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Unexplained pneumonia appeared in Wuhan was soon determined to be a novel coronavirus disease, referred to as COVID-19. On March 11, 2020, WHO characterized COVID-19 as a pandemic and the virus has been recognized as a global threat. A plethora of studies is being carried out using various statistical and mathematical models to predict the probable evolution of this pandemic. Though most of them are focusing on building predictive models to assess mortality rates and risk, concentrating on the length of hospital stay can improve decision making and treatment plans. While modeling the length of stay, possible outcomes observed are either discharge or mortality. Modeling the duration of recovery and death provides valuable information for health officials to design proper strategies to reduce the burden on the health system during the outbreak. In this study, we are exploring this competing event aspect of the survival data obtained from COVID-19 patients using the state-of-the-art model DeepHit, a discrete survival model.
The repeated occurrence of the same event in a process is commonly observed in many domains. Such events are referred to as recurrent events. The time to occurrence of these repeated events varies from unit to unit with a possibility of events not occurring among some of the units. Invariably such data are dealt with using some of the techniques in survival analysis called recurrent event models, which are commonly encountered in epidemiological studies and clinical trials. However, it applies to other domains in science and technology. We illustrate the usefulness of recurrent event models in the context of defect proneness analysis in quality assessment of software. Some of the models in practice are introduced on data collected to study the impact of module size on defect proneness in the Mozilla product. Module size plays a significant role in defect proneness and each defect fix makes the class more susceptible to further defects. The risk estimates obtained from the different models vary owing to the differences in the properties of the models as well as the assumptions underlying it.
Mortality prediction in surgical intensive care units (SICUs) is considered to be among the most critical steps in enforcing efficient treatment policies. This study aims to evaluate the performance of various deep learning models in predicting the mortality of patients admitted to SICUs. The survival of 2,225 adult patients admitted to SICUs was modeled using five salient deep learning-based survival models, namely, Cox-CC, Cox-Time, DeepSurv, DeepHit, and N-MTLR. The data were extracted from the Medical Information Mart for Intensive Care II (MIMIC-II) database. The performance of the models was compared using the time-dependent concordance index (Ctd-index) and integrated Brier score (IBS). From among the five models, DeepSurv achieved the most accurate prediction, while Cox-Time demonstrated the least optimal predictive ability. For DeepSurv, Cox-CC, DeepHit, N-MTLR, and Cox-Time, the mean Ctd -index was 0.773, 0.767, 0.765, 0.732, and 0.659, and the mean IBS was 0.181, 0.192, 0.195, 0.212, and 0.225, respectively. DeepSurv, Cox-CC, and DeepHit yielded comparable performance. Deep learning models are free from the stringent assumptions inherent in standard survival models. Hence, these models are considered flexible alternatives to the standard approaches in scalable, real-world survival problems.
BackgroundUnexplained pneumonia appeared in Wuhan was soon determined to be a novel coronavirus disease, referred to as COVID-19. On March 11, 2020, WHO characterized COVID-19 as a pandemic. A plethora of studies on this pandemic is being carried out using various statistical and mathematical models. Though most of them are focusing on building predictive models, concentrating on the length of hospital stay can improve decision making and treatment plans. While modeling the length of stay, possible outcomes observed are either discharge or mortality.ObjectiveThe study aimed to analyse the survival data of COVID-19 patients with the competing risk methodology.MethodologyThe performance DeepHit, a deep learning-based competing risk model is compared with Fine-Gray, a traditional statistical model using a time-dependent concordance index. ResultsThe deep learning-based competing risk model outperformed the statistical model in terms of discriminative power.ConclusionModeling the duration of recovery and death provides valuable information for health officials to design proper strategies during the outbreak. These outcomes should be considered as competing events to model the data adequately.
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