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Blinding01:11

Blinding

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Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control trial or RCT. It reduces the bias that affects the results. In an RCT, blinding is used in the form of a placebo. A placebo effect occurs when untreated subjects falsely believe they have received the treatment and report improved symptoms. A placebo or a dummy treatment is administered to subjects to negate the bias caused by such an effect.
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Halo Effect01:27

Halo Effect

552
The halo effect is a cognitive bias in which an individual's overall impression influences judgments about their specific traits. This psychological phenomenon leads people to associate positive characteristics with those they perceive as generally good and negative characteristics with those they view as bad. This effect is particularly influential in social perception, professional evaluations, and decision-making processes.The Psychological Basis of the Halo EffectThe halo effect is rooted...
552
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

459
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
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臨床人工知能におけるマルチ敵対的デバイアス

Md Rahat Shahriar Zawad1, Irene Y Chen2,3, Peter Washington3

  • 1University of Hawaii at Manoa, USA.

AMIA ... Annual Symposium proceedings. AMIA Symposium
|February 23, 2026
PubMed
まとめ
この要約は機械生成です。

この研究では、複数の公平性指標を同時に最適化することにより、臨床機械学習の公平性を向上させるためのマルチ敵対的デバイアスフレームワークを紹介します。新しい手法は、モデルのパフォーマンスを維持しながら、人口統計学的公平性と不当な誤診を効果的に低減します。

キーワード:
臨床人工知能公平性機械学習デバイアス敵対的学習

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