  Automatic structuring of electronic medical records is of high demand for
clinical workflow solutions to facilitate extraction, storage, and querying of
patient care information. However, developing a scalable solution is extremely
challenging, specifically for radiology reports, as most healthcare institutes
use either no template or department/institute specific templates. Moreover,
radiologists' reporting style varies from one to another as sentences are
telegraphic and do not follow general English grammar rules. We present an
ensemble method that consolidates the predictions of three models, capturing
various attributes of textual information for automatic labeling of sentences
with section labels. These three models are: 1) Focus Sentence model, capturing
context of the target sentence; 2) Surrounding Context model, capturing the
neighboring context of the target sentence; and finally, 3) Formatting/Layout
model, aimed at learning report formatting cues. We utilize Bi-directional
LSTMs, followed by sentence encoders, to acquire the context. Furthermore, we
define several features that incorporate the structure of reports. We compare
our proposed approach against multiple baselines and state-of-the-art
approaches on a proprietary dataset as well as 100 manually annotated radiology
notes from the MIMIC-III dataset, which we are making publicly available. Our
proposed approach significantly outperforms other approaches by achieving 97.1%
accuracy.
