Time-series analysis of multidimensional clinical-laboratory data by dynamic Bayesian networks reveals trajectories of COVID-19 outcomes

Longato, Enrico; Morieri, Mario Luca; Sparacino, Giovanni; Camillo, Barbara Di; Cattelan, Annamaria; Menzo, Sara Lo; Trevenzoli, Marco; Vianello, Andrea; Guarnieri, Gabriella; Lionello, Federico; Avogaro, Angelo; Fioretto, Paola; Vettor, Roberto; Fadini, Gian Paolo

doi:10.1016/j.cmpb.2022.106873

Cited by 4 publications

(1 citation statement)

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“…This capability is particularly valuable for maritime emergency rescue data, which inherently follows a cyclical pattern. In contrast to the high computational demands and complexity of DBN ( 41 ), SARIMA offers high prediction accuracy at lower computational costs, making it well-suited for various real-time and near-real-time applications. While ARIMA struggles with seasonal data ( 15 ), SARIMA stands out for its ability to make precise predictions in complex datasets without the need for intricate assumptions or extensive prior knowledge.…”

Section: Discussionmentioning

confidence: 99%

Statistical machine learning models for prediction of China’s maritime emergency patients in dynamic: ARIMA model, SARIMA model, and dynamic Bayesian network model

Yang,

Cheng,

Zhang

et al. 2024

Front. Public Health

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IntroductionRescuing individuals at sea is a pressing global public health issue, garnering substantial attention from emergency medicine researchers with a focus on improving prevention and control strategies. This study aims to develop a Dynamic Bayesian Networks (DBN) model utilizing maritime emergency incident data and compare its forecasting accuracy to Auto-regressive Integrated Moving Average (ARIMA) and Seasonal Auto-regressive Integrated Moving Average (SARIMA) models.MethodsIn this research, we analyzed the count of cases managed by five hospitals in Hainan Province from January 2016 to December 2020 in the context of maritime emergency care. We employed diverse approaches to construct and calibrate ARIMA, SARIMA, and DBN models. These models were subsequently utilized to forecast the number of emergency responders from January 2021 to December 2021. The study indicated that the ARIMA, SARIMA, and DBN models effectively modeled and forecasted Maritime Emergency Medical Service (EMS) patient data, accounting for seasonal variations. The predictive accuracy was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2) as performance metrics.ResultsIn this study, the ARIMA, SARIMA, and DBN models reported RMSE of 5.75, 4.43, and 5.45; MAE of 4.13, 2.81, and 3.85; and R2 values of 0.21, 0.54, and 0.44, respectively. MAE and RMSE assess the level of difference between the actual and predicted values. A smaller value indicates a more accurate model prediction. R2 can compare the performance of models across different aspects, with a range of values from 0 to 1. A value closer to 1 signifies better model quality. As errors increase, R2 moves further from the maximum value. The SARIMA model outperformed the others, demonstrating the lowest RMSE and MAE, alongside the highest R2, during both modeling and forecasting. Analysis of predicted values and fitting plots reveals that, in most instances, SARIMA’s predictions closely align with the actual number of rescues. Thus, SARIMA is superior in both fitting and forecasting, followed by the DBN model, with ARIMA showing the least accurate predictions.DiscussionWhile the DBN model adeptly captures variable correlations, the SARIMA model excels in forecasting maritime emergency cases. By comparing these models, we glean valuable insights into maritime emergency trends, facilitating the development of effective prevention and control strategies.

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Section: Discussionmentioning

confidence: 99%