# A Large Language Model Outperforms Other Computational Approaches to the High-Throughput Phenotyping of Physician Notes

arXiv: https://arxiv.org/abs/2406.14757 · HTML: https://mlsanity.com/paper/2406.14757/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 41 references that are in the map.
- First posted on arXiv 20 Jun 2024.

## Builds on (most cited first, 10 of 10)

- [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)
- [Named Entity Recognition with Bidirectional LSTM-CNNs](https://mlsanity.com/paper/1511.08308/) (2015)
- [Metrics for Multi-Class Classification: an Overview](https://mlsanity.com/paper/2008.05756/) (2020)
- [BERT-based Ranking for Biomedical Entity Normalization](https://mlsanity.com/paper/1908.03548/) (2019)
- [Enhancing Phenotype Recognition in Clinical Notes Using Large Language Models: PhenoBCBERT and PhenoGPT](https://mlsanity.com/paper/2308.06294/) (2023)
- [High Throughput Phenotyping of Physician Notes with Large Language and Hybrid NLP Models](https://mlsanity.com/paper/2403.05920/) (2024)
- [Precision Medicine Informatics: Principles, Prospects, and Challenges](https://mlsanity.com/paper/1911.01014/) (2019)

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

- [High-Throughput Phenotyping of Clinical Text Using Large Language Models](https://mlsanity.com/paper/2408.01214/) (2024)
- [A Simplified Retriever to Improve Accuracy of Phenotype Normalizations by Large Language Models](https://mlsanity.com/paper/2409.13744/) (2024)
- [Predicting Failures of LLMs to Link Biomedical Ontology Terms to Identifiers Evidence Across Models and Ontologies](https://mlsanity.com/paper/2509.04458/) (2025)

## Abstract (arXiv)

High-throughput phenotyping, the automated mapping of patient signs and symptoms to standardized ontology concepts, is essential to gaining value from electronic health records (EHR) in the support of precision medicine. Despite technological advances, high-throughput phenotyping remains a challenge. This study compares three computational approaches to high-throughput phenotyping: a Large Language Model (LLM) incorporating generative AI, a Natural Language Processing (NLP) approach utilizing deep learning for span categorization, and a hybrid approach combining word vectors with machine learning. The approach that implemented GPT-4 (a Large Language Model) demonstrated superior performance, suggesting that Large Language Models are poised to be the preferred method for high-throughput phenotyping of physician notes.