The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Workshop:CAFCW21: Computational Approaches for Cancer Workshop 2021
Authors: Divya Nagaraj (Stanford University)
Abstract: Electronic 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.
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