Promoting Fairness and Equity in the Development and Application of Advanced Analytics in Precision Health

Interdisciplinary Areas: Data and Engineering Applications, Engineering-Medicine, Others

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


Arezoo Ardekani, Mechanical engineering

Alok Chaturvedi, Information systems, Purdue Krannert


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.,

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