  Oral drugs are becoming increasingly common in oncology care. In contrast to
intravenous chemotherapy, which is administered in the clinic and carefully
tracked via structure electronic health records (EHRs), oral drug treatment is
self-administered and therefore not tracked as well. Often, the details of oral
cancer treatment occur only in unstructured clinic notes. Extracting this
information is critical to understanding a patient's treatment history. Yet,
this a challenging task because treatment intervals must be inferred
longitudinally from both explicit mentions in the text as well as from document
timestamps. In this work, we present TIFTI (Temporally Integrated Framework for
Treatment Intervals), a robust framework for extracting oral drug treatment
intervals from a patient's unstructured notes. TIFTI leverages distinct sources
of temporal information by breaking the problem down into two separate
subtasks: document-level sequence labeling and date extraction. On a labeled
dataset of metastatic renal-cell carcinoma (RCC) patients, it exactly matched
the labeled start date in 46% of the examples (86% of the examples within 30
days), 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 a
similar level of performance on a labeled dataset of advanced non-small-cell
lung cancer (NSCLC) patients.
