Jove
Visualize
Contáctanos
JoVE
x logofacebook logolinkedin logoyoutube logo
ACERCA DE JoVE
Visión GeneralLiderazgoBlogCentro de Ayuda JoVE
AUTORES
Proceso de PublicaciónConsejo EditorialAlcance y PolíticasRevisión por ParesPreguntas FrecuentesEnviar
BIBLIOTECARIOS
TestimoniosSuscripcionesAccesoRecursosConsejo Asesor de BibliotecasPreguntas Frecuentes
INVESTIGACIÓN
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchivo
EDUCACIÓN
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualCentro de Recursos para ProfesoresSitio de Profesores
Términos y Condiciones de Uso
Política de Privacidad
Políticas

Videos de Conceptos Relacionados

External and Internal Respiration01:24

External and Internal Respiration

7.4K
External respiration occurs in the lungs, and it is the first step in the journey of oxygen inside the body. When we inhale, oxygen enters our lungs and diffuses across the thin alveolar membrane. The alveoli are tiny, air-filled sacs that provide a vast surface area for gas exchange. Oxygen in the alveoli has a higher partial pressure (105 mmHg) than in the adjacent pulmonary capillaries (40 mmHg), establishing a pressure gradient. As a result, oxygen molecules move from the alveoli into the...
7.4K
Internal and External Forces01:12

Internal and External Forces

16.2K
Newton's first law states that a net external force causes a change in motion. External forces act on an object or system, originating outside of the object or system. In contrast, internal forces originate inside the system of interest and do not lead to any acceleration. In simpler words, internal forces are forces that act on one part of an object and are exerted by another part of the same object. External forces are forces that act on an object due to some other object. Therefore, when...
16.2K
Reliability and Validity01:29

Reliability and Validity

13.8K
Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
13.8K
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

760
The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
760
Wind Turbine Machine Models01:24

Wind Turbine Machine Models

576
In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
576
Internal Energy02:00

Internal Energy

36.6K
The total of all possible kinds of energy present in a substance is called the internal energy (U), sometimes symbolized as E. Suppose a system with initial internal energy, Uinitial, undergoes a change in energy (transfer of work or heat), and the final internal energy of the system is Ufinal. Change in internal energy equals the difference between Ufinal and Uinitial.
36.6K

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por
Same author

Wafer-Level Self-Assembly and Interface Passivation Patterning Technology for Nanomaterial-Compatible 3D MEMS Sensing Chips.

Nano-micro letters·2026
Same author

Correction to "A Nanomaterial-Independent Biosensor Based on Gallium Arsenide High-Electron-Mobility Transistors for Rapid and Ultra-Sensitive Pathogen Detection".

ACS sensors·2025
Same author

A Nanomaterial-Independent Biosensor Based on Gallium Arsenide High-Electron-Mobility Transistors for Rapid and Ultra-Sensitive Pathogen Detection.

ACS sensors·2025
Same author

Bioinformatics analysis of circular RNAs associated with atrial fibrillation and their evaluation as predictive biomarkers.

Human genomics·2025
Same author

Ni/Fe-MOF Electrochemical Transistor Biosensors with 3D Debye Space for Ultrasensitive Detection of Coronavirus Nucleocapsid Protein.

Small (Weinheim an der Bergstrasse, Germany)·2025
Same author

Double-Phase Ga-Doped In<sub>2</sub>O<sub>3</sub> Nanospheres and Their Self-Assembled Monolayer Film for Ultrasensitive HCHO MEMS Gas Sensors.

Small (Weinheim an der Bergstrasse, Germany)·2025

Video Experimental Relacionado

Updated: Jan 27, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.4K

Modelo predictivo interpretable de aprendizaje automático para la desnutrición en pacientes posictus subagudos: un

Ping Sun1,2, Junqi Luan3, Guotao Duan1

  • 1Second Clinical Medical College, Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang Province, China.

Frontiers in nutrition
|January 26, 2026
PubMed
Resumen

Este estudio desarrolló un modelo de aprendizaje automático para predecir el riesgo de desnutrición en pacientes con ictus durante la rehabilitación. El modelo CatBoost (CAT) identifica con precisión a los pacientes que necesitan apoyo nutricional, mejorando la atención.

Palabras clave:
CATaprendizaje automáticoestudio multicéntricomodelo predictivofactores de riesgoictus subagudo

Más Videos Relacionados

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

503
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.7K

Videos de Experimentos Relacionados

Last Updated: Jan 27, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.4K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

503
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.7K

Área de la Ciencia:

  • Informática Médica
  • Aprendizaje Automático en Atención Médica
  • Ciencia de la Nutrición

Sus antecedentes:

  • La desnutrición es prevalente en pacientes con ictus durante la rehabilitación subaguda, lo que aumenta la mortalidad y los resultados adversos.
  • Las herramientas existentes para predecir el riesgo de desnutrición en esta población son limitadas.
  • La identificación temprana del riesgo de desnutrición es crucial para una intervención eficaz.

Objetivo del estudio:

  • Desarrollar y validar un modelo interpretable de aprendizaje automático (ML) para predecir el riesgo de desnutrición en pacientes con ictus en rehabilitación subaguda.
  • Crear una herramienta clínicamente útil para la estratificación temprana del riesgo de desnutrición.
  • Mejorar los resultados de los pacientes a través de intervenciones nutricionales oportunas.

Principales métodos:

  • Un estudio multicéntrico que involucra cohortes de desarrollo (n=802) y validación externa (n=345).
  • Selección de características utilizando regresión LASSO y el algoritmo Boruta.
  • Entrenamiento y evaluación de ocho modelos de ML, incluido CatBoost (CAT), utilizando validación cruzada y métricas como AUC, curvas de calibración y DCA.
  • Interpretabilidad evaluada mediante análisis SHAP.

Principales resultados:

  • El algoritmo CAT demostró un rendimiento superior, con AUC de 0.848 (desarrollo) y 0.772 (validación externa).
  • El modelo mostró buena calibración y utilidad clínica a través de DCA.
  • El análisis SHAP identificó la edad, la fuerza de agarre y el índice de Barthel (BI) como predictores clave de la desnutrición.

Conclusiones:

  • Se desarrolló y validó con éxito un modelo interpretable de ML (basado en CAT) para el cribado del riesgo de desnutrición en pacientes con ictus subagudo.
  • El modelo proporciona una herramienta clínicamente útil para la estratificación temprana del riesgo.
  • Esto facilita intervenciones nutricionales específicas y rehabilitación personalizada, mejorando potencialmente los resultados de los pacientes.