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Oral drugs are becoming increasingly common in oncology care. In contrast tointravenous chemotherapy, which is administered in the clinic and carefullytracked via structure electronic health records (EHRs), oral drug treatment isself-administered and therefore not tracked as well. Often, the details of oralcancer treatment occur only in unstructured clinic notes. Extracting thisinformation is critical to understanding a patient's treatment history. Yet,this a challenging task because treatment intervals must be inferredlongitudinally from both explicit mentions in the text as well as from documenttimestamps. In this work, we present TIFTI (Temporally Integrated Framework forTreatment Intervals), a robust framework for extracting oral drug treatmentintervals from a patient's unstructured notes. TIFTI leverages distinct sourcesof temporal information by breaking the problem down into two separatesubtasks: document-level sequence labeling and date extraction. On a labeleddataset of metastatic renal-cell carcinoma (RCC) patients, it exactly matchedthe labeled start date in 46% of the examples (86% of the examples within 30days), and it exactly matched the labeled end date in 52% of the examples (78%of the examples within 30 days). Without retraining, the model achieved asimilar level of performance on a labeled dataset of advanced non-small-celllung cancer (NSCLC) patients.

TIFTI: A Framework for Extracting Drug Intervals from Longitudinal Clinic Notes