Predicting Tumor Time to Recurrence from Free-Text Notes
Event Type
Workshop
Applications
Computational Science
Education and Training and Outreach
HPC Community Collaboration
HPC Training and Education
Machine Learning and Artificial Intelligence
Performance
Workforce
W
TimeSunday, 14 November 20212:45pm - 3pm CST
Location223
DescriptionElectronic medical records contain a significant amount of unstructured patient information from free text, but crucial information can be difficult to find within lengthy notes. Thus, we develop an automated tool that can detect mentions of tumor recurrence and progression in clinical, radiology, and pathology notes to infer time to recurrence and progression. This approach avoids the need for re-training models and is flexible enough to be applied to a variety of institutions and note types.
We tested this approach with a cohort of pediatric and adult brain tumor patients with 27,137 clinical, radiology, and pathology notes from Stanford University Hospital, creating cumulative patient trajectory graphs and fine-tuning models to predict time to recurrence. To assess initial accuracy, we compared the patient trajectories to their diagnosis from the ICD-10 codes associated with the notes and found high accuracy from the weak labeling pipeline.
We tested this approach with a cohort of pediatric and adult brain tumor patients with 27,137 clinical, radiology, and pathology notes from Stanford University Hospital, creating cumulative patient trajectory graphs and fine-tuning models to predict time to recurrence. To assess initial accuracy, we compared the patient trajectories to their diagnosis from the ICD-10 codes associated with the notes and found high accuracy from the weak labeling pipeline.
Author/Presenter
