Predicting Opioid Treatment Program Dropout with Machine Learning on Behavioral Health EMR’s
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I4P103Keywords:
Opioid Treatment Programs, Behavioral Health Electronic Medical Records, Machine Learning, Predictive ModelingAbstract
The opioid crisis continues to threaten global health, with addiction, relapses, and overdose deaths at record highs. The effectiveness of OTPs, which include buprenorphine treatment and methadone treatment, is compromised because the dropout rate of OTPs is abysmal, with a range of 60-85 percent. This study was primarily designed to predict outcomes in the Opioid Treatment Program (OTP), such as dropout from treatment, using Behavioral Health EMR data and machine learning strategies. We amalgamate data from diverse OTP projects across the nation to foster insights into unique opioid use disorders. The study achieves an ROC-AUC of 0.82 and demonstrates that machine learning algorithms deliver superior classification accuracy compared to classic statistical techniques, as the survey ultimately met the expectations of all involved. Costa C, who discusses collected features, notes that there exist many methods to compare, which enable us to create ensembled predictors. Necessary advice was provided for the utilization of various Machine Learning algorithms, including Neural Networks, Logistic Regression, Random Forests, and Gradient Boosting. Prior comprehensive methods were flawed and busy with the increasing problem of missing appointments for evaluations. In addition to these, any other models can predict a poor prognosis, allowing for the identification of factors associated with this in the database at the initial stage
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