Generalizability of machine learning methods in detecting adverse drug events from clinical narratives in electronic medical records - Zitu, Md Muntasir; Zhang, Shijun; Owen, Dwight H.; Chiang, Chienwei; Li, Lang (Frontiers in Pharmacology, 2023)
We assessed the generalizability of machine learning methods using natural language processing (NLP) techniques to detect adverse drug events (ADEs) from clinical narratives in electronic medical records (EMRs).We constructed a new corpus correlating drugs with adverse drug events using total 1394 clinical notes of 47 randomly selected patients who received immune checkpoint inhibitors (ICIs) from 2011 to 2018 at The Ohio State University James Cancer Hospital, annotating189 drug-ADE relations in single sentences within the medical records. We also used data of Harvard's publicly available 2018 National Clinical Challenge (n2c2), which includes 505 discharge summaries with annotations of 1355 single-sentence drug-ADE relations. We applied classical machine learning (support vector machine [SVM]), deep learning (convolutional neural network [CNN] and bidirectional long short-term memory [BiLSTM]), and state-of-the-art transformer-based (bidirectional encoder representations from transformers [BERT] and ClinicalBERT) methods trained and tested in the two different corpora and compared performance among them to detect drug-ADE relationships.ClinicalBERT detected drug-ADE relationships better than the other methods when trained using our data set and tested in n2c2 (ClinicalBERT F-score, 0.78; other methods, F-scores, 0.61-0.73) and when trained using the n2c2 data set and tested in ours (ClinicalBERT F-score, 0.74; other methods, F-scores, 0.55-0.72).Comparison among several machine learning methods demonstrated the superior performance and therefore greatest generalizability of findings of ClinicalBERT for the detection of ADEs for the detection of drug-ADE relations from clinical narratives in electronic medical records.