Forecasting Opioid Incidents for Rapid Actionable Data for Opioid Response in Kentucky

We present efforts in the fields of machine learning and time series forecasting to accurately predict counts of future opioid overdose incidents recorded by Emergency Medical Services (EMS) in the state of Kentucky. Forecasts are useful to state government agencies to properly prepare and distribute resources related to opioid overdoses effectively. Our approach uses county and district level aggregations of EMS opioid overdose encounters and forecasts future counts for each month. A variety of additional covariates were tested to determine their impact on the model's performance. Models with different levels of complexity were evaluated to optimize training time and accuracy. Our results show that when special precautions are taken to address data sparsity, useful predictions can be generated with limited error by utilizing yearly trends and covariance with additional data sources.

CLASSify: A Web-Based Tool for Machine Learning

Machine learning classification problems are widespread in bioinformatics, but the technical knowledge required to perform model training, optimization, and inference can prevent researchers from utilizing this technology. This article presents an automated tool for machine learning classification problems to simplify the process of training models and producing results while providing informative visualizations and insights into the data. This tool supports both binary and multiclass classification problems, and it provides access to a variety of models and methods. Synthetic data can be generated within the interface to fill missing values, balance class labels, or generate entirely new datasets. It also provides support for feature evaluation and generates explainability scores to indicate which features influence the output the most. We present CLASSify, an open-source tool for simplifying the user experience of solving classification problems without the need for knowledge of machine learning.