Analyzing and Predicting Dynamic Fluctuations of Physiological State in Healthcare Workers

crossref(2024)

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摘要
Abstract Background Healthcare workers face continuous exposure to various physiological and psychological stressors, which can lead to dynamic changes in their physiological environment and potentially culminate in debilitating disease. This study was conducted to analyze the dynamic changes in physiological measures of health examination and anticipate health status and disease risk in healthcare workers. Methods A retrospective study extracting health examination data from healthcare workers from 2018 to 2022 was carried out. Principal component analysis (PCA) was employed for unsupervised dimensionality reduction to identify the combinations of measures to best capture the variation in the population. The average path length on the two-dimensional graph of the subjects with 3 ~ 5 health examination records was calculated and analyzed. Based on machine learning, we also developed predictive models to anticipate healthcare workers' dynamic changes in physiological measures. Results The results showed that 4.65% of healthcare workers exhibited unusually high average path length and were identified as outliers in abnormal fluctuation in physiological measures. Additionally, we identified statistically significant differences in the average path length between different genders, departments, and ages. Notably, the average path length was significantly correlated with hemoglobin, platelet count, hematocrit, mean corpuscular volume, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, platelet crit, creatinine, uric acid, and low-density lipoprotein cholesterol. We also developed random forest, support vector machine, and K-nearest neighbors regressions, which showed strong predictive performance for the average path length. Conclusions This study provides novel insight into the assessment of subtle changes in physiological measures and anticipatory analytics of the healthcare workers’ health status. This will provide an important reference value for healthcare workers’ health prediction, promotion, and management.
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