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相关概念视频

Counterfactual Thinking01:19

Counterfactual Thinking

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Counterfactual thinking is a cognitive process wherein individuals mentally reconstruct alternative versions of past events, often beginning with “what if” or “if only.” This reflective mechanism plays a significant role in shaping emotional experiences and guiding future behavior. Though typically triggered by unfavorable or unexpected outcomes, counterfactual thinking can also emerge in mundane, everyday decisions and experiences, revealing its deep entrenchment in...
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Data Collection I01:30

Data Collection I

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Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of...
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Causality in Epidemiology01:21

Causality in Epidemiology

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Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
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Introduction To Survival Analysis01:18

Introduction To Survival Analysis

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Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
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The Availability Heuristic01:08

The Availability Heuristic

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A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
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Purpose of Health Records II01:19

Purpose of Health Records II

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Health records serve various essential purposes in the healthcare system. Here are some key purposes:
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相关实验视频

Updated: Feb 24, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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从现实世界的数据生成假的患者时间表.

Yu Akagi1, Tomohisa Seki2, Toru Takiguchi2

  • 1Department of Biomedical Informatics, Graduate School of Medicine, The University of Tokyo, Japan.

AMIA ... Annual Symposium proceedings. AMIA Symposium
|February 23, 2026
PubMed
概括

一个先进的AI模型产生现实的患者健康轨迹,用于探索假设的场景. 这一突破有助于个性化医疗和in-silico试验,通过高精度模拟临床结果.

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科学领域:

  • 人工智能在医学中的应用
  • 计算生物学 计算生物学
  • 医疗信息学 医疗信息学

背景情况:

  • 反事实模拟对于个性化医学和in-silico试验至关重要.
  • 目前,方法上的局限性阻碍了有效的反事实模拟.

研究的目的:

  • 开发和验证一种自回归生成模型,用于临床上可信的反事实模拟.
  • 评估模型能够复制已知的临床模式的能力.

主要方法:

  • 在一个大数据集上训练了一种自回归生成模型 (超过30万名患者,4亿条时间线条目).
  • 将该模型应用于COVID-19患者,通过改变年龄,C反应蛋白 (CRP) 和血清肌酸氨酸来模拟结果.
  • 根据已知的临床模式验证的反事实轨迹.

主要成果:

  • 该模型产生了临床上可信的反事实患者轨迹.
  • 模拟显示,随着年龄的增长,死亡率增加,CRP升高,血清肌氨酸升高.
  • 根据CRP和脏功能预测雷梅西维尔处方的预测变化.

结论:

  • 自动回归生成模型可以有效地执行反事实临床模拟.
  • 基于真实世界的数据进行自我监督学习为先进的临床建模提供了基础.
  • 这种方法支持个性化医疗和in-silico试验开发.