{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T08:15:22Z","timestamp":1784621722108,"version":"3.55.0"},"reference-count":35,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T00:00:00Z","timestamp":1724803200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.1371\/journal.pone.0309424","type":"journal-article","created":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T18:23:18Z","timestamp":1724869398000},"page":"e0309424","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["A neural network approach to predict opioid misuse among previously hospitalized patients using electronic health records"],"prefix":"10.1371","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9500-6104","authenticated-orcid":true,"given":"Lucas","family":"Vega","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Winslow","family":"Conneen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael A.","family":"Veronin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert P.","family":"Schumaker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,8,28]]},"reference":[{"key":"pone.0309424.ref001","unstructured":"National Center for Drug Abuse Statistics. Opioid Epidemic: Addiction Statistics., 2021; Accessed: December 11, 2021, https:\/\/drugabusestatistics.org\/opioid-epidemic\/."},{"key":"pone.0309424.ref002","unstructured":"National Institute on Drug Abuse. What are prescription opioids? 2021; Accessed: March 22, 2024, https:\/\/nida.nih.gov\/publications\/drugfacts\/prescription-opioids."},{"key":"pone.0309424.ref003","unstructured":"National Library of Medicine. Opioid Use Disorder. 2024; Accessed: March 24, 2024, https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK553166\/."},{"key":"pone.0309424.ref004","unstructured":"Centers for Disease Control and Prevention. Drug Overdose Deaths in the U.S. Top 100,000 Annually. 2022; Accessed: November 22, 2022, https:\/\/www.cdc.gov\/nchs\/pressroom\/nchs_press_releases\/2021\/20211117.htm."},{"issue":"1","key":"pone.0309424.ref005","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/s40122-018-0097-6","article-title":"A Brief History of the Opioid Epidemic and Strategies for Pain Medicine","volume":"7","author":"M Jones","year":"2018","journal-title":"Pain and Therapy"},{"key":"pone.0309424.ref006","doi-asserted-by":"crossref","first-page":"65","DOI":"10.2147\/DHPS.S214771","article-title":"Opioids and Frequency Counts in the US Food and Drug Administration Adverse Event Reporting System (FAERS) Database: A Quantitative View of the Epidemic","volume":"11","author":"M Veronin","year":"2019","journal-title":"Drug, Healthcare and Patient Safety"},{"issue":"15","key":"pone.0309424.ref007","first-page":"1624","article-title":"CDC Guideline for Prescribing Opioids for Chronic Pain\u2014United States, 2016","volume":"315","author":"D Dowell","year":"2016","journal-title":"JAMA Network"},{"issue":"4","key":"pone.0309424.ref008","first-page":"318","article-title":"Opioid Risk Tool","volume":"116","author":"J Ducharme","year":"2019","journal-title":"StatPearls"},{"issue":"6","key":"pone.0309424.ref009","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1111\/j.1526-4637.2005.00072.x","article-title":"Predicting Aberrant Behaviors in Opioid-Treated Patients: Preliminary Validation of the Opioid Risk Tool","volume":"6","author":"L Webster","year":"2005","journal-title":"Pain Medicine"},{"issue":"1","key":"pone.0309424.ref010","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.pain.2007.01.014","article-title":"Development and validation of the Current Opioid Misuse Measure","volume":"130","author":"S Butler","year":"2007","journal-title":"Pain"},{"issue":"2","key":"pone.0309424.ref011","doi-asserted-by":"crossref","first-page":"171","DOI":"10.5055\/jom.2015.0266","article-title":"Validation of a New Risk Assessment Tool: The Brief Risk Questionnaire","volume":"11","author":"T Jones","year":"2015","journal-title":"Journal of Opioid