Development and evaluation of machine learning models for the detection of emergency department patients with opioid...

The accurate identification of Emergency Department (ED) encounters involving opioid misuse is critical for health services, research, and surveillance. We sought to develop natural language processing (NLP)-based models for the detection of ED encounters involving opioid misuse.

A sample of ED encounters enriched for opioid misuse was manually annotated and clinical notes extracted. We evaluated classic machine learning (ML) methods, fine-tuning of publicly available pretrained language models, and a previously developed convolutional neural network opioid classifier for use on hospitalized patients (SMART-AI). Performance was benchmarked to opioid-related ICD-10-CM codes. Both raw text and text transformed to the Unified Medical Language System were evaluated. Face validity was evaluated by term feature importance.

There were 1123 encounters used for training, validation, and testing. Of the classic ML methods, XGBoost had the highest AU_PRC 0.9358 (95% CI 0.8945, 0.9681), accuracy 0.8874 (0.8402, 0.9349), and F1 score 0.8624 (0.7969, 0.9197) which performed comparably to ICD-10-CM codes [accuracy 0.8687 (0.8155, 0.9167); F1 0.8296 (0.7544, 0.8939)]. Excluding XGBoost, fine-tuned pre-trained language models generally outperformed classic ML methods. The best performing model by point estimate was the fine-tuned SMART-AI based model with domain adaptation [AU_PRC 0.9474 (0.9113, 0.9749); accuracy 0.8816 (0.8284, 0.9290); F1 0.8499 (0.7805, 0.9103)] but confidence intervals overlapped with other models. Explainability analyses showed the most predictive terms were "heroin," "opioids," "alcoholic intoxication, chronic," "cocaine," "opiates," and "suboxone."

NLP-based models perform comparably to entry of ICD-10-CM diagnosis codes for the detection of ED encounters with opioid misuse. Fine tuning with domain adaptation for pre-trained language models resulted in improved performance.