@inproceedings{3a01c9e3b3694d05b979aaf07e4a9220,
title = "Leveraging observational and prospective data to develop an opioid exposure detection model",
abstract = "Experimental studies are widely considered as the gold standard for discovering new evidence. However, advances in computational science provide an opportunity to take advantage of large clinical datasets in cases where randomized experiments are not practical. In this study, we used a large clinical database to train a model capable of detecting exposure to opioid medication (AUROC=0.76). We designed and implemented a clinical study to measure the performance of the model against the unseen data from the study. Our results show that the model based on hospital patient data exhibited promising performance against the retrospective clinical study data (AUROC=0.68).",
keywords = "Observational study, machine learning model, opioid, prospective study, wearables",
author = "Miran, {Seyed M.} and Gregory Boverman and Sara Mariani and Ting Feng and Luoluo Liu and Brian Gross and Daniel McFarlane and Robert Gibson and Kuchinski, {Anne Marie} and Holton Boomer and Dennis Swearingen and Joseph Frassica and Schwartz, {Richard B.} and Noren, {David P.}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 ; Conference date: 15-07-2024 Through 19-07-2024",
year = "2024",
doi = "10.1109/EMBC53108.2024.10782760",
language = "English (US)",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 - Proceedings",
}