This program uses AI to match patients into clinical trials and other research studies at the time of diagnosis. This is an interagency partnership between DOE and NCI’s national Surveillance, Epidemiology, and End Results (SEER) Program aimed at leveraging high-performance computing, novel natural language processing algorithms, and population-based cancer surveillance data to develop a more timely, comprehensive, scalable, and cost-effective cancer surveillance program. By leveraging AI to extract critical information from unstructured clinical data, we achieve “near real time” cancer incidence reporting and are approaching real-time eligibility assessments of cancer patients for clinical trials, which show great potential in advancing the standard of care.
However, matching patients with clinical trials remains a challenge, mostly due to the unstructured nature of eligibility criteria as well as the clinical documentation. ORNL scientists leverage large-scale knowledge graphs and deep learning to bring together cancer registry data, medical ontologies, and clinical trials data to answer complex questions and provide real-time feedback for patients and clinicians on the novel experimental treatments available to them.
Deployed AI algorithms processed over 12 SEER cancer registries and millions of pathologies with increased efficiency and accuracy. The precision and efficiency of AI is expected to reduce the workload burden for cancer registrars while allowing them to realign their focus on abstracting additional complex variables (e.g., new cancer biomarkers, cancer recurrence) currently not possible using AI.