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

arXiv: https://arxiv.org/abs/2403.05920 · HTML: https://mlsanity.com/paper/2403.05920/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 46 references that are in the map.
- First posted on arXiv 9 Mar 2024.

## Builds on (most cited first, 8 of 8)

- [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)
- [BERT-based Ranking for Biomedical Entity Normalization](https://mlsanity.com/paper/1908.03548/) (2019)
- [Large language models in medicine: the potentials and pitfalls](https://mlsanity.com/paper/2309.00087/) (2023)
- [Large Language Models with Retrieval-Augmented Generation for Zero-Shot Disease Phenotyping](https://mlsanity.com/paper/2312.06457/) (2023)

## Built on by (most cited first, 4 of 4)

- [A Large Language Model Outperforms Other Computational Approaches to the High-Throughput Phenotyping of Physician Notes](https://mlsanity.com/paper/2406.14757/) (2024)
- [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)

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.