Digital health · 2025
Machine learning-based prediction of in-hospital mortality in patients with chronic respiratory disease exacerbations.
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Ryu Seung Yeob, Lee Seon Min, Kim Young Jae, Kim Kwang Gi
Аннотация
OBJECTIVE: Exacerbation of chronic respiratory diseases leads to poor prognosis and a significant socioeconomic burden. To address this issue, an artificial intelligence model must assess patient prognosis early and classify patients into high- and low-risk groups. This study aimed to develop a model to predict in-hospital mortality in patients with chronic respiratory disease using demographic, clinical, and environmental factors, specifically air pollution exposure levels.
METHODS: This study included 6272 patients diagnosed with chronic respiratory diseases comprising 39 risk factors. Air pollution indicators such as particulate matter (PM10), fine particulate matter (PM2.5), CO, NO2, O3, and SO2 were used based on long-term and short-term exposure levels. Logistic regression, support vector machine, random forest, and extreme gradient boost were used to develop prediction models.
RESULTS: The AUCs for the four models were 0.932, 0.935, 0.933, and 0.944. The key risk factors that significantly influenced predictions included blood urea nitrogen, red blood cell distribution width, respiratory rate, and age, which were positively correlated with mortality prediction. In contrast, albumin, lymphocyte count, diastolic blood pressure, and SpO2 were negatively correlated with mortality prediction.
CONCLUSION: This study developed a prediction model for in-hospital mortality in patients with chronic respiratory disease and demonstrated a relatively high predictive performance. By incorporating environmental factors, such as air pollution exposure levels, the model with the best performance suggested that 365 days of exposure to air pollution was a key risk factor in mortality prediction.
METHODS: This study included 6272 patients diagnosed with chronic respiratory diseases comprising 39 risk factors. Air pollution indicators such as particulate matter (PM10), fine particulate matter (PM2.5), CO, NO2, O3, and SO2 were used based on long-term and short-term exposure levels. Logistic regression, support vector machine, random forest, and extreme gradient boost were used to develop prediction models.
RESULTS: The AUCs for the four models were 0.932, 0.935, 0.933, and 0.944. The key risk factors that significantly influenced predictions included blood urea nitrogen, red blood cell distribution width, respiratory rate, and age, which were positively correlated with mortality prediction. In contrast, albumin, lymphocyte count, diastolic blood pressure, and SpO2 were negatively correlated with mortality prediction.
CONCLUSION: This study developed a prediction model for in-hospital mortality in patients with chronic respiratory disease and demonstrated a relatively high predictive performance. By incorporating environmental factors, such as air pollution exposure levels, the model with the best performance suggested that 365 days of exposure to air pollution was a key risk factor in mortality prediction.