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The prognostic assessment of an ICU patient involves assessing the severity of their condition, interventions, and length of ICU stay. Over the past 30 years, researchers have proposed numerous predictive models and severity assessment scales for ICU patients in specific regions, including APACHE II and SAPS II. However, most existing methods rely heavily on curve fitting which do not account for misclassifications caused by false negatives and positives. Specificity and sensitivity must be provided as an indicator of model performance. The primary aim in this study is to develop a machine-learning model to formulate a prognosis for MICU and SICU patients by using data from the MIMIC-IV for training. The predictive models developed in this study facilitate the prediction of mortality and other outcomes across various treatment regimens.

Importance: Due to the evolving variants of coronavirus disease 2019 (COVID-19), it is important to understand the relationship between the disease condition and socioeconomic, demographic, and health indicators across regions. Background: Studies examining the relationships between infectious disease and socioeconomic variables are not yet well established. Design: A total of 3042 counties in the United States are included as the observation unit in the study. Two outcome variables employed in the study are the control of disease spread and infection prevalence rates in each county. Method: Data are submitted to quantile regression, hierarchical regression, and random forest analyses to understand the extent to which health outcomes are affected by demographics, socioeconomics, and health indicators. Results: Counties with better control of the disease spread tend to have lower infection rates, and vice versa. When measuring different outcome variables, the common risk factors for COVID-19 with a 5% level of statistical significance include employment ratio, female labor ratio, young population ratio, and residents’ average health risk factors, while protective factors include land size, housing value, travel time to work, female population ratio, and ratio of residents who identify themselves as mixed race. Conclusions: The implications of the findings are that the ability to maintain social distancing and personal hygiene habits are crucial in deterring disease transmission and lowering incidence rates, especially in the early stage of disease formation. Relevant authorities should identify preventive factors and take early actions to fight infectious diseases in the future.