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Related Concept Videos

Survival Tree01:19

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Post-traumatic stress disorder (PTSD) is a psychiatric condition that arises following exposure to traumatic events such as natural disasters, forced displacement, or severe accidents. It significantly impairs individuals' ability to cope with daily activities and disrupts their emotional and psychological equilibrium.
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Predicting PTSD development with early post-trauma assessments: a proof-of-concept for a concise tree-based

Chia-Hao Shih1, Elyssa Charlotte Feuer2, Ben Kurzion3

  • 1Department of Emergency Medicine, University of Toledo, Toledo, OH, USA.

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|February 18, 2025
PubMed
Summary
This summary is machine-generated.

A new Classification and Regression Trees (CART) model efficiently predicts posttraumatic stress disorder (PTSD) development using only three questions, offering a promising tool for early PTSD detection in trauma survivors.

Keywords:
Acute traumaCART frameworkPTSDTEPTTrauma agudoaprendizaje automáticomachine learningmarco CARTmodelado predictivopredictive modelling

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Area of Science:

  • Psychiatry
  • Computational Neuroscience
  • Clinical Psychology

Background:

  • Traumatic events affect a majority globally, with significant risk of developing posttraumatic stress disorder (PTSD).
  • Effective PTSD prevention and treatment strategies are critical due to its prevalence and impact.

Purpose of the Study:

  • To develop a concise, tree-based adaptive test using the Classification and Regression Trees (CART) framework for predicting PTSD development.
  • To assess the feasibility of using a minimal set of questions for early PTSD risk identification.

Main Methods:

  • Adult trauma survivors (n=143) from a longitudinal neuroimaging study were analyzed.
  • Data included demographic, trauma-related, and clinical symptom features collected within two weeks post-trauma.
  • CART model performance was compared against Random Forest (RF) and Gradient Boosting (GB) machine learning algorithms.

Main Results:

  • The CART model, using only three questions, achieved predictive performance comparable to RF and GB models.
  • CART accuracy was 0.641 and AUC was 0.663, demonstrating high efficiency.
  • The model's conciseness suggests practical utility for early PTSD detection.

Conclusions:

  • The CART framework offers a streamlined and efficient method for predicting PTSD onset in trauma survivors.
  • Further validation and refinement are needed to enhance predictive accuracy and establish broader clinical utility for early intervention.