相关实验视频

Updated: Jun 5, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

502

使用大型语言模型在现实医院环境中高效编码癌症登记册:可行性研究

Chen-Kai Wang1, Cheng-Rong Ke2, Ming-Siang Huang3

  • 1Department of Computer Science, National Yang Ming Chiao Tung University Hsinchu, 300093, Taiwan, ROC, Taiwan, dennisckwang@gmail.com.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|December 13, 2024
PubMed
概括

这项研究表明,大型语言模型 (LLM) 可以改善癌症注册表编码. 采用提取增强生成 (RAG) 的快速工程提高了癌症报告工作流程的LLM准确性.

更多相关视频

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.4K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.0K

相关概念视频

Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
328