{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T17:52:37Z","timestamp":1792000357157,"version":"4.4.0"},"reference-count":43,"publisher":"Georg Thieme Verlag KG","issue":"03","funder":[{"DOI":"10.13039\/100000051","name":"National Human Genome Research Institute","doi-asserted-by":"crossref","award":["NIH NHGRI R35 HG010714"],"award-info":[{"award-number":["NIH NHGRI R35 HG010714"]}],"id":[{"id":"10.13039\/100000051","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Appl Clin Inform"],"published-print":{"date-parts":[[2024,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Background\u2003Managing acute postoperative pain and minimizing chronic opioid use are crucial for patient recovery and long-term well-being.<\/jats:p>\n                  <jats:p>Objectives\u2003This study explored using preoperative electronic health record (EHR) and wearable device data for machine-learning models that predict postoperative acute pain and chronic opioid use.<\/jats:p>\n                  <jats:p>Methods\u2003The study cohort consisted of approximately 347 All of Us Research Program participants who underwent one of eight surgical procedures and shared EHR and wearable device data. We developed four machine learning models and used the Shapley additive explanations (SHAP) technique to identify the most relevant predictors of acute pain and chronic opioid use.<\/jats:p>\n                  <jats:p>Results\u2003The stacking ensemble model achieved the highest accuracy in predicting acute pain (0.68) and chronic opioid use (0.89). The area under the curve score for severe pain versus other pain was highest (0.88) when predicting acute postoperative pain. Values of logistic regression, random forest, extreme gradient boosting, and stacking ensemble ranged from 0.74 to 0.90 when predicting postoperative chronic opioid use. Variables from wearable devices played a prominent role in predicting both outcomes.<\/jats:p>\n                  <jats:p>Conclusion\u2003SHAP detection of individual risk factors for severe pain can help health care providers tailor pain management plans. Accurate prediction of postoperative chronic opioid use before surgery can help mitigate the risk for the outcomes we studied. Prediction can also reduce the chances of opioid overuse and dependence. Such mitigation can promote safer and more effective pain control for patients during their recovery.<\/jats:p>","DOI":"10.1055\/a-2321-0397","type":"journal-article","created":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T19:11:11Z","timestamp":1715109071000},"page":"569-582","source":"Crossref","is-referenced-by-count":9,"title":["Predicting Postoperative Pain and Opioid Use with Machine Learning Applied to Longitudinal Electronic Health Record and Wearable Data"],"prefix":"10.1055","volume":"15","author":[{"given":"Nidhi","family":"Soley","sequence":"additional","affiliation":[{"name":"Institute for Computational Medicine, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland, United States"},{"name":"Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Traci J.","family":"Speed","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, School of Medicine, Baltimore, Maryland, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anping","family":"Xie","sequence":"additional","affiliation":[{"name":"Armstrong Institute for Patient Safety and Quality, Johns Hopkins University, School of Medicine, Baltimore, Maryland, United States"},{"name":"Department of Anesthesia and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Casey Overby","family":"Taylor","sequence":"additional","affiliation":[{"name":"Institute for Computational Medicine, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland, United States"},{"name":"Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, 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Soley","year":"2023","journal-title":"Pac Symp Biocomput"},{"issue":"10","key":"ref14","doi-asserted-by":"crossref","first-page":"1763","DOI":"10.1016\/j.apmr.2015.06.003","article-title":"Pain sensitivity and pain catastrophizing are associated with persistent pain and disability after lumbar spine surgery","volume":"96","author":"R A Coronado","year":"2015","journal-title":"Arch Phys Med Rehabil"},{"issue":"36","key":"ref15","first-page":"1","article-title":"Subacute pain as a predictor of long-term pain following orthopedic surgery: an Australian prospective 12 month observational cohort study","volume":"94","author":"F C Veal","year":"2015","journal-title":"Medicine (Baltimore)"},{"issue":"04","key":"ref16","doi-asserted-by":"crossref","first-page":"e207","DOI":"10.1016\/j.bja.2019.11.015","article-title":"Elevated heart rate after noncardiac surgery: post hoc analysis of a prospective observational cohort