Investigating Alternative Feature Extraction Pipelines For Clinical Note Phenotyping
This work addresses the challenge of automated phenotyping for medical applications, but it is incremental as it builds on existing methods with trade-offs in accuracy and speed.
The paper tackled the problem of extracting medical attributes from unstructured clinical notes by proposing an alternative pipeline using ScispaCy and supervised learning, which moderately underperformed a BERT-LSTM baseline but offered faster runtime and detection of new conditions.
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.