98 citations · 138 across the 9 of their papers we have counts for
9 papers
A Multifaceted Benchmarking of Synthetic Electronic Health Record Generation Models
Chao Yan, Yao Yan, Zhiyu Wan +5
Synthetic health data have the potential to mitigate privacy concerns when sharing data to support biomedical research and the development of innovative healthcare applications. Mo…
The NLP Sandbox: an efficient model-to-data system to enable federated and unbiased evaluation of clinical NLP models
Yao Yan, Thomas Yu, Kathleen Muenzen +14
Objective The evaluation of natural language processing (NLP) models for clinical text de-identification relies on the availability of clinical notes, which is often restricted due…
Stable predictions for health related anticausal prediction tasks affected by selection biases: the need to deconfound the test set features
Elias Chaibub Neto, Phil Snyder, Solveig K Sieberts +1
In health related machine learning applications, the training data often corresponds to a non-representative sample from the target populations where the learners will be deployed.…
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…
Indicators of retention in remote digital health studies: A cross-study evaluation of 100,000 participants
Abhishek Pratap, Elias Chaibub Neto, Phil Snyder +11
Digital technologies such as smartphones are transforming the way scientists conduct biomedical research using real-world data. Several remotely-conducted studies have recruited th…
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…
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…