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.
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.
J Clin Virol. 2022 Oct;155:105251. doi: 10.1016/j.jcv.2022.105251. Epub 2022 Aug 3.
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.
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.
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.
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.
我们的目标是开发一种工具，通过在检测能力有限的时期集中进行流行病学调查和 PCR 检查，来促进对 COVID-19 病例的早期发现。
我们开发了一种用于分析以色列国防军医疗服务提供者记录的医疗记录的算法。该算法利用文本分析来检测出现可疑症状的患者，并在 92 个随机选择的单位中进行了测试。如果一个单位中出现疑似病例集群，该算法会提示进行有针对性的流行病学调查，并辅以算法提供的数据。
在一个月的随访期间，该算法标记了 17 个需要调查的单位。随后的流行病学调查导致对 78 人进行了检测，并在四个之前未被发现的集群中发现了 8 例病例。由此产生的阳性检出率为 10.25%，是研究时以色列国防军平均水平的五倍。该算法未错过检查单位中的任何 COVID-19 病例。
本研究描述了一种基于文本分析的 COVID-19 病例早期检测算法的成功开发和大规模部署，证明了自然语言处理医疗文本作为促进公共卫生的工具的潜力。
