Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

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

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
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

Related Experiment Video

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

Decoding the adolescent non-suicidal self-injury: understanding with interpretable machine learning insights.

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
Summary
This summary is machine-generated.

Machine learning models effectively identify adolescent non-suicidal self-injury (NSSI) risk factors. Key elements include anxiety, depression, self-esteem, and interpersonal issues, refining the Integrated Theoretical Model.

Keywords:
Exploratory factor analysisIntegrated theoretical modelMachine learningNon-suicidal self-injurySHAP visualization

More Related Videos

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

Related Experiment Videos

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

Area of Science:

  • Adolescent psychology
  • Computational psychiatry
  • Behavioral science

Background:

  • Non-suicidal self-injury (NSSI) is a prevalent yet challenging adolescent risk behavior.
  • Early detection and intervention are crucial for mitigating NSSI's impact.
  • Understanding underlying risk and protective factors is essential for developing effective strategies.

Purpose of the Study:

  • To develop an interpretable machine learning classification model for adolescent NSSI.
  • To identify critical risk and protective factors associated with NSSI.
  • To evaluate these factors within the framework of the Integrated Theoretical Model.

Main Methods:

  • Data collected from 2989 adolescents in eastern China via questionnaires.
  • Six machine learning algorithms applied: KNN, SVM, Logistic Regression, LGBM, CatBoost, XGBoost.
  • SHAP visualization and exploratory factor analysis used to identify key factors.

Main Results:

  • CatBoost algorithm showed superior performance (AUPRC=0.736, AUC=0.863).
  • SHAP analysis highlighted 23 important items influencing NSSI.
  • Seven factors identified: Situational Anxiety, Depressive Symptoms, Positive Daily Functioning, Negative Self Esteem, Self-Appraisal of Behavior, Bullying and Reactive Aggression, and Interpersonal Problems and Self-Acceptance.

Conclusions:

  • Machine learning provides a robust approach to analyzing complex NSSI data.
  • Identified factors offer insights into refining the Integrated Theoretical Model for NSSI.
  • This study enhances understanding of adolescent NSSI, aiding targeted interventions.