Automated tracking of emergency department abdominal CT findings during the COVID-19 pandemic using natural language processing
Copyright © 2021 Elsevier Inc. All rights reserved..
PURPOSE: During the COVID-19 pandemic, emergency department (ED) volumes have fluctuated. We hypothesized that natural language processing (NLP) models could quantify changes in detection of acute abdominal pathology (acute appendicitis (AA), acute diverticulitis (AD), or bowel obstruction (BO)) on CT reports.
METHODS: This retrospective study included 22,182 radiology reports from CT abdomen/pelvis studies performed at an urban ED between January 1, 2018 to August 14, 2020. Using a subset of 2448 manually annotated reports, we trained random forest NLP models to classify the presence of AA, AD, and BO in report impressions. Performance was assessed using 5-fold cross validation. The NLP classifiers were then applied to all reports.
RESULTS: The NLP classifiers for AA, AD, and BO demonstrated cross-validation classification accuracies between 0.97 and 0.99 and F1-scores between 0.86 and 0.91. When applied to all CT reports, the estimated numbers of AA, AD, and BO cases decreased 43-57% in April 2020 (first regional peak of COVID-19 cases) compared to 2018-2019. However, the number of abdominal pathologies detected rebounded in May-July 2020, with increases above historical averages for AD. The proportions of CT studies with these pathologies did not significantly increase during the pandemic period.
CONCLUSION: Dramatic decreases in numbers of acute abdominal pathologies detected by ED CT studies were observed early on during the COVID-19 pandemic, though these numbers rapidly rebounded. The proportions of CT cases with these pathologies did not increase, which suggests patients deferred care during the first pandemic peak. NLP can help automatically track findings in ED radiology reporting.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:49 |
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| Enthalten in: |
The American journal of emergency medicine - 49(2021) vom: 02. Nov., Seite 52-57 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Li, Matthew D [VerfasserIn] |
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| Links: |
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| Themen: |
Appendicitis |
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| Anmerkungen: |
Date Completed 23.11.2021 Date Revised 21.12.2022 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.ajem.2021.05.057 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM326108262 |
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| 245 | 1 | 0 | |a Automated tracking of emergency department abdominal CT findings during the COVID-19 pandemic using natural language processing |
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| 500 | |a Date Revised 21.12.2022 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2021 Elsevier Inc. All rights reserved. | ||
| 520 | |a PURPOSE: During the COVID-19 pandemic, emergency department (ED) volumes have fluctuated. We hypothesized that natural language processing (NLP) models could quantify changes in detection of acute abdominal pathology (acute appendicitis (AA), acute diverticulitis (AD), or bowel obstruction (BO)) on CT reports | ||
| 520 | |a METHODS: This retrospective study included 22,182 radiology reports from CT abdomen/pelvis studies performed at an urban ED between January 1, 2018 to August 14, 2020. Using a subset of 2448 manually annotated reports, we trained random forest NLP models to classify the presence of AA, AD, and BO in report impressions. Performance was assessed using 5-fold cross validation. The NLP classifiers were then applied to all reports | ||
| 520 | |a RESULTS: The NLP classifiers for AA, AD, and BO demonstrated cross-validation classification accuracies between 0.97 and 0.99 and F1-scores between 0.86 and 0.91. When applied to all CT reports, the estimated numbers of AA, AD, and BO cases decreased 43-57% in April 2020 (first regional peak of COVID-19 cases) compared to 2018-2019. However, the number of abdominal pathologies detected rebounded in May-July 2020, with increases above historical averages for AD. The proportions of CT studies with these pathologies did not significantly increase during the pandemic period | ||
| 520 | |a CONCLUSION: Dramatic decreases in numbers of acute abdominal pathologies detected by ED CT studies were observed early on during the COVID-19 pandemic, though these numbers rapidly rebounded. The proportions of CT cases with these pathologies did not increase, which suggests patients deferred care during the first pandemic peak. NLP can help automatically track findings in ED radiology reporting | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Appendicitis | |
| 650 | 4 | |a Bowel obstruction | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a CT | |
| 650 | 4 | |a Diverticulitis | |
| 650 | 4 | |a Emergency | |
| 700 | 1 | |a Wood, Peter A |e verfasserin |4 aut | |
| 700 | 1 | |a Alkasab, Tarik K |e verfasserin |4 aut | |
| 700 | 1 | |a Lev, Michael H |e verfasserin |4 aut | |
| 700 | 1 | |a Kalpathy-Cramer, Jayashree |e verfasserin |4 aut | |
| 700 | 1 | |a Succi, Marc D |e verfasserin |4 aut | |
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