Fig 1.
Flowchart outlines the steps involved in our study.
Drug allergy records in the Songklanagarind Hospital’s EHR from October 2001 to July 2020 were extracted for developing algorithms. Only the records that had been reviewed by pharmacists were considered. We then trained NB-SVM, ULMFiT, and BERT-based models to map the unstructured allergy description to the institutional symptom terms. The ensemble model was then used for evaluation with pharmacists via a web application in a simulated EHR environment.
Fig 2.
GUI for document a new drug allergy record.
GUI for documenting a drug allergy record in the Songklanagarind Hospital’s EHR. The user interface was originally in Thai and was translated to English for ease of the reader.
Table 1.
Dataset characteristics.
Table 2.
Number of occurrences for each symptom term grouped according to organ systems.
Fig 3.
Diagrams outline the steps involved in our methods.
(A) Data preparation included word segmentation and tokenization, with each algorithm using different method. (B) NB-SVM involved training multiple pipelines for Naive Bayes feature extraction and SVM classification. (C) ULMFiT involved fine-tuning the pre-trained LM with our target-domain allergy corpus and fine-tuning a classifier for our multi-label classification task. (D) BERT involves fine-tuning a classifier for our multi-label classification task. This study evaluated three pre-trained general-domain BERT models and one target-domain BERT model pre-trained on our allergy corpus.
Table 3.
Performance of each natural language classifier for extracting symptoms from free-text records.
Table 4.
Average precision for each symptom term on the test set.
Table 5.
Confusion matrix for each symptom term on the test set.