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Review and Preview01:10

Review and Preview

8.4K
In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...
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Review and Preview01:13

Review and Preview

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Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
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Intelligence01:27

Intelligence

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The term "intelligence" is complex because it refers to both behavior and individuals, and its interpretation varies across cultures. European Americans tend to link intelligence with reasoning and cognitive skills, while in Kenya, it is tied to responsible participation in family and social life. In Uganda, intelligence is seen as the ability to know the right actions and carry them out effectively, while the Iatmul people of Papua New Guinea associate it with the capacity to remember...
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Machines01:19

Machines

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
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Measures of Intelligence01:29

Measures of Intelligence

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Psychologists measure intelligence by using standardized tests that produce a score known as the intelligence quotient or IQ. To understand IQ tests, it's important to recognize the key principles behind their construction: validity, reliability, and standardization.
Validity refers to how well a test measures what it claims to measure. An intelligence test should accurately assess intelligence rather than another characteristic, like anxiety. Criterion validity is one way to evaluate this;...
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Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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Updated: Feb 8, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Inteligencia artificial y aprendizaje automático para la dosificación de warfarina de precisión: una revisión

Mohammadsadra Shamohammadi1, Mohammad Ali Nazari2, Seyedeh Mohadese Mosavi Mirkalaie3

  • 1Gastrointestinal and Liver Diseases Research Center, Iran University of Medical Sciences, Tehran, Iran.

European journal of clinical pharmacology
|February 6, 2026
PubMed
Resumen

La inteligencia artificial y el aprendizaje automático muestran la promesa de optimizar la dosificación de warfarina, mejorando potencialmente el control de la relación internacional normalizada (INR). Se necesita más investigación para confirmar su efectividad clínica en comparación con los métodos tradicionales.

Palabras clave:
AnticoagulaciónINRAprendizaje automáticoDosis de precisión informada por modelosFarmacogenómicaDosis de precisiónAprendizaje por refuerzoAntagonistas de la vitamina KWarfarina

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Área de la Ciencia:

  • Farmacogenómica
  • Biología Computacional
  • Farmacología Clínica

Sus antecedentes:

  • La warfarina es un anticoagulante ampliamente utilizado con un estrecho índice terapéutico, que requiere un estrecho seguimiento del INR.
  • Las desviaciones en la dosificación pueden provocar graves eventos tromboembólicos o hemorrágicos.

Objetivo del estudio:

  • Revisar y sintetizar la literatura sobre enfoques de aprendizaje automático (ML) para la individualización de la dosis de warfarina.
  • Evaluar el rendimiento predictivo y la relevancia clínica de los modelos de ML en la terapia con warfarina.

Principales métodos:

  • Revisión narrativa de estudios que emplean técnicas de aprendizaje automático (por ejemplo, regresión de vectores de soporte, redes neuronales, modelos de conjunto, aprendizaje por refuerzo) para la dosificación de warfarina.
  • Centrarse en modelos que incorporan factores clínicos y genéticos.

Principales resultados:

  • Los modelos de dosificación de warfarina basados en ML demuestran potencial para mejorar la predicción de dosis terapéuticas y la regulación del INR en comparación con los métodos tradicionales.
  • Los modelos de ML existentes a menudo sufren de tamaños de muestra pequeños, validación externa limitada y heterogeneidad metodológica, lo que afecta la generalización.

Conclusiones:

  • La inteligencia artificial (IA) y el ML ofrecen ventajas potenciales en la precisión de la dosificación de warfarina y el control del INR.
  • Las conclusiones definitivas sobre la efectividad comparativa requieren más estudios sólidos con tamaños de muestra más grandes y validación externa rigurosa.