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

Self-Presentation: Self-Monitoring and Self-Handicapping02:05

Self-Presentation: Self-Monitoring and Self-Handicapping

40.2K
People can go to great lengths to protect their self-image and present themselves in ways that they want others to see them. Sociologist Erving Goffman presented the idea that a person is like an actor on a stage. Calling his theory dramaturgy, Goffman believed that we use “impression management” to present ourselves to others as we hope to be perceived. Each situation is a new scene, and individuals perform different roles depending on who is present (Goffman, 1959). Think about...
40.2K
Regression Toward the Mean01:52

Regression Toward the Mean

6.5K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.5K
Self-Evaluation: Self-Enhancement and Self-Verification03:00

Self-Evaluation: Self-Enhancement and Self-Verification

5.3K
Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
5.3K

También podría leer

Artículos Relacionados

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

Ordenar por
Same author

Hit the bull's eye: Engineered extracellular vesicles for targeted therapy.

Bioactive materials·2026
Same author

Exploring the Psychological Mechanisms of Self-Aggression in Male Individuals with Substance Use Disorders: A Study Combining Interpretability Automated Machine Learning and Conditional Process Model.

Substance use & misuse·2026
Same author

Integrated pan cancer analysis with breast cancer validation identifies SEC13 homolog as prognostic biomarker and immunotherapy target.

Scientific reports·2026
Same author

The burden and forecast of major depressive disorder attributed to behavioral risk factors among adolescents and adults at global, regional and national levels from 1990 to 2050: A systematic analysis for GBD 2021.

Psychiatry research·2026
Same author

DNA tetrahedron-mediated vascular targeted delivery of astragaloside IV enhances distraction osteogenesis via PI3K/AKT/FOXO pathway.

Biomaterials·2026
Same author

A dual-action core-shell microneedle system restores mitochondrial function and accelerates healing in diabetic wounds.

Journal of nanobiotechnology·2026

Video Experimental Relacionado

Updated: Sep 9, 2025

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

8.3K

Decodificación de las autolesiones no suicidas de los adolescentes: comprensión con conocimientos interpretables de

Haojie Fu1,2, Mengmeng Zhang3, Shuran Yang4

  • 1Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Siping Road, Shanghai, 200092, Shanghai, China.

BMC public health
|September 1, 2025
PubMed
Resumen

Los modelos de aprendizaje automático identifican efectivamente los factores de riesgo de autolesiones no suicidas (NSSI) en los adolescentes. Los elementos clave incluyen ansiedad, depresión, autoestima y problemas interpersonales, refinando el Modelo Teórico Integrado.

Palabras clave:
Análisis exploratorio de factoresModelo teórico integradoAprendizaje automáticoAutolesiones no suicidasVisualización de SHAP

Más Videos Relacionados

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.2K

Videos de Experimentos Relacionados

Last Updated: Sep 9, 2025

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

8.3K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.2K

Área de la Ciencia:

  • Psicología adolescente
  • Psiquiatría computacional
  • Ciencias del comportamiento

Sus antecedentes:

  • La autolesión no suicida (NSSI) es un comportamiento de riesgo adolescente prevalente pero desafiante.
  • La detección temprana y la intervención son cruciales para mitigar el impacto de la NSSI.
  • La comprensión de los factores de riesgo y de protección subyacentes es esencial para desarrollar estrategias eficaces.

Objetivo del estudio:

  • Desarrollar un modelo de clasificación de aprendizaje automático para adolescentes.
  • Identificar los factores críticos de riesgo y de protección asociados con la NSSI.
  • Evaluar estos factores en el marco del modelo teórico integrado.

Principales métodos:

  • Datos recogidos de 2989 adolescentes en el este de China a través de cuestionarios.
  • Se aplicaron seis algoritmos de aprendizaje automático: KNN, SVM, Regresión logística, LGBM, CatBoost y XGBoost.
  • Visualización SHAP y análisis exploratorio de factores utilizados para identificar los factores clave.

Principales resultados:

  • El algoritmo CatBoost mostró un rendimiento superior (AUPRC=0,736, AUC=0,863).
  • El análisis SHAP destacó 23 elementos importantes que influyen en el NSSI.
  • Se identificaron siete factores: ansiedad situacional, síntomas depresivos, funcionamiento diario positivo, autoestima negativa, autoevaluación del comportamiento, acoso y agresión reactiva, y problemas interpersonales y autoaceptación.

Conclusiones:

  • El aprendizaje automático proporciona un enfoque sólido para analizar datos complejos de NSSI.
  • Los factores identificados ofrecen información sobre el perfeccionamiento del modelo teórico integrado para la NSSI.
  • Este estudio mejora la comprensión de la NSSI en adolescentes, ayudando a las intervenciones específicas.