Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Regression Toward the Mean

Regression Toward the Mean

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...

You might also read

Related Articles

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

Sort by

Same author

Racial and Ethnic Disparities in Healthcare Utilization Among Youth in the Child Welfare System.

Children and youth services review·2026

Same author

Corrigendum to "Early childhood risk of physical abuse with isolated subconjunctival hemorrhages" [Child Abuse & Neglect (2026) 1-11 (108104)].

Child abuse & neglect·2026

Same author

Performance and Yield of Retinal Examinations in Cases of Suspected Physical Abuse Without Intracranial Injury.

Pediatric emergency care·2026

Same author

Fast MRI for Pediatric Traumatic Brain Injury: What Findings Are Missed Compared with Routine MRI?

AJNR. American journal of neuroradiology·2026

Same author

Child Opportunity Index and Youth Mental Health Emergency Department Visits.

JAMA pediatrics·2026

Same author

Early childhood risk of physical abuse with isolated subconjunctival hemorrhages.

Child abuse & neglect·2026

Search research articles

Related Experiment Video

Updated: May 3, 2026

A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers

A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers

Published on: April 21, 2017

Pilot study using machine learning to improve estimation of physical abuse prevalence.

Farah W Brink¹, Charmaine B Lo², Steven W Rust³

¹Nationwide Children's Hospital, 700 Childrens Drive, Columbus, OH 43205, United States; The Ohio State University College of Medicine, 370 West Ninth Avenue, Columbus, OH 43210, United States.

Child Abuse & Neglect

|February 18, 2024

Summary

Machine learning models can improve the accuracy of identifying child abuse using International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes. This approach enhances abuse detection beyond traditional coding methods.

Keywords:

Child abuse Child maltreatment International classification of diseases codes Machine learning Pediatric health information system Physical abuse

More Related Videos

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity

Published on: March 7, 2019

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

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

Published on: May 15, 2020

Related Experiment Videos

Last Updated: May 3, 2026

A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers

A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers

Published on: April 21, 2017

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity

Published on: March 7, 2019

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

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

Published on: May 15, 2020

Area of Science:

Medical Informatics
Pediatric Emergency Medicine
Machine Learning in Healthcare

Background:

International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes often underestimate physical abuse prevalence.
Machine learning (ML) offers potential for more accurate abuse estimation by processing diverse data.

Purpose of the Study:

To demonstrate the feasibility of using ML to identify ICD-10-CM codes associated with child abuse.
To develop a proof-of-concept model for abuse detection.

Main Methods:

A LASSO logistic regression model was developed using ICD-10-CM codes and patient age.
Data from children under 5 years old, evaluated by a child protection team (CPT) between 2016-2020, were analyzed.
Model performance was assessed using cross-validation (CV) and Receiver Operator Characteristic (ROC) curves.

Main Results:

The ML model achieved a mean CV AUC of 0.87 in identifying confirmed physical abuse (PA) using diagnosis codes and age.
Model performance was slightly lower (mean CV AUC = 0.81) for cases lacking specific abuse ICD-10-CM codes.

Conclusions:

A model utilizing ICD-10-CM codes and age can enhance the accuracy of distinguishing abusive from non-abusive injuries.
This pilot study represents a foundational step toward improving population-level abuse estimates.