Improving Generalizability of Extracting Social Determinants of Health Using Large Language Models through Prompt-tuning

Keskeiset käsitteet

Generative LLMs with P-tuning enhance SDoH extraction across domains.

Tiivistelmä

• Abstract:
  • Novel approach using soft prompt-based learning architecture for LLMs.
  • Decoder-only LLMs with prompt tuning outperform traditional fine-tuned models.
• Introduction:
  • NLP crucial for patient information extraction from clinical narratives.
  • Transfer learning aids in portability for cross-institution applications.
• Methods:
  • Two datasets used for SDoH extraction evaluation.
  • Encoder-only and decoder-only LLM architectures examined.
• Results:
  • GatorTronGPT models with P-tuning achieve best F1 scores.
  • Performance improvements observed by scaling up LLM sizes.
• Discussion:
  • Fine-tuned LLMs show limited transfer learning ability across domains.
  • P-tuning enhances transfer learning for SDoH extraction tasks.
• Conclusion:
  • Generative LLMs with P-tuning improve cross-domain clinical NLP applications.

Tilastot

GatorTronGPT achieved the best F1 scores for both datasets, outperforming traditional fine-tuned GatorTron by 8.9% and 21.8% in a cross-institution setting, and by 5.5% and 14.5% in a cross-disease setting.

Lainaukset

"Transfer learning is a promising solution to improve the portability of NLP for cross-institution applications."
"Prompt-based learning algorithms have shown promising transfer learning capabilities."
"P-tuning of generative LLMs has better transfer learning ability for cross-institution and cross-disease applications."

Tärkeimmät oivallukset

Improving Generalizability of Extracting Social Determinants of Health Using Large Language Models through Prompt-tuning

by Cheng Peng,Z... klo arxiv.org 03-20-2024

https://arxiv.org/pdf/2403.12374.pdf

Syvällisempiä Kysymyksiä

How can the findings of this study be applied to other areas within health informatics?

The findings of this study on improving generalizability through prompt-tuning with large language models (LLMs) can have significant implications for various areas within health informatics. One key application is in clinical concept extraction and relation extraction tasks, which are crucial for analyzing patient information from electronic health records (EHRs). By demonstrating the effectiveness of P-tuning with generative LLMs like GatorTronGPT, these techniques can be extended to tasks such as disease diagnosis, treatment recommendation systems, adverse event detection, and population health analytics. The ability to enhance transfer learning and improve performance across different domains or institutions opens up possibilities for more robust and adaptable healthcare AI solutions.

What are potential drawbacks or limitations of relying heavily on large language models like GatorTronGPT?

While large language models like GatorTronGPT offer impressive capabilities in natural language processing tasks, there are several drawbacks and limitations associated with relying heavily on them:

Computational Resources: Training and fine-tuning large LLMs require substantial computational resources in terms of processing power, memory capacity, and energy consumption.
Data Privacy Concerns: Large LLMs often need vast amounts of data for training effectively. This raises concerns about privacy when dealing with sensitive healthcare information contained in EHRs.
Bias Amplification: If not carefully monitored during training data selection or model development stages, biases present in the input data may get amplified by large LLMs leading to biased outputs.
Interpretability: Understanding how decisions are made by complex LLMs like GatorTronGPT can be challenging due to their black-box nature, limiting transparency and interpretability.
Fine-Tuning Dependency: Heavy reliance on fine-tuning strategies may lead to overfitting on specific datasets or tasks without achieving true generalizability across diverse applications.

How might prompt-based learning impact the future development of natural language processing technologies?

Prompt-based learning has shown great promise in enhancing transfer learning abilities while maintaining model efficiency in natural language processing (NLP) technologies. Here's how it might impact future NLP developments:

Improved Adaptability: Prompt-based methods enable easier adaptation of pre-trained models to new tasks without extensive re-training or domain-specific annotations.
Few-Shot Learning Capabilities: By using prompts as task descriptions instead of task-specific architectures, prompt-based approaches facilitate few-shot learning scenarios where limited labeled data is available.
Reduced Data Annotation Needs: With soft prompts guiding model behavior instead of extensive annotated datasets for each new task/domain, prompt-based learning reduces the burden of manual annotation efforts required for traditional supervised learning approaches.
Enhanced Generalization Across Domains: Prompt tuning allows for better generalization capabilities across different domains by decoupling task-specific knowledge from pre-trained representations.
5Ethical Considerations:: Prompts could potentially serve as a mechanism to inject ethical considerations into NLP systems by guiding them towards generating fairer outcomes based on predefined guidelines embedded within prompts.

These advancements suggest that prompt-based learning will play a pivotal role in shaping more versatile and adaptive NLP technologies that excel at handling diverse real-world challenges efficiently while promoting transparency and fairness in decision-making processes involving textual data analysis