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A neural network approach to predict opioid misuse among previously hospitalized patients using electronic health records.
Vega, Lucas; Conneen, Winslow; Veronin, Michael A; Schumaker, Robert P.
Afiliación
  • Vega L; Data Analytics Lab, The University of Texas at Tyler, Tyler, Texas, United States of America.
  • Conneen W; Data Analytics Lab, The University of Texas at Tyler, Tyler, Texas, United States of America.
  • Veronin MA; Pharmaceutical Sciences Department, The University of Texas at Tyler, Tyler, Texas, United States of America.
  • Schumaker RP; Computer Science Department, The University of Texas at Tyler, Tyler, Texas, United States of America.
PLoS One ; 19(8): e0309424, 2024.
Article en En | MEDLINE | ID: mdl-39197006
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Redes Neurales de la Computación / Registros Electrónicos de Salud / Analgésicos Opioides / Trastornos Relacionados con Opioides Límite: Humans / Male Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Redes Neurales de la Computación / Registros Electrónicos de Salud / Analgésicos Opioides / Trastornos Relacionados con Opioides Límite: Humans / Male Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos