Investigating Alternative Feature Extraction Pipelines For Clinical Note
  Phenotyping
Neil Daniel
A common practice in the medical industry is the use of clinical notes, which
consist of detailed patient observations. However, electronic health record
systems frequently do not contain these observations in a structured format,
rendering patient information challenging to assess and evaluate automatically.
Using computational systems for the extraction of medical attributes offers
many applications, including longitudinal analysis of patients, risk
assessment, and hospital evaluation. Recent work has constructed successful
methods for phenotyping: extracting medical attributes from clinical notes.
BERT-based models can be used to transform clinical notes into a series of
representations, which are then condensed into a single document representation
based on their CLS embeddings and passed into an LSTM (Mulyar et al., 2020).
Though this pipeline yields a considerable performance improvement over
previous results, it requires extensive convergence time. This method also does
not allow for predicting attributes not yet identified in clinical notes.
  Considering the wide variety of medical attributes that may be present in a
clinical note, we propose an alternative pipeline utilizing ScispaCy (Neumann
et al., 2019) for the extraction of common diseases. We then train various
supervised learning models to associate the presence of these conditions with
patient attributes. Finally, we replicate a ClinicalBERT (Alsentzer et al.,
2019) and LSTM-based approach for purposes of comparison. We find that
alternative methods moderately underperform the replicated LSTM approach. Yet,
considering a complex tradeoff between accuracy and runtime, in addition to the
fact that the alternative approach also allows for the detection of medical
conditions that are not already present in a clinical note, its usage may be
considered as a supplement to established methods.