High Throughput Phenotyping of Physician Notes with Large Language and Hybrid NLP Models
This addresses the problem of efficiently extracting detailed patient signs and symptoms from numerous physician notes for healthcare applications, but it is incremental as it builds on existing phenotyping methods.
The study tackled high-throughput deep phenotyping of physician notes from electronic health records by demonstrating that a large language model and a hybrid NLP model achieve high accuracy, with large language models likely becoming the preferred method.
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.