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関連する概念動画

Counterfactual Thinking01:19

Counterfactual Thinking

296
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...
8.8K
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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リアルワールドデータからカウンターファクチュアルな患者タイムラインを生成する

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
まとめ

高度なAIモデルが、仮説シナリオを探求するためのリアルな患者健康軌道を生成します。この画期的な技術は、臨床転帰を高精度でシミュレートすることにより、個別化医療とインシリコトライアルを支援します。

キーワード:
カウンターファクチュアルシミュレーション個別化医療インシリコトライアル患者タイムライン生成AI機械学習深層学習リアルワールドデータ臨床転帰予測電子カルテ

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科学分野:

  • 医療における人工知能;計算生物学;ヘルスインフォマティクス

背景:

  • カウンターファクチュアルシミュレーションは、個別化医療とインシリコトライアルにとって重要です。方法論的な制限は、現在、効果的なカウンターファクチュアルシミュレーションを妨げています。

研究 の 目的:

  • 臨床的に妥当なカウンターファクチュアルシミュレーションのための自己回帰生成モデルを開発および検証すること。既知の臨床パターンを再現するモデルの能力を評価すること。

主な方法:

  • 大規模データセット（300,000人以上の患者、4億のタイムラインエントリ）で自己回帰生成モデルをトレーニングしました。モデルをCOVID-19患者に適用し、年齢、C反応性タンパク質（CRP）、血清クレアチニンを変化させて転帰をシミュレートしました。既知の臨床パターンに対してカウンターファクチュアルな軌道を検証しました。

主要な成果:

  • モデルは臨床的に妥当なカウンターファクチュアルな患者の軌道を生成しました。シミュレーションでは、高齢、CRPの上昇、血清クレアチニン値の上昇に伴う死亡率の増加が示されました。CRPと腎機能に基づいてレムデシビル処方の予測される変化。

結論:

  • 自己回帰生成モデルは、カウンターファクチュアルな臨床シミュレーションを効果的に実行できます。リアルワールドデータでの自己教師あり学習は、高度な臨床モデリングの基盤を提供します。このアプローチは、個別化医療とインシリコトライアルの開発をサポートします。