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.
Abstract (translated)
深度表型研究是对患者症状和病情的详细描述，基于知识图谱的概念。在电子病历中，对大量医生笔记的深入表型研究需要高吞吐量方法。在过去的三十多年里，努力使高吞吐量表型成为现实。在这项研究中，我们证明了大型语言模型和混合NLP模型（结合词向量与机器学习分类器）可以在对医生笔记进行高吞吐量表型研究的同时保持高准确率。大型语言模型很可能成为对医生笔记进行高吞吐量表型研究的首选方法。
URL
https://arxiv.org/abs/2403.05920
