COVID-19 outbreaks surveillance through text mining applied to electronic health records.

Autor: Rocha HAL; Department of Community Health, Federal University of Ceará, Street Papi Júnior, 1223, 5th. Floor, Fortaleza, CE, Brazil. hermano@ufc.br., Solha EZM; Postgraduate Program in Applied Informatics, University of Fortaleza, Fortaleza, CE, 60811-905, Brazil., Furtado V; Postgraduate Program in Applied Informatics, University of Fortaleza, Fortaleza, CE, 60811-905, Brazil., Justino FL; Postgraduate Program in Applied Informatics, University of Fortaleza, Fortaleza, CE, 60811-905, Brazil., Barreto LAL; Department of Community Health, Federal University of Ceará, Street Papi Júnior, 1223, 5th. Floor, Fortaleza, CE, Brazil., da Silva RG; Department of Community Health, Federal University of Ceará, Street Papi Júnior, 1223, 5th. Floor, Fortaleza, CE, Brazil., de Oliveira ÍM; Health Secretariat, Ceará State Government, Fortaleza, CE, Brazil., Bates DW; Harvard Medical School, Boston, MA, USA., de Góes Cavalcanti LP; Department of Community Health, Federal University of Ceará, Street Papi Júnior, 1223, 5th. Floor, Fortaleza, CE, Brazil.; School of Public Health of Ceará, Fortaleza, CE, Brazil.; Faculty of Medicine, Christus University Center, Fortaleza, CE, Brazil., Lima Neto AS; Postgraduate Program in Applied Informatics, University of Fortaleza, Fortaleza, CE, 60811-905, Brazil.; Health Secretariat, Ceará State Government, Fortaleza, CE, Brazil., de Oliveira EA; Postgraduate Program in Applied Informatics, University of Fortaleza, Fortaleza, CE, 60811-905, Brazil.; Laboratory of Data Science and Artificial Intelligence, University of Fortaleza, Fortaleza, Ceará, 60811-905, Brazil.; Professional Masters in City Science, University of Fortaleza, Fortaleza, Ceará, 60811-905, Brazil.
Jazyk: angličtina
Zdroj: BMC infectious diseases [BMC Infect Dis] 2024 Mar 28; Vol. 24 (1), pp. 359. Date of Electronic Publication: 2024 Mar 28.
DOI: 10.1186/s12879-024-09250-y
Abstrakt: Background: The COVID-19 pandemic has caused significant disruptions to everyday life and has had social, political, and financial consequences that will persist for years. Several initiatives with intensive use of technology were quickly developed in this scenario. However, technologies that enhance epidemiological surveillance in contexts with low testing capacity and healthcare resources are scarce. Therefore, this study aims to address this gap by developing a data science model that uses routinely generated healthcare encounter records to detect possible new outbreaks early in real-time.
Methods: We defined an epidemiological indicator that is a proxy for suspected cases of COVID-19 using the health records of Emergency Care Unit (ECU) patients and text mining techniques. The open-field dataset comprises 2,760,862 medical records from nine ECUs, where each record has information about the patient's age, reported symptoms, and the time and date of admission. We also used a dataset where 1,026,804 cases of COVID-19 were officially confirmed. The records range from January 2020 to May 2022. Sample cross-correlation between two finite stochastic time series was used to evaluate the models.
Results: For patients with age 18 years, we find time-lag () = 72 days and cross-correlation () ~ 0.82, = 25 days and ~ 0.93, and = 17 days and ~ 0.88 for the first, second, and third waves, respectively.
Conclusions: In conclusion, the developed model can aid in the early detection of signs of possible new COVID-19 outbreaks, weeks before traditional surveillance systems, thereby anticipating in initiating preventive and control actions in public health with a higher likelihood of success.
(© 2024. The Author(s).)
Databáze: MEDLINE
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