Summary of High Throughput Phenotyping Of Physician Notes with Large Language and Hybrid Nlp Models, by Syed I. Munzir et al.
High Throughput Phenotyping of Physician Notes with Large Language and Hybrid NLP Models
by Syed I. Munzir, Daniel B. Hier, Michael D. Carrithers
First submitted to arxiv on: 9 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a breakthrough in deep phenotyping, which involves detailed description of patient signs and symptoms using concepts from an ontology. The authors demonstrate that large language models and hybrid NLP models can perform high-throughput phenotyping on physician notes with high accuracy. They combine word vectors with machine learning classifiers to achieve this goal. This approach is likely to emerge as the preferred method for deep phenotyping of physician notes, enabling faster and more accurate processing of electronic health records. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors understand patient symptoms better by using computers to read lots of medical notes quickly and accurately. The authors show that special kinds of artificial intelligence called large language models can do this job well. They combine these models with other techniques to make it happen. This is important because it will help doctors diagnose patients faster and improve healthcare. |
Keywords
» Artificial intelligence » Machine learning » Nlp
