@inproceedings{a1ffcccfa51949c690af76d1a71d77d0,
title = "Using big data to predict outcomes of opioid treatment programs",
abstract = "Potential of big data analytics in analyzing outcomes of opioid treatment programs (OTP) has not been fully explored. The goal of this study was to assess potential of big data in predicting OTP outcomes based on the initial intake forms which includes demographics, social and health history. The analytical sample comprised over 30,000 people admitted in OTP. Around 66% of patients reported improvements after completing OTP. We compared the results of Logistics Regression, Random Forest, and XGBoost for predictive modeling. XGBoost with sampling and threshold tuning performed the best (44% F1 score) with over 60% accuracy. Further big data exploration of OTP is warranted.",
keywords = "Big Data Analytics, Machine Learning, Opioid Treatment Program",
author = "Wanting Cui and Keren Bachi and Yasmin Hurd and Joseph Finkelstein",
note = "Publisher Copyright: {\textcopyright} 2020 The authors and IOS Press.",
year = "2020",
doi = "10.3233/SHTI200571",
language = "English",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press",
pages = "366--369",
editor = "John Mantas and Arie Hasman and Househ, {Mowafa S.} and Parisis Gallos and Emmanouil Zoulias",
booktitle = "THE IMPORTANCE OF HEALTH INFORMATICS IN PUBLIC HEALTH DURING A PANDEMIC",
address = "United States",
}