Abstract
This study introduces machine learning predictive models to predict the future values of the monitored vital signs of COVID-19 ICU patients. The main vital sign predictors include heart rate, respiration rate, and oxygen saturation. We investigated the performances of the developed predictive models by considering different approaches. The first predictive model was developed by considering the following vital signs: heart rate, blood pressure (systolic, diastolic and mean arterial, pulse pressure), respiration rate, and oxygen saturation. Similar to the first approach, the second model was developed using the same vital signs, but it was trained and tested based on a leave-one-subject-out approach. The third predictive model was developed by considering three vital signs: heart rate (HR), respiration rate (RR), and oxygen saturation (SpO). The fourth model was a leave-one-subject-out model for the three vital signs. Finally, the fifth predictive model was developed based on the same three vital signs, but with a five-minute observation rate, in contrast with the aforementioned four models, where the observation rate was hourly to bi-hourly. For the five models, the predicted measurements were those of the three upcoming observations (on average, three hours ahead). Based on the obtained results, we observed that by limiting the number of vital sign predictors (i.e., three vital signs), the prediction performance was still acceptable, with the average mean absolute percentage error (MAPE) being , and for heart rate, oxygen saturation, and respiration rate, respectively. Moreover, increasing the observation rate could enhance the prediction performance to be, on average, , and for heart rate, oxygen saturation, and respiration rate, respectively. It is envisioned that such models could be integrated with monitoring systems that could, using a limited number of vital signs, predict the health conditions of COVID-19 ICU patients in real-time.
Highlights
The recent COVID-19 pandemic shocked healthcare systems around the globe, highlighting the need for intelligent monitoring solutions
Prior to applying the localised learning approach of kNN-LS-support vector machines (SVMs), a set of statistical features were extracted from the raw measurements of each vital sign
The extracted features were the minimum, maximum, mean, median, standard deviation, and energy. These features were extracted from a time window of three observations to predict the upcoming vital sign values up to three observations ahead
Summary
The recent COVID-19 pandemic shocked healthcare systems around the globe, highlighting the need for intelligent monitoring solutions. In the study by Mahdavi et al [6], they developed support vector machine models to predict the mortality risk of COVID-19 patients, based on demographic and laboratory variables/features obtained from a patient’s first day of admission. Another related approach involves vital sign predictors for hospitalised patients [7,8]. The acceptable predictions of both studies motivated us to integrate the two concepts, namely extracting simple features from low rate measurements of COVID-19 ICU patients to predict the near future values of the vital signs, up to three hours ahead on average
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