Management"},{"issue":"3","key":"pone.0309424.ref012","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.jpainsymman.2004.04.007","article-title":"Predicting Aberrant Drug Behavior in Patients Treated for Chronic Pain: Importance of Abuse History","volume":"28","author":"E Michna","year":"2004","journal-title":"Journal of Pain and Symptom Management"},{"issue":"4","key":"pone.0309424.ref013","first-page":"318","article-title":"Opioid Use Disorder Assessment Tools and Drug Screening","volume":"116","author":"J Ducharme","year":"2019","journal-title":"Missouri Medicine"},{"key":"pone.0309424.ref014","unstructured":"American Medical Association. Report Shows Decreases in Opioid Prescribing, Increases in Overdoses. 2021; Accessed: October 13, 2022, https:\/\/www.ama-assn.org\/press-center\/press-releases\/report-shows-decreases-opioid-prescribing-increase-overdoses."},{"issue":"4","key":"pone.0309424.ref015","doi-asserted-by":"crossref","first-page":"e4235","DOI":"10.1097\/GOX.0000000000004235","article-title":"Prolonged Opioid Use following Hand Surgery: A Systematic Review and Proposed Criteria","volume":"10","author":"S Moura","year":"2022","journal-title":"Plastic and Reconstructive Surgery-Global Open"},{"issue":"7","key":"pone.0309424.ref016","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1097\/CORR.0000000000001251","article-title":"Can Predictive Modeling Tools Identify Patients at High Risk of Prolonged Opioid Use After ACL Reconstruction?","volume":"478","author":"A Anderson","year":"2020","journal-title":"Clinical Orthopaedics and Related Research"},{"issue":"10","key":"pone.0309424.ref017","doi-asserted-by":"crossref","first-page":"2272","DOI":"10.1016\/j.arth.2019.06.013","article-title":"Development of Machine Learning Algorithms for Prediction of Sustained Postoperative Opioid Prescriptions After Total Hip Arthroplasty","volume":"34","author":"A Karhade","year":"2019","journal-title":"The Journal of Arthroplasty"},{"issue":"3","key":"pone.0309424.ref018","doi-asserted-by":"crossref","first-page":"e190968","DOI":"10.1001\/jamanetworkopen.2019.0968","article-title":"Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions","volume":"2","author":"W Lo-ciganic","year":"2019","journal-title":"JAMA Network Open"},{"key":"pone.0309424.ref019","doi-asserted-by":"crossref","first-page":"103725","DOI":"10.1016\/j.jbi.2021.103725","article-title":"Predicting Opioid Overdose Risk of Patients with Opioid Prescriptions Using Electronic Health Records Based on Temporal Deep Learning","volume":"116","author":"X Dong","year":"2021","journal-title":"Journal of Biomedical Informatics"},{"key":"pone.0309424.ref020","doi-asserted-by":"crossref","first-page":"105886","DOI":"10.1016\/j.ypmed.2019.105886","article-title":"Using Machine Learning to Predict Opioid Misuse Among US Adolescents","volume":"130","author":"D Han","year":"2020","journal-title":"Preventive Medicine"},{"issue":"3","key":"pone.0309424.ref021","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1176\/appi.focus.20210002","article-title":"Chronic Pain, Opioid Use Disorder, and Clinical Management Among Older Adults","volume":"19","author":"S Potru","year":"2021","journal-title":"Focus"},{"issue":"3","key":"pone.0309424.ref022","article-title":"Predicting Opioid Dependence from Electronic Health Records with Machine Learning","volume":"12","author":"R Ellis","year":"2019","journal-title":"BioData Mining"},{"issue":"4","key":"pone.0309424.ref023","doi-asserted-by":"crossref","first-page":"1917","DOI":"10.1073\/pnas.1905355117","article-title":"Predicting High-Risk Opioid Prescriptions Before they are Given","volume":"117","author":"J Hastings","year":"2020","journal-title":"Proceedings of the National Academy of Sciences of the United States of America"},{"key":"pone.0309424.ref024","doi-asserted-by":"crossref","unstructured":"Wadekar, A. 2019 Predicting Opioid Use Disorder (OUD) Using A Random Forest. 