High Throughput Phenotyping of Physician Notes with Large Language and Hybrid NLP Models

2024/03/09 by S. Ilyas Munzir, Daniel B. Hier, Munzir, Syed I. +3 · 1 citation
Biochemistry, Genetics and Molecular Biology · Arts and Humanities · #Biomedical Text Mining and Ontologies #Translation Studies and Practices

paper · pdf · doi:10.48550/arxiv.2403.05920

Abstract

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.

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