In Silico, Medical Record-based Model for Understanding the INitiation of Autoimmune Events (IMMUNE) aims to test the feasibility and effectiveness of using a hybrid in silico/ in vivo (computer-simulated/ in living organism) model system, combined with machine learning strategies as a platform for understanding the etiology of autoimmune disease.
IMMUNE brings together a team of immunologists, oncologists, informaticists and machine learning experts working within an electronic health record (EHR) network, to identify a cohort of cancer patients who have undergone immune checkpoint inhibitor (ICI) therapy. From this cohort, the team will design and implement a broad and deep longitudinal database of EHR data, including treatment and response data and laboratory results, to enable the development of phenotypic profiles and models for autoimmune disease development in humans.
This project will create computable phenotypes to identify patients with rheumatoid arthritis that develops spontaneously or following immune checkpoint inhibitor (ICI) therapy. Following identification, these phenotypes will be extended to the PCORnet Common Data Model to expand the cohort size and per-patient data depth.
Supervised and unsupervised machine learning strategies will then be used to develop and compare preliminary phenotypic profiles for patients with rheumatoid arthritis in the presence or absence of cancer and ICI therapy.
Jeffrey Sosman, MD
Carlos Galvez, MD
Al’ona Furmanchuck, PhD, CHIP Faculty
Yuan Luo, PhD, CHIP Faculty
Luke Rasmussen, MS
Charon Gladfelter, CHIP Research Operations Lead
Funding for this project is provided by the National Institutes of Health/ National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIH/ NIAMS).
CHIP collaborators are part of the public, private and nonprofit sectors and have an interest in working toward a world where health outcomes are improved because data is used effectively.Get in Touch