NLP Methods for Extraction of Symptoms from Unstructured Data for Use in Prognostic COVID-19 Analytic Models

• Published in: The Journal of artificial intelligence research 2021-01, Vol.72, p.429-474
• Main Authors: Silverman, Greg M., Sahoo, Himanshu S., Ingraham, Nicholas E., Lupei, Monica, Puskarich, Michael A., Usher, Michael, Dries, James, Finzel, Raymond L., Murray, Eric, Sartori, John, Simon, Gyorgy, Zhang, Rui, Melton, Genevieve B., Tignanelli, Christopher J., Pakhomov, Serguei VS
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Coronaviruses; COVID-19; Decision making; Disease control; Dyspnea; Emergency medical services; Feature extraction; Hospitals; Mortality; Patients; Signs and symptoms; Statistical models; Unstructured data; Viral diseases
• ISSN: 1076-9757; 1076-9757; 1943-5037
• DOI: 10.1613/jair.1.12631

Statistical modeling of outcomes based on a patient's presenting symptoms (symptomatology) can help deliver high quality care and allocate essential resources, which is especially important during the COVID-19 pandemic. Patient symptoms are typically found in unstructured notes, and thus not readily available for clinical decision making. In an attempt to fill this gap, this study compared two methods for symptom extraction from Emergency Department (ED) admission notes. Both methods utilized a lexicon derived by expanding The Center for Disease Control and Prevention's (CDC) Symptoms of Coronavirus list. The first method utilized a word2vec model to expand the lexicon using a dictionary mapping to the Uni ed Medical Language System (UMLS). The second method utilized the expanded lexicon as a rule-based gazetteer and the UMLS. These methods were evaluated against a manually annotated reference (f1-score of 0.87 for UMLS-based ensemble; and 0.85 for rule-based gazetteer with UMLS). Through analyses of associations of extracted symptoms used as features against various outcomes, salient risks among the population of COVID-19 patients, including increased risk of in-hospital mortality (OR 1.85, p-value < 0.001), were identified for patients presenting with dyspnea. Disparities between English and non-English speaking patients were also identified, the most salient being a concerning finding of opposing risk signals between fatigue and in-hospital mortality (non-English: OR 1.95, p-value = 0.02; English: OR 0.63, p-value = 0.01). While use of symptomatology for modeling of outcomes is not unique, unlike previous studies this study showed that models built using symptoms with the outcome of in-hospital mortality were not significantly different from models using data collected during an in-patient encounter (AUC of 0.9 with 95% CI of [0.88, 0.91] using only vital signs; AUC of 0.87 with 95% CI of [0.85, 0.88] using only symptoms). These findings indicate that prognostic models based on symptomatology could aid in extending COVID-19 patient care through telemedicine, replacing the need for in-person options. The methods presented in this study have potential for use in development of symptomatology-based models for other diseases, including for the study of Post-Acute Sequelae of COVID-19 (PASC).