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Predicting the Risk of Opioid-induced Respiratory Depression Using ChatGPT-4o and Machine Learning Techniques.

Mohammad Meshkini1,2,3, Sayed Masoud Hosseini1, Peyman Erfan Talab Evini1

  • 1Toxicological Research Center, Excellence Center of Clinical Toxicology, Department of Clinical Toxicology, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

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Summary
This summary is machine-generated.

This study developed a predictive model for opioid-induced respiratory depression using ChatGPT-4o. The model accurately identifies patients at high risk, improving early detection and intervention for opioid overdose.

Keywords:
Generative Artificial IntelligenceMachine learningOpiate overdosePrecision MedicineRespiratory insufficiency

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Area of Science:

  • Medical Informatics
  • Toxicology
  • Emergency Medicine

Background:

  • Opioid-induced respiratory depression is a critical, life-threatening complication of opioid overdose.
  • Early and accurate prediction of respiratory depression is crucial for timely intervention and improved patient outcomes.

Purpose of the Study:

  • To develop and validate a predictive model for opioid-induced respiratory depression risk.
  • To leverage advanced AI, specifically ChatGPT-4o, for a no-code predictive modeling approach.

Main Methods:

  • A retrospective cross-sectional study of 2,005 patients admitted for opioid overdose.
  • Data extraction from electronic medical records including demographics, clinical presentation, interventions, and outcomes.
  • Development of a predictive model using a no-code methodology with ChatGPT-4o assistance.

Main Results:

  • Opioid-induced respiratory depression was present in 18% of patients.
  • Key predictors identified: low oxygen saturation (SpO₂), low respiratory rate (RR), and increased heart rate (HR).
  • The predictive model demonstrated high accuracy (94.4%), recall (81.0%), and AUC (0.98) for respiratory depression.

Conclusions:

  • The study successfully identified critical clinical predictors for respiratory depression in opioid overdose.
  • Machine learning models, like the one developed with ChatGPT-4o, show significant potential for enhancing early detection and intervention strategies.