Dataset Information

Clinical management of lung cancer patients during the outbreak of COVID-19 epidemic.

ABSTRACT: The rapid growth of 2019 novel coronavirus (COVID-19) outbreak in Wuhan, China, at the early December 2019. COVID-19 spread all over the word just a few months. The outbreak of COVID-19 infection poses major threat to international health and economy. World Health Organization (WHO) announced that the new coronavirus was an international public health emergency on January 30, 2020. However, with the spread of COVID-19, the routine medical care of lung cancer patients was affected. Because lung cancer patients have low immunity after anti-tumor treatment, they should become the main targets for epidemic prevention. Lung cancer patients are increasingly concerned about the prevention of COVID-19. It is necessary to provide individualized medical treatment and management for lung cancer patients based on patients' conditions and regional epidemic patterns.

SUBMITTER: Wang L

PROVIDER: S-EPMC7509820 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Clinical management of lung cancer patients during the outbreak of COVID-19 epidemic.

Wang Li L Jiang Man M Qu Jialin J Zhou Na N Zhang Xiaochun X

Infectious agents and cancer 20200923

The rapid growth of 2019 novel coronavirus (COVID-19) outbreak in Wuhan, China, at the early December 2019. COVID-19 spread all over the word just a few months. The outbreak of COVID-19 infection poses major threat to international health and economy. World Health Organization (WHO) announced that the new coronavirus was an international public health emergency on January 30, 2020. However, with the spread of COVID-19, the routine medical care of lung cancer patients was affected. Because lung c ...[more]

PMID: 32983254

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Project description:BackgroundThe coronavirus disease (COVID-19) pandemic has spread rapidly across the world, creating an urgent need for predictive models that can help healthcare providers prepare and respond to outbreaks more quickly and effectively, and ultimately improve patient care. Early detection and warning systems are crucial for preventing and controlling epidemic spread.ObjectiveIn this study, we aimed to propose a machine learning-based method to predict the transmission trend of COVID-19 and a new approach to detect the start time of new outbreaks by analyzing epidemiological data.MethodsWe developed a risk index to measure the change in the transmission trend. We applied machine learning (ML) techniques to predict COVID-19 transmission trends, categorized into three labels: decrease (L0), maintain (L1), and increase (L2). We used Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB) as ML models. We employed grid search methods to determine the optimal hyperparameters for these three models. We proposed a new method to detect the start time of new outbreaks based on label 2, which was sustained for at least 14 days (i.e., the duration of maintenance). We compared the performance of different ML models to identify the most accurate approach for outbreak detection. We conducted sensitivity analysis for the duration of maintenance between 7 days and 28 days.ResultsML methods demonstrated high accuracy (over 94%) in estimating the classification of the transmission trends. Our proposed method successfully predicted the start time of new outbreaks, enabling us to detect a total of seven estimated outbreaks, while there were five reported outbreaks between March 2020 and October 2022 in Korea. It means that our method could detect minor outbreaks. Among the ML models, the RF and XGB classifiers exhibited the highest accuracy in outbreak detection.ConclusionThe study highlights the strength of our method in accurately predicting the timing of an outbreak using an interpretable and explainable approach. It could provide a standard for predicting the start time of new outbreaks and detecting future transmission trends. This method can contribute to the development of targeted prevention and control measures and enhance resource management during the pandemic.

Dataset Information

Clinical management of lung cancer patients during the outbreak of COVID-19 epidemic.

Publications

Clinical management of lung cancer patients during the outbreak of COVID-19 epidemic.

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