Natural Language Processing and Machine Learning for Detection of Respiratory Illness by Chest CT Imaging and Tracking of COVID-19 Pandemic in the US
| Autor: | Tony Lindsey, Reese H Clark, Robson Macedo, Istvan Megyeri, Robert J. Harris, Juan C. Batlle, Brian Baker, Shwan Kim, Ricardo C. Cury |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
2019-20 coronavirus outbreak
Coronavirus disease 2019 (COVID-19) Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Chest ct Computed tomography chest CT 030204 cardiovascular system & hematology computer.software_genre Machine learning 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine big data Pandemic medicine Radiology Nuclear Medicine and imaging natural language processing Original Research Respiratory illness medicine.diagnostic_test business.industry public health computed tomography Pulmonary Imaging viral outbreak machine learning Artificial intelligence business computer Natural language processing |
| Zdroj: | Radiology: Cardiothoracic Imaging |
| ISSN: | 2638-6135 |
| Popis: | Background Coronavirus disease 2019 (COVID-19) has spread quickly throughout the United States (US) causing significant disruption in healthcare and society. Tools to identify hot spots are important for public health planning. The goal of our study was to determine if natural language processing (NLP) algorithm assessment of thoracic computed tomography (CT) imaging reports correlated with the incidence of official COVID-19 cases in the US. Methods Using de-identified HIPAA compliant patient data from our common imaging platform interconnected with over 2,100 facilities covering all 50 states, we developed three NLP algorithms to track positive CT imaging features of respiratory illness typical in SARS-CoV-2 viral infection. We compared our findings against the number of official COVID-19 daily, weekly and state-wide. Results The NLP algorithms were applied to 450,114 patient chest CT comprehensive reports gathered from January 1st to October 3rd, 2020. The best performing NLP model exhibited strong correlation with daily official COVID-19 cases (r2=0.82, p |
| Databáze: | OpenAIRE |
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