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Related Concept Videos

Survival Tree01:19

Survival Tree

60
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.
 Building a Survival Tree
Constructing a...
60

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Dynamic Visual Tests to Identify and Quantify Visual Damage and Repair Following Demyelination in Optic Neuritis Patients
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Predictive model for converting optic neuritis to multiple sclerosis; decision tree in focus.

Saeid Rasouli1, Mohammad Sedigh Dakkali2, Azim Ghazvini3

  • 1Five Senses Health Research Institute, School of Medicine, Hazrat-e Rasool General Hospital, Iran University of Medical Sciences, Tehran, Iran.

Plos One
|December 2, 2024
PubMed
Summary
This summary is machine-generated.

This study developed a decision tree model to predict multiple sclerosis (MS) risk in optic neuritis (ON) patients. Magnetic resonance imaging (MRI) lesions and ON type were key predictors, aiding early diagnosis.

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Area of Science:

  • Neuroscience
  • Ophthalmology
  • Medical Informatics

Background:

  • Optic neuritis (ON) is a frequent initial symptom of multiple sclerosis (MS).
  • Accurate prediction of MS development in ON patients is crucial for timely intervention.
  • Identifying at-risk individuals aids in early diagnosis and management.

Purpose of the Study:

  • To develop a practical predictive model for identifying optic neuritis patients at high risk of developing multiple sclerosis.
  • To leverage clinical and imaging data for early MS risk stratification.
  • To provide physicians with a tool for informed decision-making in ON management.

Main Methods:

  • Utilized data from the Optic Neuritis Treatment Trial (457 patients, aged 18-46).
  • Developed a Decision Tree (DT) classifier, optimizing hyperparameters for performance.
  • Employed SHapley Additive exPlanations (SHAP) for feature importance analysis.

Main Results:

  • 154 out of 388 completers developed clinically definite multiple sclerosis (CDMS).
  • Magnetic resonance imaging (MRI) lesions were present in 61% of CDMS patients.
  • The DT model achieved 70.1% cross-validation accuracy; MRI lesions (61%) and ON type (18%) were most significant predictors.

Conclusions:

  • The developed decision tree model demonstrates satisfactory performance in stratifying MS risk for ON patients.
  • Baseline findings, particularly MRI lesions and ON type, are critical for predicting MS development.
  • The model offers valuable insights for physicians to guide clinical decision-making in managing ON patients.