# High-Throughput Phenotyping of Clinical Text Using Large Language Models

arXiv: https://arxiv.org/abs/2408.01214 · HTML: https://mlsanity.com/paper/2408.01214/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 72 references that are in the map.
- First posted on arXiv 2 Aug 2024.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [Benchmarking Large Language Models in Retrieval-Augmented Generation](https://mlsanity.com/paper/2309.01431/) (2023)
- [TrustLLM: Trustworthiness in Large Language Models](https://mlsanity.com/paper/2401.05561/) (2024)
- [Named Entity Recognition with Bidirectional LSTM-CNNs](https://mlsanity.com/paper/1511.08308/) (2015)
- [Do We Still Need Clinical Language Models?](https://mlsanity.com/paper/2302.08091/) (2023)
- [Large AI Models in Health Informatics: Applications, Challenges, and the Future](https://mlsanity.com/paper/2303.11568/) (2023)
- [BERT-based Ranking for Biomedical Entity Normalization](https://mlsanity.com/paper/1908.03548/) (2019)
- [Identifying and Extracting Rare Disease Phenotypes with Large Language Models](https://mlsanity.com/paper/2306.12656/) (2023)
- [Large Language Models with Retrieval-Augmented Generation for Zero-Shot Disease Phenotyping](https://mlsanity.com/paper/2312.06457/) (2023)
- [An evaluation of GPT models for phenotype concept recognition](https://mlsanity.com/paper/2309.17169/) (2023)
- [Generalizable and Scalable Multistage Biomedical Concept Normalization Leveraging Large Language Models](https://mlsanity.com/paper/2405.15122/) (2024)
- [High Throughput Phenotyping of Physician Notes with Large Language and Hybrid NLP Models](https://mlsanity.com/paper/2403.05920/) (2024)
- [Robust Benchmarking for Machine Learning of Clinical Entity Extraction](https://mlsanity.com/paper/2007.16127/) (2020)
- [A Large Language Model Outperforms Other Computational Approaches to the High-Throughput Phenotyping of Physician Notes](https://mlsanity.com/paper/2406.14757/) (2024)
- [Precision Medicine Informatics: Principles, Prospects, and Challenges](https://mlsanity.com/paper/1911.01014/) (2019)
- [Enhancing Dimension-Reduced Scatter Plots with Class and Feature Centroids](https://mlsanity.com/paper/2403.20246/) (2024)

## Built on by (most cited first, 3 of 3)

- [A Simplified Retriever to Improve Accuracy of Phenotype Normalizations by Large Language Models](https://mlsanity.com/paper/2409.13744/) (2024)
- [From Memorization to Generalization: Fine-Tuning Large Language Models for Biomedical Term-to-Identifier Normalization](https://mlsanity.com/paper/2510.19036/) (2025)
- [A Systematic Review on the Generative AI Applications in Human Medical Genomics](https://mlsanity.com/paper/2508.20275/) (2025)

## Abstract (arXiv)

High-throughput phenotyping automates the mapping of patient signs to standardized ontology concepts and is essential for precision medicine. This study evaluates the automation of phenotyping of clinical summaries from the Online Mendelian Inheritance in Man (OMIM) database using large language models. Due to their rich phenotype data, these summaries can be surrogates for physician notes. We conduct a performance comparison of GPT-4 and GPT-3.5-Turbo. Our results indicate that GPT-4 surpasses GPT-3.5-Turbo in identifying, categorizing, and normalizing signs, achieving concordance with manual annotators comparable to inter-rater agreement. Despite some limitations in sign normalization, the extensive pre-training of GPT-4 results in high performance and generalizability across several phenotyping tasks while obviating the need for manually annotated training data. Large language models are expected to be the dominant method for automating high-throughput phenotyping of clinical text.