Nlp methods for extraction of symptoms from unstructured data for use in prognostic covid-19 analytic models

Greg M Silverman, Himanshu S. Sahoo, Nick E Ingraham, Monica Lupei, Michael A. Puskarich, Michael Usher, James Dries, Raymond L Finzel, Eric Murray, John Sartori, Gyorgy J Simon, Rui Zhang, Genevieve B. Melton, Christopher J. Tignanelli, Serguei V.S. Pakhomov

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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 Unified 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 identigied 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).

Original languageEnglish (US)
Pages (from-to)429-474
Number of pages46
JournalJournal of Artificial Intelligence Research
Volume72
DOIs
StatePublished - 2021

Bibliographical note

Funding Information:
This research was supported by Fairview Health Services, the National Institutes of Health’s National Center for Advancing Translational Sciences grant U01TR002062, the National Institutes of Health’s National Heart, Lung, and Blood Institute’s grant T32HL07741 (NEI), the Agency for Healthcare Research and Quality (AHRQ) R01HS026743 (MGU) and Patient-Centered Outcomes Research Institute (PCORI), grant K12HS026379 (SS, CJT). Additional support for MN-LHS scholars is offered by the University of Minnesota Office of Academic Clinical Affairs and the Division of Health Policy and Management, University of Minnesota School of Public Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of Fairview Health Services, the National Institutes of Health, AHRQ, PCORI, or Minnesota Learning Health System Mentored Career Development Program (MN-LHS). This grant was also supported by the University of Minnesota CTSA grant UL1TR000114.

Publisher Copyright:
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