  There is a growing interest in creating tools to assist in clinical note
generation using the audio of provider-patient encounters. Motivated by this
goal and with the help of providers and medical scribes, we developed an
annotation scheme to extract relevant clinical concepts. We used this
annotation scheme to label a corpus of about 6k clinical encounters. This was
used to train a state-of-the-art tagging model. We report ontologies, labeling
results, model performances, and detailed analyses of the results. Our results
show that the entities related to medications can be extracted with a
relatively high accuracy of 0.90 F-score, followed by symptoms at 0.72 F-score,
and conditions at 0.57 F-score. In our task, we not only identify where the
symptoms are mentioned but also map them to canonical forms as they appear in
the clinical notes. Of the different types of errors, in about 19-38% of the
cases, we find that the model output was correct, and about 17-32% of the
errors do not impact the clinical note. Taken together, the models developed in
this work are more useful than the F-scores reflect, making it a promising
approach for practical applications.
