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Early detection of COVID-19 outbreaks using textual analysis of electronic medical records.
Shapiro, Michael; Landau, Regev; Shay, Shahaf; Kaminsky, Marina; Verhovsky, Guy.
  • Shapiro M; Department of Internal Medicine T, Tel Aviv Sourasky Medical Center, 7 Dafna St., Tel Aviv, Israel; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Israel Defense Forces Medical Corps, Ramat Gan, Israel. Electronic address: mikehpg@gmail.com.
  • Landau R; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Israel Defense Forces Medical Corps, Ramat Gan, Israel; Internal Medicine D and Hypertension Unit, The Chaim Sheba Medical Center, Tel Hashomer, Israel.
  • Shay S; Department of Military Medicine, Hebrew University of Jerusalem, Faculty of Medicine, Jerusalem, Israel.
  • Kaminsky M; Israel Defense Forces Medical Corps, Ramat Gan, Israel.
  • Verhovsky G; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Israel Defense Forces Medical Corps, Ramat Gan, Israel; Department of Urology, Shamir Medical Center, Tzrifin, Israel.
J Clin Virol ; 155: 105251, 2022 10.
Article in English | MEDLINE | ID: covidwho-1966826
ABSTRACT

PURPOSE:

Our objective was to develop a tool promoting early detection of COVID-19 cases by focusing epidemiological investigations and PCR examinations during a period of limited testing capabilities.

METHODS:

We developed an algorithm for analyzing medical records recorded by healthcare providers in the Israeli Defense Forces. The algorithm utilized textual analysis to detect patients presenting with suspicious symptoms and was tested among 92 randomly selected units. Detection of a potential cluster of patients in a unit prompted a focused epidemiological investigation aided by data provided by the algorithm.

RESULTS:

During a month of follow up, the algorithm has flagged 17 of the units for investigation. The subsequent epidemiological investigations led to the testing of 78 persons and the detection of eight cases in four clusters that were previously gone unnoticed. The resulting positive test rate of 10.25% was five time higher than the IDF average at the time of the study. No cases of COVID-19 in the examined units were missed by the algorithm.

CONCLUSIONS:

This study depicts the successful development and large scale deployment of a textual analysis based algorithm for early detection of COVID-19 cases, demonstrating the potential of natural language processing of medical text as a tool for promoting public health.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Cohort study / Diagnostic study / Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: J Clin Virol Journal subject: Virology Year: 2022 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Cohort study / Diagnostic study / Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: J Clin Virol Journal subject: Virology Year: 2022 Document Type: Article