相关实验视频

Updated: Jan 9, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

系统性抗癌疗法时间表从电子医疗记录中提取文本:算法开发和验证

Jiarui Yao¹, Eli Goldner¹, Harry Hochheiser²

¹Computational Health Informatics Program, Boston Children's Hospital, Harvard Medical School, 401 Park Drive, Boston, MA, 02115, United States, 1 7813545014.

JMIR bioinformatics and biotechnology

|December 4, 2025

概括

从电子病历 (EMR) 中自动提取系统性抗癌治疗 (SACT) 时间表至关重要. 一个精心调整的EntityBERT模型获得了93%的F1得分,在SACT时间线提取方面表现优于大型语言模型.

科学领域:

自然语言处理自然语言处理.
计算语言学计算语言学
生物信息学是一种生物信息学.

背景情况:

系统性抗癌疗法 (SACT) 通常涉及复杂的药物组合和序列.
电子医疗记录 (EMR) 中的临床叙述包含详细的SACT时间表.
自动提取这些时间表是一个重大挑战.

研究的目的:

探索用于从EMR中的临床叙述中提取患者级SACT时间表的自动方法.
为了对比微调语言模型和大型语言模型 (LLM) 在此任务中的性能.

主要方法:

使用了两个数据集:THYME (结肠直肠癌) 和Chemotimelines共享任务 (卵巢,乳腺癌,黑色素瘤).
探索了微调较小的语言模型 (EntityBERT) 和LLM的少数镜头提示 (LLaMA,Mixtral).
评估了子任务1 (从注释输入构建时间表) 和子任务2 (直接从笔记提取) 的绩效.

主要成果:

精心调整的EntityBERT模型获得了93%的F1得分,超过了共享任务ChemoTimelines中最佳的子任务1结果 (90%).
实体BERT在子任务2.2中排名第二.
LLM (LLaMA2,LLaMA3.1,Mixtral) 的表现低于微调模型,最好的LLM在共享任务数据集 (子任务1) 上获得77%的宏观F1得分.

结论:

语言模型的特定任务微调,如EntityBERT,对于从临床叙述中提取SACT时间表非常有效.
这种方法在这个专业任务上优于通用LLM.
这些发现有助于推进从EMR中提取自动化治疗时间表.

关键词:

电子医疗记录电子医疗记录大型语言模型.自然语言处理自然语言处理.系统性抗癌疗法系统性抗癌疗法处理时间表提取处理时间表提取

更多相关视频

Generation of Comprehensive Thoracic Oncology Database - Tool for Translational Research

Generation of Comprehensive Thoracic Oncology Database - Tool for Translational Research

Published on: January 22, 2011

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

相关概念视频

Cancer Survival Analysis

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...

Combination Therapies and Personalized Medicine

Combination Therapies and Personalized Medicine

Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...

Targeted Cancer Therapies

Targeted Cancer Therapies

The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
There are several types of targeted therapies against...