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), Milwaukee, WI, July 2019, Institute of Electrical and Electronics Engineers.","DOI":"10.1109\/COMPSAC.2019.00161"},{"issue":"2","key":"pone.0309424.ref025","doi-asserted-by":"crossref","first-page":"57","DOI":"10.4018\/ijphim.2014070104","article-title":"Implementation of Electronic Health Record (EHR) System in Healthcare Industry","volume":"2","author":"R Schumaker","year":"2014","journal-title":"International Journal of Privacy and Health Information Management"},{"key":"pone.0309424.ref026","doi-asserted-by":"crossref","unstructured":"Suzuki, K. Artificial Neural Networks: Methodological Advances and Biomedical Applications. InTech, 2011, pp. 3.","DOI":"10.5772\/644"},{"issue":"2","key":"pone.0309424.ref027","doi-asserted-by":"crossref","first-page":"321","DOI":"10.3390\/rs14020321","article-title":"Comparative Study of Convolutional Neural Network and Conventional Machine Learning Methods for Landslide Susceptibility Mapping","volume":"14","author":"R Liu","year":"2022","journal-title":"Remote Sensing"},{"issue":"5","key":"pone.0309424.ref028","doi-asserted-by":"crossref","first-page":"1477","DOI":"10.1109\/TCAD.2022.3202430","article-title":"Data-Driven Reliability Models of Quantum Circuit: From Traditional ML to Graph Neural Network","volume":"42","author":"V Saravanan","year":"2022","journal-title":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems"},{"key":"pone.0309424.ref029","unstructured":"Krizhevsky, A and Sutskever I and Hinton G. 2012 ImageNet Classification with Deep Convolutional Neural Networks. 2012 NIPS\u201912 Proceedings of the 25th International Conference on Neural Information Processing Systems, Lake Tahoe, December 2012, Curran Associates."},{"issue":"9","key":"pone.0309424.ref030","doi-asserted-by":"crossref","first-page":"e0236400","DOI":"10.1371\/journal.pone.0236400","article-title":"Deep Neural Network Models for Identifying Incident Dementia using Claims and EHR Datasets","volume":"15","author":"V Nori","year":"2020","journal-title":"PLOS ONE"},{"key":"pone.0309424.ref031","unstructured":"Johnson, A and Bulgarelli, L and Pollard, T and Horng, S and Celi, L.A. and Mark, R. MIMIC-IV (version 1.0) 2020, PhysioNet, https:\/\/physionet.org\/content\/mimiciv\/1.0\/."},{"key":"pone.0309424.ref032","unstructured":"Centers for Disease Control and Prevention. Classification of Diseases, Functioning, and Disability. 2021, Accessed: October 11, 2022, https:\/\/www.cdc.gov\/nchs\/icd\/index.htm."},{"key":"pone.0309424.ref033","doi-asserted-by":"crossref","first-page":"113137","DOI":"10.1016\/j.dss.2019.113137","article-title":"Electronic Health Records in IS Research: Quality Issues, Essential Thresholds and Remedial Actions","volume":"126","author":"G Jetley","year":"2019","journal-title":"Decision Support Systems"},{"key":"pone.0309424.ref034","doi-asserted-by":"crossref","unstructured":"Muthukrishnan, R and Rohini, R. LASSO: A Feature Selection Technique in Predictive Modeling for Machine Learning. 2016 IEEE International Conference on Advances in Computer Applications (ICACA), Coimbatore, Tamil Nadu, India, Institute of Electrical and Electronics Engineers.","DOI":"10.1109\/ICACA.2016.7887916"},{"issue":"4","key":"pone.0309424.ref035","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1093\/jamiaopen\/ooz035","article-title":"Heterogeneity Introduced by EHR System Implementation in a De-Identified Data Resource from 100 Non-Affiliated Organizations","volume":"2","author":"E Glynn","year":"2019","journal-title":"JAMIA Open"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0309424","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T18:23:45Z","timestamp":1724869425000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0309424"}},"subtitle":[],"editor":[{"given":"Sunil","family":"Shrestha","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2024,8,28]]},"references-count":35,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,8,28]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0309424","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,28]]}}}