mlsanityData to 14 Oct 2026

GAMedX: Generative AI-based Medical Entity Data Extractor Using Large Language Models

arXiv 2405.20585 ↗First posted 31 May 2024cs.CL cs.AIcited by 5

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Abstract (arXiv)

In the rapidly evolving field of healthcare and beyond, the integration of generative AI in Electronic Health Records (EHRs) represents a pivotal advancement, addressing a critical gap in current information extraction techniques. This paper introduces GAMedX, a Named Entity Recognition (NER) approach utilizing Large Language Models (LLMs) to efficiently extract entities from medical narratives and unstructured text generated throughout various phases of the patient hospital visit. By addressing the significant challenge of processing unstructured medical text, GAMedX leverages the capabilities of generative AI and LLMs for improved data extraction. Employing a unified approach, the methodology integrates open-source LLMs for NER, utilizing chained prompts and Pydantic schemas for structured output to navigate the complexities of specialized medical jargon. The findings reveal significant ROUGE F1 score on one of the evaluation datasets with an accuracy of 98%. This innovation enhances entity extraction, offering a scalable, cost-effective solution for automated forms filling from unstructured data. As a result, GAMedX streamlines the processing of unstructured narratives, and sets a new standard in NER applications, contributing significantly to theoretical and practical advancements beyond the medical technology sphere.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 35 of its 63 references that are in the map. First posted on arXiv 31 May 2024.

Builds on

35 of 63 references.

References · 35

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020Language Models are Few-Shot Learnerscited by 32,419
  4. 2022Training language models to follow instructions with human feedbackcited by 15,971
  5. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  6. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  7. 2023Mistral 7Bcited by 3,164
  8. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,811
  9. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,790
  10. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
  11. 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,470
  12. 2020Domain-Specific Language Model Pretraining for Biomedical Natural Language Processingcited by 1,009
23 more
  1. 2019Publicly Available Clinical BERT Embeddingscited by 1,002
  2. 2016Neural Architectures for Named Entity Recognitioncited by 550
  3. 2020LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attentioncited by 326
  4. 2019SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimizationcited by 254
  5. 2022GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Recordscited by 238
  6. 2022Large Language Models are Few-Shot Clinical Information Extractorscited by 207
  7. 2019A Unified MRC Framework for Named Entity Recognitioncited by 199
  8. 2021Reframing Instructional Prompts to GPTk's Languagecited by 176
  9. 2020Named Entity Recognition as Dependency Parsingcited by 151
  10. 2019Semantics-aware BERT for Language Understandingcited by 112
  11. 2020Retrospective Reader for Machine Reading Comprehensioncited by 86
  12. 2020BioMegatron: Larger Biomedical Domain Language Modelcited by 76
  13. 2019TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selectioncited by 74
  14. 2021Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extractioncited by 68
  15. 2021Packed Levitated Marker for Entity and Relation Extractioncited by 49
  16. 2020BERT-XML: Large Scale Automated ICD Coding Using BERT Pretrainingcited by 37
  17. 2021Relation Classification with Entity Type Restrictioncited by 31
  18. 2023The 2022 n2c2/UW Shared Task on Extracting Social Determinants of Healthcited by 21
  19. 2022QaNER: Prompting Question Answering Models for Few-shot Named Entity Recognitioncited by 15
  20. 2023Clinical Concept and Relation Extraction Using Prompt-based Machine Reading Comprehensioncited by 13
  21. 2023Contextualized Medication Information Extraction Using Transformer-based Deep Learning Architecturescited by 8
  22. 2023AE-GPT: Using Large Language Models to Extract Adverse Events from Surveillance Reports-A Use Case with Influenza Vaccine Adverse Eventscited by 8
  23. 2022What GPT Knows About Who is Whocited by 7

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Cited by year

Citing papers per year: 2024: 1; 2025: 4.

5 papers.

  1. 2025BEYONDWORDS is All You Need: Agentic Generative AI based Social Media Themes Extractorcited by 3
  2. 2025QUAD-LLM-MLTC: Large Language Models Ensemble Learning for Healthcare Text Multi-Label Classificationcited by 1
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  4. 2025Enhancing LLMs for Identifying and Prioritizing Important Medical Jargons from Electronic Health Record Notes Utilizing Data Augmentation: A Comparative Studycited by 0
  5. 2025Semantic Similarity in Radiology Reports via LLMs and NERcited by 0