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相关概念视频

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

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相关实验视频

Updated: Jun 29, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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使用基于共识的推理和大型语言模型从外科病理报告中提取结构化数据.

Aakash Tripathi1, Asim Waqas2, Kavya Venkatesan1

  • 1Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL.

Laboratory investigation; a journal of technical methods and pathology
|December 18, 2025
PubMed
概括

一个使用多个大型语言模型 (LLM) 的新框架准确地从病理学报告中提取癌症数据. 这种方法改善了用于癌症分期和治疗计划的数据分析.

关键词:
癌症注册中心 癌症注册中心提取 提取 提取大型语言模型 (LLM)自然语言处理 (NLP) 是一种自然语言处理.推理 推理 推理 推理手术病理学报告 报告

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科学领域:

  • 医疗信息学 医疗信息学
  • 计算病理学计算病理学
  • 人工智能在医学中的应用

背景情况:

  • 手术病理学报告包含关键的癌症诊断信息,但在格式和风格上有很大差异.
  • 这些报告的非结构化性质阻碍了用于大规模分析的自动数据提取.
  • 跨瘤类型和机构的变化为一致的数据检索带来了重大挑战.

研究的目的:

  • 从病理学报告中提取标准诊断变量和生物标志物的共识驱动,基于推理的框架.
  • 适应本地部署的大型语言模型 (LLM) 进行准确可靠的数据提取.
  • 评估框架在不同器官系统和癌症类型中的表现.

主要方法:

  • 利用多个本地部署的大型语言模型 (LLM) 来提取诊断变量 (位置,组织学,阶段,等级,行为) 和生物标志物.
  • 采用了三个独立的推理模型来评估LLM产生的输出的准确性和连贯性.
  • 汇总的输出以确定最终的共识值,并由董事会认证的病理学家进行专家验证.

主要成果:

  • 该框架在从超过6100份癌症基因组图谱 (TCGA) 报告 (平均84.9%±7.3%) 和510份莫菲特癌症中心报告 (平均88.2%±7.2%) 中提取标准变量时取得了高准确度.
  • 组织学,部位和行为显示出最高的提取精度,专家审查证实了关键变量之间的强烈一致.
  • 生物标志物提取实现了70.6%±7.9%的整体准确度,特定的生物标志物在相关瘤类型中表现高.

结论:

  • 在基于共识的框架内,本地部署的LLM提供了用于病理学数据提取的透明,准确和可审计的解决方案.
  • 该框架展示了将其整合到现实世界工作流程中的潜力,例如综合报告和癌症注册表抽象.
  • 多层次,多器官评估框架与多评估者共识对于临床应用中的LLM的基准测试至关重要.