an instrumental variable approach for randomized trials with imperfect compliance
Elias Chaibub Neto, Ross L Prentice, Brian M Bot +5
Mobile health studies can leverage longitudinal sensor data from smartphones to guide the application of personalized medical interventions. In this paper, we propose that adoption…