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相关概念视频

Classification of Illness01:17

Classification of Illness

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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
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Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Uncertainty: Overview00:59

Uncertainty: Overview

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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相关实验视频

Updated: Jan 11, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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不确定性意识的大语言模型用于可解释的疾病诊断.

Shuang Zhou1, Jiashuo Wang2, Zidu Xu3

  • 1Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN, USA.

NPJ digital medicine
|November 18, 2025
PubMed
概括

ConfiDx是一种人工智能模型,可以识别和解释医学中的诊断不确定性. 这种先进的系统提高了诊断准确度,并帮助临床医生在面对不明确的患者病例时做出更好的决策.

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科学领域:

  • 人工智能在医学中的应用
  • 临床决策支持系统 临床决策支持系统
  • 医疗信息学 医疗信息学

背景情况:

  • 使用人工智能的可解释性疾病诊断显示出临床前景,但由于患者证据不足,与诊断不确定性作斗争.
  • 人工智能系统中诊断不确定性的明确识别和解释尚未得到充分探索,增加了误诊风险.

研究的目的:

  • 引入ConfiDx,一种不确定性意识的大型语言模型 (LLM),旨在解决临床环境中的诊断不确定性.
  • 将不确定性意识诊断的任务正式化,并创建反映诊断模两可的注释数据集.

主要方法:

  • 开发了ConfiDx,这是一个精心调整的LLM,用于不确定性意识诊断的诊断标准.
  • 精选丰富的注释数据集,不同程度的诊断模两可.
  • 在现实世界的临床数据集上评估ConfiDx.

主要成果:

  • ConfiDx在识别诊断不确定性和改善诊断性能方面表现出色.
  • 该模型为诊断和不确定性产生了可靠的解释.
  • 与独立专家相比,ConfiDx辅助的专家在不确定性识别 (10.7%) 和解释 (26%) 上显著改善.

结论:

  • ConfiDx有效地解决了人工智能驱动的诊断系统中诊断不确定性的挑战.
  • 该模型提高了人工智能产生的诊断的可信性和可解释性.
  • 通过帮助专家识别和解释不确定性,ConfiDx具有改善临床决策的巨大潜力.