AI and Machine Learning Applications to Biomedical Informatics
We use a variety of AI, machine learning (ML) and data mining (DM) tools to provide insights into biomedical problems. This research is rooted in the explosion of data available for biomedical research. In recent years we have focused on temporal ML and DM as well as on understanding end-to-end uncertainty quantification for biomedical prediction and modeling. For the first, we are developing a comprehensive program in computational diabetes, with a focus on understanding the onset of T1D, which now we are extending to other autoimmune diseases. For the second we are exploring both classical techniques such as Monte Carlo simulations as well recent advances in Conformal Prediction.
Exemplar Publications
Mistry, S., Gouripeddi, R., Facelli, J.C. Data-driven identification of temporal glucose patterns in a large cohort of nondiabetic patients with COVID-19 using time-series clustering (2021) JAMIA Open 4:3 DOI: 10.1093/jamiaopen/ooab063.
Vazquez, J., Facelli, J.C. Conformal Prediction in Clinical Medical Sciences (2022) Journal of Healthcare Informatics Research. https://doi.org/10.1007/s41666-021-00113-8.
Maldonado-Catala P, Gouripeddi R, Schlesinger N, Facelli JC. Molecular mimicry impact of the COVID-19 pandemic: Sequence homology between SARS-CoV-2 and autoimmune diseases epitopes. ImmunoInformatics. 2025;18:100050.
Fearn T, Gouripeddi R, Maldonado-Catala PJ, Lebiedz-Odrobina D, Schlesinger N, Facelli J. 20 Decoding auto-immunity: Uncovering pre-onset infectious disease patterns of idiopathic inflammatory myopathies. Journal of Clinical and Translational Science. 2025;9(s1):7-
Millar AS, Arnn J, Himes S, Facelli JC. Uncertainty in breast cancer risk prediction: a conformal prediction study of race stratification. MEDINFO 2023—The Future Is Accessible: IOS Press; 2024. p. 991-5.