Promoting Fairness and Equity in the Development and Application of Advanced Analytics in Precision Health
Project Description
Large-scale person-generated health data (PGHD) from smartphones/wearables/sensors are invaluable to digital precision health. This field uses advanced analytics to develop individual-specific interventions to improve health and wellbeing. Equitable digital precision health research and application demands an interconnected infrastructure comprised of (1) a large, sociodemographically representative cohort of participants who have consented to contribute digital data for long timescales, thereby producing (2) large-scale PGHD datasets involving high-quality, well-labeled, continuous and multidimensional data to benchmark development/validation/ evaluation of analytical models; and (3) an advanced analytics platform for manipulating data that adequately maintains participant privacy and data security. This project uses ALiR to develop and deploy a fair and equitable PGHD infrastructure comprising a large sociodemographically representative cohort that regularly contributes various forms of PGHD. This project focuses on developing cyberinfrastructure and analytics tools that maintain participant privacy and security while enabling the development/ testing/ application of advanced analytic approaches. This project will consist of developing analytics tools integrating AI/ML, feature engineering, and econometrics/causal inference for advanced descriptive and predictive analytics and agent-based models, and digital twin simulation tools.
Start Date
May 2024
Postdoc Qualifications
Machine learning, Artificial Intelligence, Data Science
Co-Advisors
Arezoo Ardekani, Mechanical engineering
ardekani@purdue.edu
https://engineering.purdue.edu/ComplexFlowLab/
Alok Chaturvedi, Information systems, Purdue Krannert
alok@purdue.edu
https://business.purdue.edu/directory/bio.php?username=alok
Short Bibliography
Radin JM, Quer G, Ramos E, et al. Assessment of prolonged physiological and behavioral changes associated with COVID-19 infection. JAMA Netw Open. 4(7), e2115959, 2021. doi:10.1001/jamanetworkopen.2021.15959
Giorgio Quer, Jennifer M. Radin, Matteo Gadaleta, Katie Baca-Motes, Lauren Ariniello, Edward Ramos, Vik Kheterpal, Eric J. Topol and Steven R. Steinhubl, “Wearable sensor data and self-reported symptoms for COVID-19 detection”, Nature Medicine, VOL 27, 73–77, 2021. https://doi.org/10.1038/s41591-020-1123-x, https://doi.org/10.1038/s41591-020-1123-x
Miad Boodaghidizaji, Thaisa Jungles, Tingting Chen, Bin Zhang, Alan Landay, Ali Keshavarzian, Bruce Hamaker, Arezoo Ardekani, “Machine learning-based gut microbiota pattern and response to fiber as a diagnostic tool for chronic inflammatory diseases”, 2023, bioRxiv, 2023.03. 27.534466