Objective The COVID-19 (coronavirus disease 2019) pandemic response at the Medical University of South Carolina included virtual care visits for patients with suspected severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The telehealth system used for these visits only exports a text note to integrate with the electronic health record, but structured and coded information about COVID-19 (eg, exposure, risk factors, symptoms) was needed to support clinical care and early research as well as predictive analytics for data-driven patient advising and pooled testing. Materials and Methods To capture COVID-19 information from multiple sources, a new data mart and a new natural language processing (NLP) application prototype were developed. The NLP application combined reused components with dictionaries and rules crafted by domain experts. It was deployed as a Web service for hourly processing of new data from patients assessed or treated for COVID-19. The extracted information was then used to develop algorithms predicting SARS-CoV-2 diagnostic test results based on symptoms and exposure information. Results The dedicated data mart and NLP application were developed and deployed in a mere 10-day sprint in March 2020. The NLP application was evaluated with good accuracy (85.8% recall and 81.5% precision). The SARS-CoV-2 testing predictive analytics algorithms were configured to provide patients with data-driven COVID-19 testing advices with a sensitivity of 81% to 92% and to enable pooled testing with a negative predictive value of 90% to 91%, reducing the required tests to about 63%. Conclusions SARS-CoV-2 testing predictive analytics and NLP successfully enabled data-driven patient advising and pooled testing.
Background Family history information is important to assess the risk of inherited medical conditions. Natural language processing has the potential to extract this information from unstructured free-text notes to improve patient care and decision making. We describe the end-to-end information extraction system the Medical University of South Carolina team developed when participating in the 2019 National Natural Language Processing Clinical Challenge (n2c2)/Open Health Natural Language Processing (OHNLP) shared task. Objective This task involves identifying mentions of family members and observations in electronic health record text notes and recognizing the 2 types of relations (family member-living status relations and family member-observation relations). Our system aims to achieve a high level of performance by integrating heuristics and advanced information extraction methods. Our efforts also include improving the performance of 2 subtasks by exploiting additional labeled data and clinical text-based embedding models. Methods We present a hybrid method that combines machine learning and rule-based approaches. We implemented an end-to-end system with multiple information extraction and attribute classification components. For entity identification, we trained bidirectional long short-term memory deep learning models. These models incorporated static word embeddings and context-dependent embeddings. We created a voting ensemble that combined the predictions of all individual models. For relation extraction, we trained 2 relation extraction models. The first model determined the living status of each family member. The second model identified observations associated with each family member. We implemented online gradient descent models to extract related entity pairs. As part of postchallenge efforts, we used the BioCreative/OHNLP 2018 corpus and trained new models with the union of these 2 datasets. We also pretrained language models using clinical notes from the Medical Information Mart for Intensive Care (MIMIC-III) clinical database. Results The voting ensemble achieved better performance than individual classifiers. In the entity identification task, our top-performing system reached a precision of 78.90% and a recall of 83.84%. Our natural language processing system for entity identification took 3rd place out of 17 teams in the challenge. We ranked 4th out of 9 teams in the relation extraction task. Our system substantially benefited from the combination of the 2 datasets. Compared to our official submission with F1 scores of 81.30% and 64.94% for entity identification and relation extraction, respectively, the revised system yielded significantly better performance (P<.05) with F1 scores of 86.02% and 72.48%, respectively. Conclusions We demonstrated that a hybrid model could be used to successfully extract family history information recorded in unstructured free-text notes. In this study, our approach to entity identification as a sequence labeling problem produced satisfactory results. Our postchallenge efforts significantly improved performance by leveraging additional labeled data and using word vector representations learned from large collections of clinical notes.
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