Abstract
Can Electronic Health Records (EHR) predict opioid misuse in general patient populations? This research trained three back propagation 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.
Description
© 2024 Vega et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited (http://creativecommons.org/licenses/by/4.0/).
Publisher
PLOS One
Date of publication
8-2024
Language
english
Persistent identifier
http://hdl.handle.net/10950/4826
Document Type
Article
Recommended Citation
Vega, Lucas; Conneen, Winslow; Veronin, Michael A.; and Schumaker, Robert P., "A neural network approach to predict opioid misuse among previously hospitalized patients using electronic health records" (2024). Computer Science Faculty Publications and Presentations. Paper 32.
http://hdl.handle.net/10950/4826
Publisher Citation
Vega L, Conneen W, Veronin MA, Schumaker RP (2024) A neural network approach to predict opioid misuse among previously hospitalized patients using electronic health records. PLoS ONE 19(8): e0309424. https://doi.org/10.1371/journal.pone.0309424