study","volume":"124","author":"A BU 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Mueller","year":"2017","journal-title":"Anesth Analg"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"103089","DOI":"10.1016\/j.jbi.2018.12.003","article-title":"A comprehensive data level analysis for cancer diagnosis on imbalanced data","volume":"90","author":"S Fotouhi","year":"2019","journal-title":"J Biomed Inform"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.ins.2014.08.051","article-title":"SMOTE\u2013IPF: addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering","volume":"291","author":"J S\u00e1ez","year":"2015","journal-title":"Inf Sci"},{"issue":"01","key":"ref30","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: synthetic minority over-sampling technique","volume":"16","author":"N V Chawla","year":"2002","journal-title":"J Artif Intell Res"},{"issue":"05","key":"ref36","doi-asserted-by":"crossref","first-page":"964","DOI":"10.1177\/0194599815572127","article-title":"Correlation between sleep disruption on postoperative pain","volume":"152","author":"A Miller","year":"2015","journal-title":"Otolaryngol Head Neck Surg"},{"issue":"03","key":"ref37","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/j.jpainsymman.2008.03.010","article-title":"Disrupted sleep the night before breast surgery is associated with increased postoperative pain","volume":"37","author":"C E Wright","year":"2009","journal-title":"J Pain Symptom Manage"},{"issue":"09","key":"ref38","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3390\/jcm10092000","article-title":"Sleep well and recover faster with less pain-a narrative review on sleep in the perioperative period","volume":"10","author":"R M Sipil\u00e4","year":"2021","journal-title":"J Clin Med"},{"issue":"04","key":"ref39","doi-asserted-by":"crossref","first-page":"e025091","DOI":"10.1136\/bmjopen-2018-025091","article-title":"Preoperative predictors of poor acute postoperative pain control: a systematic review and meta-analysis","volume":"9","author":"M MH Yang","year":"2019","journal-title":"BMJ Open"},{"key":"ref40","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.jss.2021.12.023","article-title":"Association of prehabilitation with postoperative opioid use in colorectal surgery: an observational cohort study","volume":"273","author":"A Lee","year":"2022","journal-title":"J Surg Res"},{"issue":"10","key":"ref41","doi-asserted-by":"crossref","first-page":"e2131271","DOI":"10.1001\/jamanetworkopen.2021.31271","article-title":"Association of physical therapy interventions with long-term opioid use after total knee replacement","volume":"4","author":"K Aoyagi","year":"2021","journal-title":"JAMA Netw 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Assoc"},{"issue":"07","key":"ref48","doi-asserted-by":"crossref","first-page":"e0234962","DOI":"10.1371\/journal.pone.0234962","article-title":"Diversity and inclusion for the All of Us research program: a scoping review","volume":"15","author":"B M Mapes","year":"2020","journal-title":"PLoS One"},{"key":"ref49","first-page":"2825","article-title":"Scikit-learn: machine learning in Python","volume":"12","author":"F Pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"issue":"01","key":"ref50","doi-asserted-by":"crossref","first-page":"12","DOI":"10.11613\/BM.2014.003","article-title":"Understanding logistic regression analysis","volume":"24","author":"S Sperandei","year":"2014","journal-title":"Biochem Med (Zagreb)"},{"key":"ref52","doi-asserted-by":"crossref","first-page":"329","DOI":"10.3389\/fnagi.2017.00329","article-title":"Random forest algorithm for the classification of neuroimaging data in Alzheimer's disease: a systematic review","volume":"9","author":"A Sarica","year":"2017","journal-title":"Front Aging Neurosci"},{"issue":"03","key":"ref54","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1007\/s40708-017-0065-7","article-title":"Machine learning-XGBoost analysis of language networks to classify patients with epilepsy","volume":"4","author":"L Torlay","year":"2017","journal-title":"Brain Inform"},{"key":"ref55","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1177\/11795468221133611","article-title":"XGBoost, a novel explainable AI technique, in the prediction of myocardial infarction: a UK Biobank cohort study","volume":"16","author":"A Moore","year":"2022","journal-title":"Clin Med Insights Cardiol"},{"issue":"01","key":"ref57","doi-asserted-by":"crossref","first-page":"10256","DOI":"10.1038\/s41598-023-35476-y","article-title":"A stacking ensemble classifier-based machine learning model for classifying pollution sources on photovoltaic panels","volume":"13","author":"P W Khan","year":"2023","journal-title":"Sci 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