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Causality in Epidemiology01:21

Causality in Epidemiology

1.8K
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...
1.8K
Patient-centered Care01:13

Patient-centered Care

3.2K
Patient-centered care involves delivering care beyond inpatient hospitalization. Reflective practice can enhance a patient-centered approach. Reflective practice is a process of reasoning that considers all aspects of the present situation, including practicalities, learning from personal practice, and consideration of patient needs. Patients appreciate care decisions made while considering their input. Involving the patient in their care provides the patient with a sense of contribution rather...
3.2K
Decision Making01:20

Decision Making

1.1K
Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
1.1K
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

489
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...
489

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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因果グラフを通じた解釈可能な臨床的意思決定のための生成AIの活用

Mehmet Eren Ahsen1, Rand Kittani2, Travis Gerke3

  • 1Gies College of Business.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 27, 2026
PubMed
まとめ

生成AIは、臨床AIのための解釈可能な構造的因果モデル(SCM)を作成し、因果推論を改善します。これらのAI主導のSCMは、COVID-19治療効果の推定において人間の専門家と同等のパフォーマンスを示します。

キーワード:
生成AI構造的因果モデル臨床AI解釈可能性因果推論COVID-19

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