Browsing by Author "Ray, Brad"

Now showing 1 - 3 of 3
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    Evaluation of the Indianapolis Mobile Crisis Assistance Team
    (2018) Bailey, Katie; Ray, Brad; Grommon, Eric; Lowder, Evan; Rising Paquet, Staci
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    Point process modeling of drug overdoses with heterogeneous and missing data
    (Institute of Mathematical Statistics, 2021) Liu, Xueying; Carter, Jeremy; Ray, Brad; Mohler, George; Computer and Information Science, School of Science
    Opioid overdose rates have increased in the United States over the past decade and reflect a major public health crisis. Modeling and prediction of drug and opioid hotspots, where a high percentage of events fall in a small percentage of space–time, could help better focus limited social and health services. In this work we present a spatial-temporal point process model for drug overdose clustering. The data input into the model comes from two heterogeneous sources: (1) high volume emergency medical calls for service (EMS) records containing location and time but no information on the type of nonfatal overdose, and (2) fatal overdose toxicology reports from the coroner containing location and high-dimensional information from the toxicology screen on the drugs present at the time of death. We first use nonnegative matrix factorization to cluster toxicology reports into drug overdose categories, and we then develop an EM algorithm for integrating the two heterogeneous data sets, where the mark corresponding to overdose category is inferred for the EMS data and the high volume EMS data is used to more accurately predict drug overdose death hotspots. We apply the algorithm to drug overdose data from Indianapolis, showing that the point process defined on the integrated data out-performs point processes that use only coroner data (AUC improvement 0.81 to 0.85). We also investigate the extent to which overdoses are contagious, as a function of the type of overdose, while controlling for exogenous fluctuations in the background rate that might also contribute to clustering. We find that drug and opioid overdose deaths exhibit significant excitation with branching ratio ranging from 0.72 to 0.98.
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    SOS-EW: System for Overdose Spike Early Warning Using Drug Mover’s Distance-Based Hawkes Processes
    (Springer, 2020) Chiang, Wen-Hao; Yuan, Baichuan; Li, Hao; Wang, Bao; Bertozzi, Andrea; Carter, Jeremy; Ray, Brad; Mohler, George; Computer and Information Science, School of Science
    Opioid addictions and overdoses have increased across the U.S. and internationally over the past decade. In urban environments, overdoses cluster in space and time, with 50% of overdoses occurring in less than 5% of the city and dozens of calls for emergency medical services being made within a 48-hour period. In this work, we introduce a system for early detection of opioid overdose clusters based upon the toxicology report of an initial event. We first use drug SMILES, one hot encoded molecular substructures, to generate a bag of drug vectors corresponding to each overdose (overdoses are often characterized by multiple drugs taken at the same time). We then use spectral clustering to generate overdose categories and estimate multivariate Hawkes processes for the space-time intensity of overdoses following an initial event. As the productivity parameter of the process depends on the overdose category, this allows us to estimate the magnitude of an overdose spike based on the substances present (e.g. fentanyl leads to more subsequent overdoses compared to Oxycontin). We validate the model using opioid overdose deaths in Indianapolis and show that the model outperforms several recently introduced Hawkes-Topic models based on Dirichlet processes. Our system could be used in combination with drug test strips to alert drug using populations of risky batches on the market or to more efficiently allocate naloxone to users and health/social workers.