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Updated: Jul 9, 2025

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使用观察性重症监护病房数据进行因果推断:对未来实践进行范围审查和建议

J M Smit1,2, J H Krijthe3, W M R Kant4

  • 1Department of Intensive Care, Erasmus University Medical Center, Rotterdam, The Netherlands. j.smit@erasmusmc.nl.

NPJ digital medicine
|November 27, 2023
PubMed
概括

本次审查强调了人工智能 (AI) 强大的因果推理模型对于临床决策的关键需求. 在重症监护室 (ICU) 进行因果推理研究的报告标准的改进对于开发可靠的人工智能工具至关重要.

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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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Data Collection by Observations

Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
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Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
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