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Videos de Conceptos Relacionados

The Uncertainty Principle04:08

The Uncertainty Principle

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Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
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Uncertainty in Measurement: Reading Instruments02:46

Uncertainty in Measurement: Reading Instruments

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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
53.2K
Uncertainty: Overview00:59

Uncertainty: Overview

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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Uncertainty in Measurement: Significant Figures03:34

Uncertainty in Measurement: Significant Figures

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All the digits in a measurement, including the uncertain last digit, are called significant figures or significant digits. Note that zero may be a measured value; for example, if a scale that shows weight to the nearest pound reads “140,” then the 1 (hundreds), 4 (tens), and 0 (ones) are all significant (measured) values.
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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Video Experimental Relacionado

Updated: Feb 8, 2026

Non-restraining EEG Radiotelemetry: Epidural and Deep Intracerebral Stereotaxic EEG Electrode Placement
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Incertidumbre en el aprendizaje profundo para EEG bajo cambios de conjunto de datos

Mats Tveter1, Thomas Tveitstøl1, Christoffer Hatlestad-Hall2

  • 1Department of Neurology, Oslo University Hospital, Oslo, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway.

Artificial intelligence in medicine
|February 6, 2026
PubMed
Resumen

Los métodos de aprendizaje de conjunto mejoran la precisión diagnóstica de la inteligencia artificial (IA) y la estimación de la incertidumbre para datos de electroencefalografía (EEG). Estos modelos de IA predicen de manera fiable el deterioro cognitivo, incluso con cambios en los datos, lo que aumenta la confianza clínica.

Palabras clave:
IAAprendizaje profundoEEGMLCambiosIncertidumbre

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Sus antecedentes:

  • La integración de la IA en el diagnóstico médico requiere estimaciones de incertidumbre fiables junto con predicciones precisas.
  • La comprensión de la confianza en la predicción es crucial para la toma de decisiones clínicas, especialmente en escenarios fuera de distribución.
  • Las métricas de incertidumbre tienen como objetivo alinear la confianza del modelo con el rendimiento real, adaptándose a los cambios de fiabilidad.

Conclusiones:

  • Las estrategias de aprendizaje de conjunto mejoran la fiabilidad y la precisión de los modelos de IA para la detección del deterioro cognitivo basada en EEG.
  • Estos hallazgos respaldan la implementación clínica de la IA al garantizar la transparencia y la robustez ante la variabilidad de los datos.
  • Los modelos conscientes de la incertidumbre son esenciales para aplicaciones de IA seguras y confiables en la atención médica.