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A Comparative Effectiveness Study on Opioid Use Disorder Prediction Using Artificial Intelligence and Existing Risk

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    Summary

    Artificial intelligence (AI) models can predict opioid use disorder (OUD) more effectively than traditional clinical tools. This AI approach aids in identifying at-risk patients for better management and care.

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    Area of Science:

    • Medical Informatics
    • Artificial Intelligence in Healthcare
    • Public Health

    Background:

    • Opioid use disorder (OUD) is a significant cause of mortality in the U.S., imposing substantial burdens on individuals, families, and healthcare systems.
    • Artificial intelligence (AI) offers potential for developing automated prediction tools for OUD by leveraging available healthcare data.

    Purpose of the Study:

    • To develop and evaluate AI-based models for predicting OUD.
    • To compare the predictive performance of AI models against existing clinical tools, including the unweighted Opioid Risk Tool (ORT).

    Main Methods:

    • A retrospective study utilizing a dataset of 474,208 patients over 10 years.
    • Development of various AI models, including a transformer-based model, for OUD prediction.
    • Comparison of AI model performance (AUC) against logistic regression, random forest, xgboost, LSTM, transformer, and the unweighted ORT model on 100 test sets (47,396 patients).

    Main Results:

    • The proposed transformer-based AI model achieved a higher predictive accuracy (AUC = 0.742 ± 0.021) compared to other AI models and the unweighted ORT (AUC = 0.559 ± 0.025).
    • AI models, particularly the transformer-based approach, demonstrated superior performance in predicting OUD compared to established clinical methods.

    Conclusions:

    • AI algorithms show significant promise in enhancing the prediction of OUD.
    • Integrating AI into clinical practice can assist healthcare providers in risk stratification and managing patients undergoing opioid therapy.