A neural network approach to predict opioid misuse among previously hospitalized patients using electronic health records

Lucas Vega, Winslow Conneen, Michael A Veronin and Robert P Schumaker

PLOS ONE, 2024, vol. 19, issue 8, 1-15

Abstract: Can Electronic Health Records (EHR) predict opioid misuse in general patient populations? This research trained three backpropagation neural networks to explore EHR predictors using existing patient data. Model 1 used patient diagnosis codes and was 75.5% accurate. Model 2 used patient prescriptions and was 64.9% accurate. Model 3 used both patient diagnosis codes and patient prescriptions and was 74.5% accurate. This suggests patient diagnosis codes are best able to predict opioid misuse. Opioid misusers have higher rates of drug abuse/mental health disorders than the general population, which could explain the performance of diagnosis predictors. In additional testing, Model 1 misclassified only 1.9% of negative cases (non-abusers), demonstrating a low type II error rate. This suggests further clinical implementation is viable. We hope to motivate future research to explore additional methods for universal opioid misuse screening.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0309424

DOI: 10.1371/journal.pone.0309424

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