  Deep phenotyping is the detailed description of patient signs and symptoms
using concepts from an ontology. The deep phenotyping of the numerous physician
notes in electronic health records requires high throughput methods. Over the
past thirty years, progress toward making high throughput phenotyping feasible.
In this study, we demonstrate that a large language model and a hybrid NLP
model (combining word vectors with a machine learning classifier) can perform
high throughput phenotyping on physician notes with high accuracy. Large
language models will likely emerge as the preferred method for high throughput
deep phenotyping of physician notes.
