Work

Using Big Data to Predict Outcomes of Opioid Treatment Programs

Year: 2020

Type: book-chapter

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.

Source: Studies in health technology and informatics

DOI: 10.3233/shti200571

Authors: Cui Wanting, Bachi Keren, Hurd Yasmin, Finkelstein Joseph

Institution: Icahn School of Medicine at Mount Sinai

Language: English

Topic: Opioid Use Disorder Treatment

Subfield: Public Health, Environmental and Occupational Health

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funders: National Institutes of Health, National Institute on Drug Abuse