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A common practice in the medical industry is the use of clinical notes, whichconsist of detailed patient observations. However, electronic health recordsystems 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 offersmany applications, including longitudinal analysis of patients, riskassessment, and hospital evaluation. Recent work has constructed successfulmethods for phenotyping: extracting medical attributes from clinical notes.BERT-based models can be used to transform clinical notes into a series ofrepresentations, which are then condensed into a single document representationbased on their CLS embeddings and passed into an LSTM (Mulyar et al., 2020).Though this pipeline yields a considerable performance improvement overprevious results, it requires extensive convergence time. This method also doesnot allow for predicting attributes not yet identified in clinical notes.  Considering the wide variety of medical attributes that may be present in aclinical note, we propose an alternative pipeline utilizing ScispaCy (Neumannet al., 2019) for the extraction of common diseases. We then train varioussupervised learning models to associate the presence of these conditions withpatient attributes. Finally, we replicate a ClinicalBERT (Alsentzer et al.,2019) and LSTM-based approach for purposes of comparison. We find thatalternative methods moderately underperform the replicated LSTM approach. Yet,considering a complex tradeoff between accuracy and runtime, in addition to thefact that the alternative approach also allows for the detection of medicalconditions that are not already present in a clinical note, its usage may beconsidered as a supplement to established methods.

Investigating Alternative Feature Extraction Pipelines For Clinical Note Phenotyping