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.

用大型语言模型和混合 NLP 模型（将词向量与机器学习分类器相结合）高准确度地进行医生笔记的高通量特征化，从而让大型语言模型成为医生笔记的高通量深度特征化的首选方法。

基于大规模语言与混合自然语言处理模型的医生笔记高通量表型分析

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

Background: Common data models solve many challenges of standardizing
electronic health record (EHR) data, but are unable to semantically integrate
all the resources needed for deep phenotyping. Open Biological and Biomedical
Ontology (OBO) Foundry ontologies provide computable representations of
biological knowledge and enable the integration of heterogeneous data. However,
mapping EHR data to OBO ontologies requires significant manual curation and
domain expertise. Objective: We introduce OMOP2OBO, an algorithm for mapping
Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO
ontologies. Results: Using OMOP2OBO, we produced mappings for 92,367
conditions, 8611 drug ingredients, and 10,673 measurement results, which
covered 68-99% of concepts used in clinical practice when examined across 24
hospitals. When used to phenotype rare disease patients, the mappings helped
systematically identify undiagnosed patients who might benefit from genetic
testing. Conclusions: By aligning OMOP vocabularies to OBO ontologies our
algorithm presents new opportunities to advance EHR-based deep phenotyping.

介绍了一种将 OMOP 词汇表映射到 OBO 本体论的算法 OMOP2OBO，可以帮助系统地识别从基因测试中受益的难以诊断的患者，从而促进了基于 EHR 的深度表型分析。