Natural Language Processing for Clinical Laboratory Data Repository Systems: Implementation and Evaluation for Respiratory Viruses

BackgroundWith the growing volume and complexity of laboratory repositories, it has become tedious to parse unstructured data into structured and tabulated formats for secondary uses such as decision support, quality assurance, and outcome analysis. However, advances in Natural Language Processing (NLP) approaches have enabled efficient and automated extraction of clinically meaningful medical concepts from unstructured reports. ObjectiveIn this study, we aimed to determine the feasibility of using the NLP model for information extraction as an alternative approach to a time-consuming and operationally resource-intensive handcrafted rule-based tool. Therefore, we sought to develop and evaluate a deep learning-based NLP model to derive knowledge and extract information from text-based laboratory reports sourced from a provincial laboratory repository system. MethodsThe NLP model, a hierarchical multi-label classifier, was trained on a corpus of laboratory reports covering testing for 14 different respiratory viruses and viral subtypes. The corpus included 85k unique laboratory reports annotated by eight Subject Matter Experts (SME). The models performance stability and variation were analyzed across fine-grained and coarse-grained classes. Moreover, the models generalizability was also evaluated internally and externally on various test sets. ResultsThe NLP model was trained several times with random initialization on the development corpus, and the results of the top ten best-performing models are presented in this paper. Overall, the NLP model performed well on internal, out-of-time (pre-COVID-19), and external (different laboratories) test sets with micro-averaged F1 scores >94% across all classes. Higher Precision and Recall scores with less variability were observed for the internal and pre-COVID-19 test sets. As expected, the models performance varied across categories and virus types due to the imbalanced nature of the corpus and sample sizes per class. There were intrinsically fewer classes of viruses being detected than those tested; therefore, the models performance (lowest F1-score of 57%) was noticeably lower in the "detected" cases. ConclusionsWe demonstrated that deep learning-based NLP models are promising solutions for information extraction from text-based laboratory reports. These approaches enable scalable, timely, and practical access to high-quality and encoded laboratory data if integrated into laboratory information system repositories.