Analyzing Large Language Models for Perioperative Care

Khái niệm cốt lõi

Pre-trained clinical LLMs offer opportunities for postoperative risk predictions, with further improvements from finetuning emphasizing the importance of transferring pre-trained knowledge to maximize their utility in clinical care.

Tóm tắt

In a study focusing on perioperative care, researchers assessed the predictive capabilities of clinical large language models (LLMs) using various training strategies. Pre-trained LLMs outperformed traditional word embeddings, with significant gains in performance. Finetuning these models further improved results, highlighting the potential of leveraging LLMs in healthcare settings. Incorporating labels enhanced model performance, especially in foundational models, showcasing the benefits of task-agnostic learning. The study aimed to bridge gaps in deploying LLMs effectively for perioperative risk predictions.

Thống kê

Pre-trained LLMs outperformed traditional word embeddings with gains of up to 38.3% for AUROC and 14% for AUPRC.
Self-supervised finetuning improved performance by 3.2% for AUROC and 1.5% for AUPRC.
Semi-supervised finetuning showed enhancements of 1.8% for AUROC and 2% for AUPRC compared to self-supervised finetuning.
Foundational modeling led to increases of 3.6% for AUROC and 2.6% for AUPRC compared to self-supervised finetuning.

Trích dẫn

"Pre-trained clinical LLMs offer opportunities for postoperative risk predictions."
"Incorporating labels can boost performance, with peaks in foundational models indicating potential task-agnostic learning."
"The study aimed to bridge critical gaps in deploying LLMs effectively for perioperative risk predictions."

Thông tin chi tiết chính được chắt lọc từ

Prescribing Large Language Models for Perioperative Care

by Bing Xue,Cha... lúc arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.17493.pdf

Yêu cầu sâu hơn

How can the findings from this study be applied to other healthcare domains?

The findings from this study, which demonstrate the effectiveness of pre-trained Large Language Models (LLMs) in predicting postoperative risks using clinical texts, can be extrapolated to various other healthcare domains. For instance, these LLMs could be utilized in areas such as disease diagnosis, patient triage systems, symptom detection in telemedicine apps, and even improving the readability of informed consent documents. By fine-tuning these models with domain-specific data and labels, they can potentially enhance decision-making processes across a wide range of medical specialties.

Is there a possibility that pre-trained LLMs might reach a saturation point where further fine-tuning is unnecessary?

While pre-trained LLMs have shown significant improvements over traditional word embeddings without additional training efforts in certain scenarios ("zero-shot" learning), there is still room for further optimization through fine-tuning. It's possible that these models may not necessarily reach a complete saturation point where no more improvement is needed; instead, continuous adaptation and refinement through finetuning strategies are likely to enhance their performance in specific tasks or datasets. The study highlights how incorporating labels during semi-supervised or foundational finetuning significantly boosts predictive performance compared to self-supervised approaches.

How can the use of foundational models impact decision-making beyond perioperative care?

The utilization of foundational models—wherein a single model is trained across multiple tasks simultaneously—can have far-reaching implications beyond perioperative care. These models offer advantages such as time efficiency by eliminating the need to develop bespoke models for each task and resource savings by sharing knowledge across different outcomes within one comprehensive model. In broader healthcare applications, foundational models could streamline decision-making processes by providing robust predictions for diverse clinical scenarios while ensuring generalizability and mitigating concerns related to overfitting on limited samples from specific tasks. This approach has the potential to revolutionize AI-driven decision support systems across various medical specialties.