Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology

Amer, Ahmed Youssef Ali; Wouters, Femke; Vranken, Julie; Boer, Dianne de Korte-de; Smit-Fun, Valérie; Duflot, Patrick; Beaupain, Marie-Hélène; Vandervoort, Pieter M.; Luca, Stijn; Aerts, Jean‐Marie; Vanrumste, Bart

doi:10.3390/s20226593

Cited by 19 publications

(12 citation statements)

References 35 publications

(45 reference statements)

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“…The outcome shows the accuracy of ML models. Youssef Ali Amer et al (30) have carried our experiment with vital signs of patients using wearable devices. The vital signs considered were BP, oxygen level, and HR.…”

Section: Related Study Literature Surveymentioning

confidence: 99%

Smart Cardiac Framework for an Early Detection of Cardiac Arrest Condition and Risk

Shah

Ahirrao

Pandya

et al. 2021

Front. Public Health

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Cardiovascular disease (CVD) is considered to be one of the most epidemic diseases in the world today. Predicting CVDs, such as cardiac arrest, is a difficult task in the area of healthcare. The healthcare industry has a vast collection of datasets for analysis and prediction purposes. Somehow, the predictions made on these publicly available datasets may be erroneous. To make the prediction accurate, real-time data need to be collected. This study collected real-time data using sensors and stored it on a cloud computing platform, such as Google Firebase. The acquired data is then classified using six machine-learning algorithms: Artificial Neural Network (ANN), Random Forest Classifier (RFC), Gradient Boost Extreme Gradient Boosting (XGBoost) classifier, Support Vector Machine (SVM), Naïve Bayes (NB), and Decision Tree (DT). Furthermore, we have presented two novel gender-based risk classification and age-wise risk classification approach in the undertaken study. The presented approaches have used Kaplan-Meier and Cox regression survival analysis methodologies for risk detection and classification. The presented approaches also assist health experts in identifying the risk probability risk and the 10-year risk score prediction. The proposed system is an economical alternative to the existing system due to its low cost. The outcome obtained shows an enhanced level of performance with an overall accuracy of 98% using DT on our collected dataset for cardiac risk prediction. We also introduced two risk classification models for gender- and age-wise people to detect their survival probability. The outcome of the proposed model shows accurate probability in both classes.

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Section: Related Study Literature Surveymentioning

confidence: 99%

Smart Cardiac Framework for an Early Detection of Cardiac Arrest Condition and Risk

Shah

Ahirrao

Pandya

et al. 2021

Front. Public Health

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“…Machine learning has become a popular and reliable analytical technique in recent years, especially in the medical domain. Many studies investigated hospitalised patients and ICU patients, for monitoring or mortality prediction [1][2][3][4][5][6][7][8]. Some of these studies investigated to what extent vital signs could inform on a patient's clinical deterioration and adverse events [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, 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]. In our previous study [8], we developed predictive models to predict the future values of the same vital signs, up to three hours ahead on average.…”

Section: Introductionmentioning

confidence: 99%

“…Another related approach involves vital sign predictors for hospitalised patients [7,8]. In our previous study [8], we developed predictive models to predict the future values of the same vital signs, up to three hours ahead on average. However, the population of that study comprised patients admitted to general wards (i.e., cardiology, post-surgical, and dialysis patients).…”

Section: Introductionmentioning

confidence: 99%

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Vital Signs Prediction for COVID-19 Patients in ICU

Amer

Wouters

Vranken

et al. 2021

Sensors

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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 (SpO2). 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 12%,5%, and 21.4% for heart rate, oxygen saturation, and respiration rate, respectively. Moreover, increasing the observation rate could enhance the prediction performance to be, on average, 8%,4.8%, and 17.8% 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.

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“…Machine learning has also been applied to drive clinical decision‐making and to provide decision support in a diverse set of clinical settings and specialties including but not limited to neurosurgery, 23 cardiovascular medicine/cardiac surgery, 24,25 endocrinology, 26–28 radiology, 29 and critical care 30–32 . It has also been leveraged for time series prediction of specific patient vital signs 33,34 or even therapeutic setpoints 28,31,35 . Similarly, algorithms and modeling approaches have also been applied to prediction of patient outcomes including mortality, 36,37 length of stay in the hospital 38,39 or intensive care unit, 40,41 readmission, 42–44 and countless others.…”

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confidence: 99%

Machine learning in medicine: It has arrived, let's embrace it

Pappada

2021

Journal of Cardiac Surgery

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Machine learning and artificial intelligence (AI) have arrived in medicine and the healthcare community is experiencing significant growth in their adoption across numerous patient care settings. There are countless applications for machine learning and AI in medicine ranging from patient outcome prediction, to clinical decision support, to predicting future patient therapeutic setpoints. This commentary discusses a recent application leveraging machine learning to predict one‐year patient survival following orthotopic heart transplantation. This modeling approach has significant implications in terms of improving clinical decision‐making, patient counseling, and ultimately organ allocation and has been shown to significantly outperform pre‐existing algorithms. This commentary also discusses how adoption and advancement of this modeling approach in the future can provide increased personalization of patient care. The continued expansion of information systems and growth of electronic patient data sources in health care will continue to pave the way for increased use and adoption of data science in medicine. Personalized medicine has been a long‐standing goal of the healthcare community and with machine learning and AI now being continually incorporated into clinical settings and practice, this technology is well on the pathway to make a considerable impact to greatly improve patient care in the near future.

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Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology

Cited by 19 publications

References 35 publications

Smart Cardiac Framework for an Early Detection of Cardiac Arrest Condition and Risk

Smart Cardiac Framework for an Early Detection of Cardiac Arrest Condition and Risk

Vital Signs Prediction for COVID-19 Patients in ICU

Machine learning in medicine: It has arrived, let's embrace it

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